Apprentices

As a versatile Large Language Model (LLM) tool, ChatGPT allows users to create everything from commercial jingles to Python code snippets. ChatGPT can also assist with strategic decision-making, website optimization, and other complex tasks. These functions save significant time and help busy employees get more done with less effort.

This article highlights some of the many uses of ChatGPT in business. We’ll also explore the benefits and potential challenges of adopting this innovative technology.

Core functional areas for ChatGPT use

ChatGPT has over 200 million users worldwide. 92% of Fortune 500 companies use OpenAI tools. Several factors have contributed to ChatGPT’s meteoric rise, including its versatility and user-friendliness.

The chatbot uses natural language processing and machine learning algorithms to interpret user input and deliver relevant, human-like responses. Anyone can easily enter instructions or queries and receive meaningful answers, even if they have a limited understanding of AI technology.

Here are a few ways professionals use ChatGPT to enhance workplace productivity.

ChatGPT for marketing

Marketing was one of the first fields to embrace AI tools. According to Econsultancy’s annual The Future of Marketing report, 32% of marketers use generative AI tools like ChatGPT with another 43% actively exploring the technology.

With strategic prompting, ChatGPT can create virtually any text-based marketing content. You could ask the app to generate an article in your brand voice or write riddles for your social media followers to solve. Marketers also use ChatGPT to brainstorm ideas, such as quirky podcast topics and persuasive calls to action. This capability helps marketers overcome writer’s block and enables marketing teams to think outside the box faster.

The user can optimise ChatGPT responses for SEO by prompting the app to include specific keywords.

In addition, ChatGPT makes it easy to hone content for search engine optimization (SEO) purposes.

A marketer could prompt the AI tool to generate a blog post about fashion trends and include specific keywords to boost its visibility in search engine rankings. The app performs these tasks in seconds, while a human might take hours to complete the same process manually.

Social media management is another popular application of ChatGPT. Marketers can use AI to draft posts and recommend optimal posting times. After they’ve shared the content, ChatGPT can generate responses to comments from followers to enhance engagement.

ChatGPT also enables businesses to create personalised marketing content more quickly. Say, for instance, a marketer is developing an email campaign to promote a new product line. They could input data about their target audience and prompt ChatGPT to create customised email copy that resonates with recipients.

Sales

ChatGPT and other AI technologies are revolutionising the sales sector. Researchers at the University of Alabama at Birmingham predict these tools could complete 60% of sales tasks by 2028.

Some businesses deploy ChatGPT-powered chatbots on their websites to automatically qualify leads. Sales professionals create intelligent prompts for the chatbot to talk to leads and answer questions. For example, Chatfuel uses ChatGPT to engage customers with personalised responses and collect contact information for follow-up.

Businesses can use ChatGPT-powered apps like Chatfuel to connect with customers and capture leads automatically.

ChatGPT can also integrate with lead scoring tools to evaluate potential customers based on their responses. This function helps sales representatives focus on the most promising leads for optimal efficiency.

ChatGPT also streamlines customer communication. Sales representatives can input custom instructions to draft personalised emails, follow-ups, and proposals. This process speeds up content creation and gives professionals more time to strengthen client relationships.

Additionally, ChatGPT is a valuable sales training tool. The application can generate role-play scenarios and complex simulations in seconds. For example, users can have conversations with ChatGPT to practise negotiating contracts or delivering complex sales pitches. These exercises enable sales professionals to sharpen their sales skills and prepare for challenging real-world scenarios.

Data analysis

ChatGPT is a powerful data analysis tool for businesses in all sectors. It can interpret complex text-based data and provide actionable insights. Professionals also use ChatGPT to automatically create reports and presentations to share their findings.

For instance, Business Analysts can integrate ChatGPT with machine learning tools to analyse training data. The AI technology can detect trends in the dataset and explain them in accessible, natural language. It can also offer recommendations to improve the effectiveness of training programs.

Software engineering

ChatGPT and other AI tools have become integral to many software engineering tasks. A Checkmarx survey found 99% of development teams use artificial intelligence to generate code.

ChatGPT can accelerate software development by generating API endpoints in different programming languages.

A Software Engineer can enter a code snippet into the chat window and prompt the tool to complete or optimise it. ChatGPT also detects coding errors and offers debugging solutions. These capabilities help Software Engineers automate repetitive programming tasks and develop applications faster.

Documentation is another popular function of ChatGPT. This tool automates the creation of code documentation and user manuals. Users can input their code or product features to generate accurate and clear descriptions.

Finally, ChatGPT streamlines project management tasks for busy tech professionals. Users can plan detailed schedules and generate progress updates to share with clients and Project Managers.

Benefits of using ChatGPT at work

ChatGPT offers numerous advantages for professionals in all sectors. Here are four reasons to develop basic AI skills and integrate this innovative technology into your workflow.

Improved productivity

ChatGPT reduces time spent on repetitive tasks, such as writing emails and creating reports. ChatGPT Plus also offers voice mode, allowing users to multitask and input prompts on the go. This increased efficiency gives you more time to focus on strategic work that requires human creativity and intuition.

By improving productivity, ChatGPT can help companies cut costs and increase profitability. Workday predicts that the use of AI tools to improve worker efficiency could add £119 billion to the UK economy.

Enhanced creativity

Do you ever find yourself staring at a blank computer screen as you search for the right words? You’re not alone. Even the most talented professionals can struggle to find inspiration or write the perfect content, especially on tight deadlines.

ChatGPT helps users overcome creative blocks by generating ideas and suggesting new approaches. For instance, you could prompt the app to generate 20 ideas for funny TikTok videos or recommend innovative strategies to engage rewards program members. These suggestions can kickstart the brainstorming process and help you solve problems.

Automation of routine tasks

Many employees use ChatGPT to automate time-consuming tasks, such as:

• Composing product descriptions
• Creating memos
• Drafting follow-up emails
• Outlining blog posts
• Proofreading content
• Responding to customer reviews
• Summarising meeting notes
• Translating content

This automation lets you cross items off your to-do list in seconds and prioritise more impactful tasks. It may also reduce burnout caused by boring and repetitive work.

Improved communication

ChatGPT enables users to produce clear and concise messages for clients and team members. For example, you might ask the app to explain a contract’s fine print to a customer to clarify the terms and conditions. Similarly, you can prompt ChatGPT to write a timeline for completing a project to keep everyone on the same page.

ChatGPT can generate detailed timelines for developing an employee handbook and other projects.

This enhanced communication saves time by decreasing the risk of errors and misunderstandings. It can also reduce the time you spend answering follow-up questions and providing clarifications.

Common AI challenges (and how to overcome them)

While AI in business offers many opportunities, it also poses potential challenges in the workplace. Here are three common issues and strategies to overcome them.

Accuracy and reliability

Accuracy is one of the main concerns for ChatGPT users. The ChatGPT app is an LLM pulling from a vast dataset to interpret and respond to queries. It could generate incorrect results if it relies on unreliable or outdated information. ChatGPT may also misinterpret some prompts, leading to irrelevant or nonsensical responses — an outcome known as hallucination.

This viral conversation demonstrates how ChatGPT’s responses can contain misleading information about simple topics.

For example, TikTok user @yallwatchthis shared a lengthy conversation where ChatGPT told them that “strawberry” has two “r”s instead of three. While this mistake is humorous, other errors could have serious consequences. For instance, the Association of Health Care Journalists warns members that ChatGPT may fabricate data and provide false medical information, potentially endangering patients.

How can you avoid this issue? By thoroughly fact-checking AI-generated content.

Paid users can ask ChatGPT to provide links to sources. Review each source carefully to make sure the AI cites recent and trustworthy information.

By contrast, the free version can’t provide direct sources or hyperlinks. If you use the unpaid app, you should consult reliable outside sources to cross-check its information. For instance, you can use Google to search for the original sources of quotes and statistics to verify their authenticity and relevance.

Dependence on AI

ChatGPT is convenient and fast, making it tempting to rely on it for many tasks. But overdependence on AI can stifle creativity and problem-solving skills.

A marketer may notice their AI-generated blog posts have a robotic tone and lack personal touches. Similarly, a Software Developer might struggle to write complex code after relying on ChatGPT to generate it.

Maintain a balance between AI and human intelligence by using ChatGPT to supplement your skills instead of replacing them. Use this tool for preliminary tasks like drafting emails and generating article titles. But you should rely on your expertise and personal judgement for complex tasks, such as writing in-depth case studies and solving interpersonal conflicts. This approach will allow you to produce more nuanced content and strengthen your soft skills.

Data privacy concerns

You may handle sensitive or proprietary information in your daily work. Follow these guidelines to protect this information when using ChatGPT in professional settings:

• Never enter confidential or personal information into the app.
• Navigate to the ChaGPT menu at the top of the chat window and turn on the temporary chat. This feature erases your past conversations from the chat history, improving privacy. It also prevents OpenAI Developers from analysing and using your information to improve the model.
• Follow your organisation’s privacy guidelines and ask an IT Specialist for guidance on data handling best practices.

Examples of popular ChatGPT tools and integrations

Software Developers have used ChatGPT to create many cutting-edge tools and integrations. Consider adding these tools to your workflows:

• Zapier integrations: Zapier integrates ChatGPT with various apps to automate workflows. For instance, you can integrate ChatGPT with Google Sheets to automatically input information into a spreadsheet and generate reports.
• Slack: This team communication platform integrates ChatGPT into its Slackbot. This AI assistant provides instant help, schedules meetings, sends notifications, and more.
• ChatGPT for Windows: This Google Chrome extension enhances searches by generating ChatAPT responses alongside Google results. It also lets users complete quick tasks and conduct research without navigating to the ChatGPT app.

Get ahead with cutting-edge artificial intelligence tools

There’s always room to improve your productivity and simplify your professional life. ChatGPT and other AI-powered technology can empower you to conquer your to-do list more efficiently and eliminate repetitive, energy-draining tasks.

Interested in learning how to elevate your productivity with AI tools? Multiverse’s AI for Business Value apprenticeship could be right for you.

This detailed program teaches you how to leverage AI tools to make an impact in your organisation. You'll study machine learning fundamentals, AI ethics, business analysis, and other critical concepts, giving you the hands-on experience you need to leverage AI to drive organisational change.

Additionally, all Multiverse apprentices get access to our exclusive AI Jumpstart module. This asynchronous training course teaches advanced AI and critical thinking skills to help you navigate complex AI applications.

Most importantly, our upskilling program lets you gain expertise and skills while working for your current employer. Complete our quick application to see if you’re eligible.

According to McKinsey’s Global Survey on AI, 72% of organisations worldwide have integrated this technology into at least one business function. This trend is particularly evident in Europe, where 90% of professionals report using generative AI tools for work or personal reasons.

The growing adoption of AI in business has significantly increased the demand for professionals with relevant expertise. Microsoft’s 2024 Work Trend Index Annual Report found that 66% of business leaders are unwilling to hire candidates who lack AI skills. Additionally, 71% of respondents said they would prefer to hire a less experienced candidate with AI expertise over a more experienced candidate without these skills.

Upskilling can help you exploit this trend and pursue new career opportunities. This guide explores common uses of AI in business processes and the benefits of adopting this versatile technology. We’ll also spotlight popular AI platforms and resources for developing your skills.

Core business use cases for AI

Numerous organisations have already integrated artificial intelligence into their business functions, and many others are in the process of doing so. A 2024 Slack survey revealed that 96% of executives feel a pressing need to adopt AI in business operations, and 35% aim to accomplish this goal within the next 18 months.

Here are a few innovative ways companies leverage AI to improve business functions and gain a competitive edge.

Customer service and experience

When you contact a company’s customer service, you may interact with AI technology without even realising it. A Forbes Advisor survey found that 56% of businesses use AI to improve the customer experience and deliver timely service.

Many companies rely on AI-powered chatbots and virtual assistants to answer customer inquiries and offer personalised recommendations. These tools provide instant responses, eliminating the need for customers to wait on hold for a human representative.

For example, Sephora France has implemented an AI-powered chatbot for Facebook and Messenger. This chatbot uses customer data to answer questions about frequent shopper points, order status, and other account-related matters. This tool also serves as a virtual beauty coach by providing personalised tips and tutorial videos.

Data analysis and decision-making

Companies in all industries use AI applications for data collection and analysis. These tools allow users to collect large amounts of data from multiple sources and process it efficiently. Additionally, AI can identify patterns and trends that Data Analysts may overlook, allowing businesses to gain fresh insights.

The US-based insurance company State Farm uses AI to streamline claims processing and other time-consuming tasks. An AI platform digitises claims documents, extracts data, and analyses the information for accuracy. This tool allows the insurer to process claims more efficiently and make data-driven decisions about claims approvals.

Process automation

Many organisations are harnessing AI applications to automate repetitive tasks that don’t require human intervention, such as:

• Filtering emails
• Generating reports
• Inspecting products for quality
• Processing invoices
• Sending follow-up emails
• Tracking inventory
• Transcribing meeting notes

AI automation improves efficiency and reduces the risk of error. It also allows employees to focus on more creative or critical tasks requiring human input. These benefits can help companies save money and scale their operations quickly.

The Forbes Advisor survey found that 51% of businesses have already adopted AI for this purpose. For instance, Walmart has developed innovative AI-powered logistics technology to optimise driving routes. The software automatically maps the most efficient routes based on traffic patterns, weather, store delivery windows, and other data. This technology reduced the company’s CO² emissions by 94 million pounds.

Marketing and sales

Generative AI has taken the marketing and sales world by storm. According to the McKinsey Global Survey on AI, 34% of companies now use the technology in these fields, a percentage that has doubled since 2023.

Sales teams have integrated AI applications into many business processes. This technology allows them to identify leads, score prospects, and personalise outreach. Sales Representatives can also use AI-powered predictive analytics to forecast customer behaviour and market trends. These actionable insights enable them to optimise pricing and tailor their sales strategies.

Artificial intelligence helps Marketers analyse data and develop tailored marketing strategies. For instance, AI systems can analyse customer reviews and purchases to extract meaningful insights about consumer preferences. Marketers use these findings to create personalised marketing materials that engage customers more effectively.

Additionally, many marketing teams use AI tools for content generation. Large language models (LLMs) like GPT-4 allow users to generate persuasive blog posts, emails, social media captions, and other marketing materials. Marketers also rely on image-generation tools like Midjourney and DALL-E to create compelling advertisements.

In 2023, Coca-Cola made headlines for “Create Real Magic,” an innovative AI marketing campaign. The company partnered with OpenAI to launch an AI platform that combined the capabilities of DALLE-E with GPT-4. Coca-Cola invited fans to create AI artwork with branded elements using the platform. This campaign drew on user-generated content to boost engagement and generate hype about Coca-Cola products.

Human resources

AI systems are reshaping how companies hire and manage their human workforce. A 2024 SHRM study found that approximately 25% of businesses use artificial intelligence for human resources (HR) tasks. Large organisations with 5,000 or more employees are the most likely to adopt AI for this purpose.

According to SHRM, 64% of companies implementing AI for human resources use this technology to automate recruitment processes. AI-powered applicant tracking systems (ATS) analyse resumes and rank candidates based on their qualifications. This software also uses predictive analytics to forecast a candidate’s performance. These functions accelerate recruitment by helping HR Representatives quickly identify top talent.

Additionally, 49% of surveyed businesses use AI to support learning and professional development activities. For instance, AI tools can generate training materials for human workers and track their development. This technology also helps managers deliver actionable feedback by assessing performance data and identifying employees’ strengths and weaknesses.

Finally, AI implementation can help companies diversify their workforce. One anonymous global investing firm used the AI platform pymetrics to assess job candidates and recommend suitable applicants. After implementing the tool, the firm increased the number of women hired by 62%. This drastic improvement in gender diversity demonstrates how AI can promote more inclusive hiring practices.

Risk management

The AI revolution has transformed how organisations approach risk assessment and management. Machine learning algorithms allow businesses to analyse large volumes of data and identify risks more accurately than previously possible. For instance, financial institutions can use AI-powered fraud detection systems to detect suspicious activity and block potentially fraudulent transactions.

Artificial intelligence also enables companies to track data in real time and identify emerging threats quickly. For example, Innocens BV partnered with IBM to create an edge computing solution that analyses data from medical sensors. This technology helps healthcare providers identify infants at risk of developing sepsis faster, leading to better patient outcomes.

Benefits driving AI-adoption in business workflows

Companies that integrate AI into their business operations can experience many benefits, including:

• Improved customer experiences - AI enables companies to deliver more personalised and timely customer experiences. Businesses can use this technology to develop tailored marketing campaigns, provide around-the-clock customer support, and anticipate client needs. These applications increase customer loyalty and satisfaction, driving long-term business growth.
• Increased efficiency and productivity - Artificial intelligence can significantly boost productivity by automating repetitive tasks and streamlining workflows. A 2023 study found that professionals who used AI completed tasks 25% more quickly than workers who didn’t use it. Additionally, the AI users had 40% higher quality results than the control group. This case study suggests that AI implementation can improve performance and promote organisational success.
• Reduced costs - A Forbes article predicts that artificial intelligence will reduce business costs by 37% in 2024. This technology can lower operational expenses by boosting efficiency and improving resource allocation. Additionally, AI helps companies identify and address costly inefficiencies and waste.

While business AI tools provide many benefits, they still need human intelligence to safeguard against potential drawbacks.

AI hallucinations are one of the most significant concerns. This phenomenon occurs when AI models generate false or misleading results. For instance, a model could provide incorrect medical advice or recommend a trucking route that leads drivers into unsafe conditions. Businesses must use human oversight to catch these errors and verify the accuracy of AI-generated recommendations.

Skills gaps are another persistent challenge many companies face — especially small businesses with limited resources. According to a 2024 Opinium survey, 31% of U.K. professionals haven’t been trained to use AI in their roles, and 23% lack confidence in using this technology. Employees who lack AI skills may not use these tools efficiently, decreasing productivity and increasing the risk of errors.

What are some of the most common AI tools in use today?

Business professionals use many AI tools to improve workflows and reduce manual tasks. Here are three popular applications.

GitHub Copilot

According to Stack Overflow’s 2024 Developer Survey, 82% of Software Developers use AI applications to write code. GitHub Copilot is one of the most popular tools for this purpose. This application uses natural language processing to analyse and respond to user input.

Developers can enter partially completed code into the chat, and the platform will generate recommendations to complete it. GitHub Copilot also detects bugs and proposes solutions, increasing accuracy.

Midjourney

Midjourney is a generative AI platform that converts text into vivid images and videos. Marketers can use this tool to create a wide variety of visual marketing content. For instance, marketers can ask Midjourney to generate images for articles, product catalogues, and social media posts. Users simply input a written description of the images they’re envisioning, their desired style, and other instructions.

This platform saves companies significant time and money by allowing marketing teams to create high-quality content in seconds instead of hiring external creators or producing it in-house.

HubSpot

HubSpot offers a suite of AI-powered tools to support various business functions. Popular features include:

• Caption generator for social media posts
• Chatbot builder to streamline scheduling
• Content remixer
• Copywriting generator for blog posts, emails, meta descriptions, and other content
• Search engine optimization recommendations
• Webpage builder

Marketing and sales professionals can use these tools to generate content and engage customers more efficiently. The platform also uses AI to analyse data and generate reports so businesses can track their marketing campaigns’ performance.

AI for business: Where do we go from here?

Business AI isn’t going away anytime soon. The International Trade Administration predicts that the value of the U.K. artificial intelligence market will increase from $21 billion in 2024 to over $1 trillion in 2035. Only the United States and China will outpace this rapid growth.

The need for skilled AI professionals will increase as AI capabilities continue to develop. According to PwC, postings for jobs requiring AI skills like machine learning have increased 3.6 times faster than postings for all roles. Sectors that have already embraced this technology have increased their productivity by nearly fivefold.

Additionally, 64% of U.K. CEOs believe that most of their workforce will need to gain new AI skills in the next three years to keep up with rapid technological advancements. Professionals who gain these skills now will be better positioned to take advantage of emerging trends and advance their careers.

Drive innovation and get ahead at work with AI skills

Artificial intelligence has already begun to reshape the business landscape in the U.K. and beyond. Companies can integrate AI into numerous business functions, including customer service, inventory management, and risk management.

Many AI platforms are free or have affordable subscriptions, allowing businesses of all sizes to take advantage of this technology. Upskillers can also use these applications to begin developing their AI skills. For instance, you can purchase a basic Midjourney subscription to hone your prompt engineering skills.

A Multiverse apprenticeship can also help you gain in-demand technical skills. Our AI upskilling programmes allows you to develop new skills and gain hands-on experience without leaving your current role.

This program is completely free, so you’ll accelerate your professional development and future-proof your career without paying tuition. All Multiverse apprentices also get access to our exclusive AI Jumpstart module, which teaches AI fundamentals and problem solving skills.

Complete our quick application today to start expanding your business AI skills.

This step-by-step guide covers everything you need to know about creating an effective career path plan. Follow our steps and top tips to set yourself up for professional success.

What is a career path plan?

A career path plan is a roadmap for your professional development. It describes your major career aspirations and breaks them down into short-term goals and milestones. You can also use this framework to identify skill gaps and create a strategy to fill them.

A career development plan is a living document that evolves as your circumstances, goals, and interests change. For example, you might originally aspire to become a Senior Product Manager. But this plan could change if you develop a new passion for artificial intelligence (AI) or ethical hacking.

The career planning process offers many benefits. This strategic approach empowers you to navigate your career proactively instead of going with the flow. Taking control will help you avoid getting stuck in your current role or passively following your employer’s goals.

A career plan also allows you to align your goals with actionable steps. Lofty goals can sometimes feel unreachable, especially if you’re at the beginning of your career or transitioning to a new industry. Breaking these ambitions into smaller milestones will help you stay motivated and track your progress.

How to craft a career path plan: a strategic approach

It’s never too early or too late to create a career plan. No matter where you’re at in your professional journey, these steps will help you develop a structured roadmap.

Assess your skills

In many ways, charting your career is like planning a road trip. You need to identify your starting point to map out the most effective route to your destination. Otherwise, you might wander off course or waste time relearning skills.

To get started, you should develop a clear understanding of your current location by conducting a skills assessment. List the interpersonal and technical skills you’ve developed in your current and previous positions. You can also include abilities you’ve gained through education, volunteer work, and personal projects.

As you compile your list, rate your abilities as “basic,” “proficient,” and “advanced.” This step will help you identify your strengths and areas to improve.

Here’s an example of a skills assessment for an entry-level Data Analyst:

• SQL: Advanced
• Python: Advanced
• Written communication: Proficient
• Data cleansing and processing: Proficient
• Data visualisation: Proficient
• R: Basic
• Public speaking: Basic

Update this self-assessment regularly as you expand and hone your skill set. You can use this list to quickly write resumes and cover letters when applying for new jobs.

Explore potential career paths

New career opportunities constantly emerge as industries and technologies evolve. Career exploration allows you to investigate new paths that may fit your goals and interests.

Start by following the latest trends in your industry. You can stay updated by joining professional associations, subscribing to newsletters, and participating in online communities. These steps will keep you informed about emerging careers and cutting-edge technologies.

Suppose you’re a Junior Software Developer. You notice that your online tech communities are buzzing about a new framework called edge computing. You could become an early adopter of this technology and use your skills to become an Edge Solutions Architect.

Informational interviews also allow you to explore your career options. Network with people who have interesting jobs and ask if they’d be willing to share career information and insights. This process gives you an inside look at various jobs and may help you discover new paths you’ve never considered.

Look for case studies and models

Observing role models can motivate you and provide you with a blueprint for success. Look for professionals who have already achieved your ultimate career goals. You can use many strategies to find these case studies, such as:

• Identify people you admire in your company
• Search LinkedIn for professionals with your dream job title
• Follow industry influencers and thought leaders on social media
• Participate in mentorship programs
• Read or listen to biographies, interviews, and podcasts about industry rock stars

Study these role models to learn about their career progression, skills, and accomplishments. For example, you can visit their professional websites to view their CVs and portfolios. You can also look at their previous job titles and employers on LinkedIn.

This process will help you understand your role models’ professional journeys and identify essential skills. You can use this information to shape your career plan and imitate their success.

For instance, you may discover that many Lead UX Designers have a Google UX design certification. Earning this certification would help prepare you for this advanced role.

Set SMART goals

Once you have a sense of your broad ambitions, it's time to set your career goals. The SMART framework is one of the most effective ways to establish short- and long-term objectives.

Let’s break down this acronym and its meaning:

• Specific - Use action words to describe what you plan to achieve.
• Measurable - You can quantify or observe your progress.
• Achievable - You realistically expect to accomplish your goal given your current abilities and resources.
• Relevant - The goal fits your overall career plan.
• Time-bound - The goal has milestones and a target deadline.

Let's say you're an IT Support Specialist who wants to reskill and become a UX Designer. Here are a few examples of SMART objectives you could establish to help you achieve this overall goal:

• Complete two online courses on fundamental UX design principles in the next three months.
• Spend 30 minutes daily reading about UX design best practices and trends.
• Develop wireframes for a chess game mobile app, e-commerce website, and other applications by the end of the year.
• Create a prototype for a to-do list mobile app in December.
• Design a digital portfolio to showcase your UX design skills in the next 12 months.

Creating SMART goals allows you to identify tangible action items and keep yourself accountable. They also help you plan your schedule and focus on your long term goals.

Prioritise skills and interests

You can’t learn everything at once, especially if you have other responsibilities. Decide which skills and interests you want to prioritise in your career plan.

Start by analysing job advertisements to identify must-have competencies. Read several dozen job descriptions for the types of roles you plan to pursue. Note the required certifications, skills, and tools for each position. Focus on gaining qualifications that appear in multiple postings to increase your competitiveness for these roles.

Researching job market trends can also help you prioritise skills. Follow industry news to learn about in-demand technologies and tools. Gaining relevant knowledge and skills will allow you to position yourself as an innovator and stay competitive.

Identify concrete steps and take action

Use your SMART goals to create an action plan. Break each goal down into several steps you can accomplish in a few weeks or months.

Let’s say you want to learn Swift in the next six months to create iOS applications. This goal may seem daunting, but you can divide it into these concrete steps:

• Get concrete professional experience via an internship or apprenticeship programme
• Use the Swift Playgrounds app to practise your coding skills daily.
• Complete an online course to learn how to build a social media app with Swift.
• Visit the r/iOSProgramming subreddit daily to learn about the experiences and challenges of Swift Developers.

Finally, use these goals to craft your action plan. Set aside at least 30 minutes daily to work on one or more goals. You should also assess your progress regularly and adjust your schedule as necessary. This structured approach will allow you to make consistent progress and avoid burning out.

Tips for setting concrete career goals

Establishing clear goals is one of the most difficult parts of career path mapping. These additional tips will help you develop and achieve your goals:

• Conduct an honest self-assessment to identify your interests, skills, and values.
• Don’t be afraid to research career paths that catch your interest, even if they seem unrelated to your current role. Many people successfully transition to new careers.
• Schedule a performance development planning session with your manager or mentor. They can give advice about your career plans and suggest developmental opportunities.
• Prioritise your career goals based on your personal and professional aspirations, not your company’s objectives.
• Regularly review and adjust your goals as your circumstances change and market conditions evolve.
• Celebrate your milestones to stay motivated.

How can you accelerate your career path?

There’s no shortcut or magical formula for professional development. Successful career planning requires consistent effort and time. But these strategies can help you advance your career growth faster.

Upskill

Upskilling is the process of learning new skills or levelling up existing ones. This approach helps you expand your knowledge and stay competitive in the changing job market.

A Multiverse programme is one of the most effective ways to upskill. Learners can pick from programmes in data analytics, AI, and other in-demand fields. They develop theoretical knowledge and gain hands-on experience working for leading companies.

Other upskilling strategies include:

• Earn advanced certifications
• Learn cutting-edge technologies
• Volunteer for more challenging responsibilities or leadership roles at your current company
• Offer your services as a freelancer or consultant
• Contribute to open-source projects to expand your technical abilities

Grow your professional network

Networking enables you to explore new career options and find mentors. It also helps you learn about open positions that may not be advertised on popular job boards.

Grow your network by becoming an active member of professional organisations in your industry. These associations often organise conferences, webinars, social gatherings, and mentorship programs. These initiatives allow you to meet people from different career stages and enrich your industry knowledge.

One example of such a group is the Multiverse Community for alumni apprentices. This group offers access to networking events, exclusive content, and career development and mentoring opportunities.

The Internet is another powerful networking tool. You can connect with professionals on LinkedIn, Instagram, and other social media platforms. Engage with their content and share resources and insights. Over time, you’ll build your reputation and strengthen your relationships.

Build a personal brand

Personal branding is the deliberate development of your public identity. Professionals often build a personal brand to establish themselves as industry leaders and increase their visibility.

These strategies will help you cultivate your personal brand:

• Create a professional website highlighting your unique abilities, achievements, and values.
• Use images, language, and logos that reflect your personality.
• Demonstrate your expertise by developing and sharing meaningful content. For example, you can write blog posts or produce videos about industry trends.
• Share personal anecdotes and lessons you’ve learned to engage your audience.

Apply learnings to take on additional strategies

Career planning is an ongoing process of exploration, learning, and reflection. Use the insights you gain to refine your career goals and action plan continually.

For example, upskilling could allow you to pursue different positions at your company. As you transition into new roles, you’ll gain additional competencies and collaborate with different colleagues. These experiences could motivate you to launch a business and pursue new goals.

Grow your career with today’s in-demand skills

It can be hard to know what direction your career path will take you. That’s why making a dedicated career plan is essential for all employees.

This framework allows you to clarify your career aspirations and set meaningful goals. It can also help you identify new opportunities for growth. For instance, you could explore new career paths related to your talents and personal interests.

Career planning can also help you prevent burnout. You’ll feel excited and motivated as you complete smaller tasks on your action plan and count the milestones you’ve reached. This process also promotes work-life balance by allowing you to map out a reasonable timeline for improvement.

Applying for an upskilling programme can accelerate your career development and help you expand your professional network. Complete Multiverse’s simple application and our team will get in touch to help you find opportunities that align with your goals.

According to the 2024 Stack Overflow Developer Survey, 65% of professional developers worldwide used Python for development work in the previous year — making it the third most popular programming language overall, behind JavaScript and SQL.

Professionals at companies of all sizes use Python to manage data and create machine learning algorithms, among other data-oriented efforts. Learning Python can open the door to an in-demand, lucrative career as a Software Engineer or Data Analyst — or even help you streamline and broaden the depth of the work you do in your current career.

Do you want to learn Python? Then this article is for you. Below, we’ll be answering some of the top questions for beginning programmers at any stage of their professional careers, including:

• Is Python hard to learn?
• How long does it take to learn Python?
• Is Python worth learning?
• What can you build with Python?

Let’s dive in.

Is it worth learning Python in 2024?

Dutch programmer Guido van Rossum created Python in 1991. It’s still one of the most widely used programming languages today.

Many developers and tech companies prefer Python because its syntax is easy for humans to read. And they can use it to build scalable applications.

Here are four top reasons for learning Python in 2024.

1. High-demand

Professionals at companies of all industries and sizes — including Netflix, Amazon, and Reddit — use Python. So, it’s in high demand.

Moreover, Python is the most popular programming language among people learning to code. Approximately 62% of developers across all skill levels use Python.

An August 2024 LinkedIn search for jobs specifying Python skills in the UK returned 90,835 results overall.

How many is that? For example, you can compare that figure to the number of results in a search for jobs asking for C# (the ninth most popular coding language in the Stack Overflow survey) skills, which returned less than 10,000.

The bottom line is that among technical competencies, Python is a highly sought after skill.

2. Flexibility

Python’s versatility makes it one of the best programming languages to learn for aspiring tech professionals.

Learning Python doesn’t mean you need to pursue a job as a Python Developer. As for job prospects, Python skills are easily transferable, so you could work in data science, software engineering, marketing, or even artificial intelligence.

3. Lucrative job options

Not only are there many careers that need Python skills, but they also pay well. Mastering Python could help you land a job with a high-paying salary or even advance in your current company to greater responsibilities.

Some Python-adjacent titles in the UK net high median salaries. For example:

• Python Developer: £73,000 (source: devITjobs)
• Machine Learning Engineer: £60,000 (devITjobs)
• Data Engineer: £70,000 (itjobswatch)
• Full Stack Developer: £60,000 (devITjobs)
• Back-End Developer: £67,500 (devITjobs)

4. AI applications

ChatGPT, MidJourney, and other AI applications are booming. The size of the artificial intelligence market is projected to reach a staggering £145 billion in 2024, growing to a market size of close to £600 billion by 2030. To support that growth, companies will need to hire more Python Engineers.

Python powers many machine learning and artificial intelligence technologies. As adoption of AI tools grows among the general population, it stands to reason there will be opportunities at the intersection of Python and artificial intelligence for professionals in an array of fields.

Examples of Python in action

Companies typically use Python to develop back-end services. These services refer to server-side or behind-the-scenes functions, like retrieving information from databases and authenticating login information.

Businesses can also use Python to perform data analytics and learn more about customers.

Here are a few real-life examples of how companies are using Python:

• Netflix: The streaming service uses Python to create machine learning algorithms that personalise the user experience. Specifically, it creates personalised recommendations for shows or movies users should watch. Also, Netflix uses Python to manage its content delivery network, which stores content on servers around the world for streaming.
• Spotify: Spotify gathers and analyses data about users’ music preferences using Python code. Based on this information, it generates personalised recommendations for artists, songs, and people to follow.
• Amazon: The biggest online retailer in the world uses Python ML engine to analyse shopping and search habits to recommend relevant products and serve ads.

How long does it take to learn Python for beginners?

Python is one of the easiest languages to learn. It uses a simple and intuitive syntax — the way you arrange code — that resembles the English language. With regular practice every week, most novice coders can learn Python basics in three to six months.

If you’re new to coding and don’t know other programming languages, becoming proficient in Python will take longer. Mastering it will take years of practice and experience. While this might seem like a long time, Python is still faster to learn than more complex languages like Java or C++.

Is Python hard to learn?

It can seem intimidating to beginners. But is Python hard to learn?

Some languages are more difficult to learn than others. And everyone starts their coding journey in different places. But there are some tips that can make learning Python a little easier.

For starters, you can build your foundational coding knowledge before diving in. Many people find it helpful to start learning simple languages like HTML and CSS, which developers use to structure and style web pages.

How to learn Python

You don’t need a university computer science qualification to become proficient in Python.

Learning Python is similar to learning any other coding language. Start with the fundamentals like the syntax. Then, move on to practising real-life coding projects.

If you’re looking to uplevel your career with Python skills, these tips can make the Python learning process faster and smoother.

1. Build a foundation

Jumping straight into reading and writing complex Python code would be like picking up a novel in a foreign language and immediately understanding it. It’s not likely to happen, and you might feel overwhelmed and give up.

Instead, study the basic syntax — structures and rules — before you start writing code.

Many free online tutorials and videos explain how Python syntax uses structures like parentheses and quotes. This will help you make sense of what code means and how you can write your own.

Once you get the hang of Python syntax, you can expand your knowledge by studying built-in functions, or pieces of reusable code, that perform specific tasks.

2. Practice problem-solving

Many developers are strong problem solvers. Building your problem-solving skills will help you adapt to unique situations that the typical course or tutorial doesn’t cover.

For example, think about the common problems you may encounter at work and how you can use Python to solve them. You may not be able to anticipate every problem you might encounter, but you can build your problem-solving skills.

There are thousands of free practice problems online. For example, websites like Edabit and w3resource offer challenges with varying difficulty levels and provide thorough explanations of solutions.

3. Think about what you want to accomplish

Understanding syntax is the first step to learning Python. But it can be easy to get too caught up in trying to format your code perfectly.

As you work through Python coding problems, you may find it helpful to hand write an outline of what you want each line of code to do without worrying about syntax. This technique is called writing pseudocode, and even experienced Python Developers use it to plan out their programs.

Now, with AI tools, you can even ask an LMM to review your code for you. But you should always double check this work for accuracy.

4. Practice coding in Python consistently

Learning Python requires consistency.

Set aside time daily to practise, and dedicate at least a few days a week to learning Python.

It may be helpful to block off small amounts of time in your schedule. There are many free online Python courses and tools that you can use to practice practise, including Practice Python and HackerRank.

5. Join Python communities

Because Python is one of the most popular coding languages, many programmers have formed in-person and virtual communities dedicated to it. Joining a group can motivate you to keep learning and help you figure out solutions to challenging problems.

Here’s a few examples of helpful Python groups:

• PyLadies
• PySlackers
• r/Python and r/learnpython subreddits
• Real Python

Also, Pycon is an annual conference that brings together coding enthusiasts to discuss Python and its many applications.

6. Gain hands-on coding experience with an apprenticeship

An apprenticeship is an excellent way to learn Python and other programming languages.

Multiverse offers a variety of apprenticeship programmess that can help you upskill or reskill without the need to leave your current role. Some of these, such as the Advanced Data Fellowship, can help you develop your Python coding skills by completing real projects under the mentorship of coding experts.

In the Advanced Data Fellowship, apprentices learn to build intuitive dashboards with business intelligence tools, manipulate data using Python, and apply machine-learning algorithms for deeper insights. They’ll also develop skills in system security, technical requirements gathering, and systems development management.

Ideal for professionals aiming to elevate their organisation’s data-driven strategies and product enhancements, this apprenticeship leads to a Level 4 Data Analyst certification upon completion.

Want to learn more about Multiverse’s Advanced Data Fellowship and other opportunities to advance your career? Fill out our quick application to determine if you’re eligible.

Prompt engineering involves giving precise and detailed instructions to generative AI tools to produce high-quality outputs. For example, you can use prompt engineering to help an AI tool generate a complex snippet of code or a detailed image. This process helps you get the desired output instead of vague or incorrect results.

Professionals in many industries use prompt engineering to obtain the best results from AI tools. But is prompt engineering a viable career path? This guide explores prompt engineering techniques, current career opportunities, and salary expectations.

Why does prompt engineering matter in the context of generative AI?

Why is prompt engineering important?

Let’s cover some basics. Generative AI models use natural language processing to interpret prompts – or inputs – from users. They then draw on vast databases to produce relevant outputs. But AI models don’t always generate the desired output, especially when asked to perform complex tasks.

Say you prompt ChatGPT to create a lesson plan for a college class on Indigenous novels. A poorly written prompt could cause the AI system to generate content that addresses younger students or uses culturally insensitive stereotypes.

Prompt engineering allows you to craft effective prompts that produce more accurate responses. It also reduces the amount of time you spend fact-checking and revising the outputs generated by AI systems.

What is prompt engineering used for? This discipline has applications in a broad range of professional and creative contexts. Here are a few use cases:

• Audio production - Video Game Developers and Video Editors can prompt generative artificial intelligence tools to create narration and sound effects.
• Code generation - Software Developers use prompt engineering to write code more efficiently. For example, a developer can input specific requirements into GitHub Copilot and receive relevant code snippets.
• Content creation - Prompt engineering allows professionals to generate tailored articles, emails, and other content. For instance, a marketer could ask ChatGPT to write an informative blog post with specific keywords.
• Data analysis - Data Analysts prompt large language models (LLMs) to interpret datasets and generate data visualisations. Generative AI services can also suggest new ways to analyse datasets, leading to fresh insights.
• Image generation - Prompt Engineers can ask text-to-image models to create custom images based on written descriptions. Companies use these generative AI services to create infographics, logos, and other marketing materials.

Examples of prompt engineering with specific tools

By way of example, here are two types of AI-generated outputs — one text and one visual — made by feeding prompts into popular generative AI models.

Prompt engineering with ChatGPT

ChatGPT is an AI chatbot powered by a large language model. It has many applications, from answering questions to writing resumes.

Suppose you want to use ChatGPT to write a jingle for your organic soap business. Here’s the output ChatGPT generates if you input this generic prompt: “Write a commercial jingle for my soap company.”

This output addresses the prompt but lacks humour and emotional appeal. It also doesn’t target a specific audience or reference identifiable products.

Crafting effective prompts lets you generate a more precise and tailored jingle. For example, you could break down the process of creating the jingle into intermediate steps, such as:

• Create a funny and upbeat jingle for Rachel’s Herbal Suds. Focus on the brand’s use of natural ingredients and commitment to sustainability.
• Address the jingle to eco-conscious women in their early twenties who care about gentle skincare and health.
• Include at least two soap puns.
• Limit the jingle to 15 lines or less.

Here’s the final answer when you input this prompt:

This output references the specific brand and includes phrases designed to appeal to the target audience, such as “Join the sudsy revolution.” Subsequent prompts could use different keywords to change the output or ask the AI to address the audience more subtly than "eco gals in your twenties."

Prompt engineering with Magic Media

Magic Media is a free text-to-image model that uses artificial intelligence to translate natural language into visual art.

Say you want to create an image of a cute dog with soap for your company’s marketing materials. Let’s start with a basic prompt: “Create an image of a dog with soap.” Magic Media generates this image:

This image satisfies the prompt but may not fit your desired aesthetic. It also doesn’t make the soap look appealing, so it’s not useful as a marketing image.

A Prompt Engineer can create a more detailed prompt, such as: “Create a marketing image of a cute poodle with soap. Make the soap look lavish and sudsy.”

In response, Magic Media generates these images:

These images depict adorable dogs with a variety of soap products. You could further engineer the prompt to change the background colour, type of soap, and art style.

How to get the most out of prompt engineering: Best practices

Because AI tools rely on natural language inputs, you don’t necessarily need a computer science degree to become a successful Prompt Engineer. Below, we detail some best practices that will help you create prompts to achieve your desired outcomes.

Define the objective

Identify your goal before you start creating prompts. Specific objectives will help you develop focused instructions to guide the AI system.

Here are a few examples of possible goals:

• Prompt the AI to create a dark, dramatic soundtrack for a movie trailer.
• Develop prompts for an AI tutor that explains complex physics concepts to students.
• Craft prompts for a legal AI to summarise Supreme Court decisions.

Provide relevant context

Unlike humans, AI technology doesn’t have previous experiences and contextual understanding. Prompt Engineers must provide relevant background information to get accurate answers. This context could include:

• Constraints
• Desired formats, such as charts or poems
• Expected length
• Industry-specific technical knowledge
• Relevant events
• Tone

Use clear and concise language

Large language models understand human text, but they can’t read the user’s mind. Avoid ambiguity by using concise and precise language.

For example, “Write a two-paragraph email explaining that a shoe order has been delayed due to weather” will generate a more detailed response than “Tell someone their order got delayed.”

Refine your prompt based on the AI’s response

Effective prompt engineering requires iterative refinement. Keep adjusting your prompts in response to the AI’s outputs until you get highly relevant responses. Changing even a single word can drastically alter the outcome, so never settle for the first response.

Experiment with different prompt engineering techniques

Prompt Engineers use many prompting techniques, including:

• Chain of thought prompting - Split complicated tasks into intermediate steps to help large language models engage in complex commonsense reasoning.
• Few shot prompting - Include specific examples to help generative AI models understand the type of response you want.
• Least to most prompting - The Prompt Engineer instructs the AI model to break a problem into subproblems and solve them in order.
• Zero shot prompting - Create simple AI prompts with minimal context.

Understand the limitations of the AI model

Researching the limits of the AI model’s ability will allow you to set realistic expectations. For instance, text-to-image models often generate nonsensical text that the Prompt Engineer must manually replace or erase.

Analyse the final product

Large language models and other AI systems can provide inaccurate or biassed information. Always check AI generated output carefully for errors and hallucinations.

Prompt engineering as a career

Prompt engineering is a relatively new discipline, but people with AI skills can pursue several career opportunities in this area. Here are two prompt engineering jobs:

Prompt Engineer

A Prompt Engineer uses natural language to create effective AI prompts. They also evaluate AI systems to identify technical issues and improve performance.

Prompt Engineers may work for creative companies to design AI images, videos, and other content. Other companies hiring Prompt Engineers include consulting and tech firms.

According to Glassdoor, the average base salary for Prompt Engineers in the UK is £52K. However, Prompt Engineers also average an additional £9k in bonuses and other non-salary compensation.

Prompt Engineers typically need these skills:

• In-depth understanding of artificial intelligence and machine learning principles, such as supervised and unsupervised learning
• Knowledge of prompting techniques
• Natural language processing skills, including sentiment analysis and text classification
• Proficiency in Python and other relevant programming languages

AI Security Engineer

An AI Security Engineer develops security measures to protect AI systems from cybersecurity threats. For example, they can mitigate prompt injection attacks by manually verifying outputs and filtering user inputs to block malicious prompts.

Glassdoor reports that AI Engineers earn an average salary of £51K, while Information Security Engineers earn nearly £62k on average.

AI Security Engineer roles require expertise in these areas:

• Cloud security principles
• Cybersecurity measures, such as encryption and vulnerability management
• Natural language processing and neural networks

Uplevel your AI skills with Multiverse

Prompt engineering is one of the newest career paths in artificial intelligence. The demand for these professionals will likely grow as more companies rely on generative AI to create content and drive innovation.

Multiverse’s free AI Jumpstart module allows apprentices to study prompt engineering, machine learning, and other AI skills while completing a related apprenticeship program. Apprentices also learn how to ethically use AI models in their current roles for free— all while receiving their regular salary.

Complete our easy application to learn more about how Multiverse can help you achieve your career goals in the tech industry and beyond.

Companies use the insights they gain from business analytics to create data-driven strategies. These approaches can improve customer satisfaction, operational efficiency, and profitability. Retailers, for example, can use data analytics to predict which products will sell fastest and optimise its supply chain management.

The people who wrangle data to create strategies are Business Data Analysts. Business Data Analysts and professionals with similar titles help companies make sense of data and apply insights strategically. This career path often appeals to people who enjoy solving problems and crunching numbers. But is it the right path for you? Below, we break down everything you need to know about becoming a Business Data Analyst, including responsibilities, skills, and salary. Let’s dive in.

What is business data analytics?

Business data analytics uses software and statistical techniques to interpret data and gain meaningful insights. This process allows organisations to understand their operations better and improve performance. Business analytics also assists with strategic planning and risk management.

Say, for example, a national restaurant brand wants to update its menu. Data analytics allows the company to interpret customer reviews and sales trends to determine which meals and ingredients perform best. Based on these insights, the restaurant can tailor its menu to satisfy customers and boost sales.

Understanding business data analysis

You may have already started to master some of the components of business data analytics. This process involves a few basic steps:

1. Ask a question - Start with a specific question or concern you want to address with data. For instance, you could explore customer trends to discover why your business's sales have declined.
2. Data collection - Identify relevant data sources and gather information. You could survey customers or use data mining techniques to harvest social media posts.
3. Data cleaning and processing - Organise the raw data into a usable format. This often involves transforming data by loading it into an environment optimised for analysis. You’ll also fill in missing data, remove inconsistencies, and correct errors.
4. Data analysis - Apply statistical techniques and software to reveal patterns and correlations in the data set.
5. Data visualisation - Use software to transform the data into easy-to-understand graphics. These visualisations may include charts, graphs, and maps.
6. Data interpretation - Study the results to extract meaningful insights. For instance, you might determine your target audience’s interests have changed, leading to a sales dip.
7. Communicate findings - Share your results with decision makers and recommend the next steps. In this scenario, you might advise developing new services that better align with your consumer base.

Real-world applications of business data analytics

Business data analytics has many practical applications across industries. Here are three case studies that illustrate the value and versatility of this approach.

Tracking Machine Health in Manufacturing

The manufacturer John Deere uses data analytics to improve its customer service and repair machines more effectively.

The company instals advanced telematics software in its construction machinery. The software collects data about different aspects of machine behaviour, including fault codes, fuel consumption, and idle time. This data gets streamed through the cloud to John Deere’s Machine Health Center in Iowa.

Local dealers use this data to diagnose machine problems remotely instead of travelling to construction sites or farms. They can select the necessary parts and repair tools to bring to the service appointment, saving time and reducing trips. Additionally, John Deere uses this information to identify and fix potential manufacturing errors.

These applications allow John Deere to improve its performance over time and provide more efficient service.

Improving patient care in healthcare

Data analytics allows Stanford Medicine Children’s Health to understand and improve the patient experience.

The organisation uses evaluation forms to collect data about patients’ experiences during their hospital stays. Analysts use AI tools to synthesise the information and reveal patterns, such as complaints about staff responsiveness and wait times.

According to Chief Analytics Officer Brendan Watkins, the organisation places these insights “directly into the hands of the folks who can make a difference, make systemic change with this data.” These stakeholders include healthcare providers who can use the information to deliver better patient care.

Boosting productivity in retail

The American grocery chain Kroger has developed two data-driven applications to improve employee productivity.

First, the company created a task management application for Night Crew Managers. This application displays each store’s inventory and merchandise deliveries in real time. It also uses data analytics to optimise employee to-do lists to help them restock stores efficiently.

Additionally, Kroger uses a store management application to streamline store audits. This tool also automatically recommends tasks for employees as they prepare for audits.

Both applications help Kroger associates adapt to changing store conditions and improve the customer experience.

The role of a Business Data Analyst

A Business Data Analyst uses data to solve business problems and identify growth opportunities. They also support decision makers by offering recommendations based on their findings.

The day-to-day responsibilities of these professionals vary by role but typically include these tasks:

• Collect data from a wide range of sources, such as customer feedback forms, financial records, or in-product data from a software application
• Develop databases to organise information
• Process raw data to prepare it for analysis
• Build and train machine learning (ML) models to analyse enormous data sets
• Design predictive models to forecast potential outcomes
• Create data visualisations
• Deliver presentations about their findings
• Collaborate with colleagues in marketing, sales, and other departments
• Learn about the latest advancements and trends in business data analytics

Business Data Analysts wield significant influence in their organisations. Leaders rely on their expertise for a broad range of business decisions, such as:

• Choosing marketing and sales tactics
• Deciding whether to invest in a new venture
• Managing financial resources
• Selecting prototypes to develop into new products
• Scheduling manufacturing equipment for maintenance and replacement

Because Business Data Analysts deliver considerable value, they can earn relatively competitive salaries. According to Glassdoor, the median salary for this career is £38800 per year in the United Kingdom.

Essential skills for Business Data Analysts

You’ll need the right technical and soft skills to thrive in a business data analytics role. If you’re interested in this career path, focus on developing these foundational abilities.

Technical skills

Business Data Analysts rely heavily on technology to interpret data. After all, you wouldn’t get very far if you had to analyse a spreadsheet with thousands of data points by hand. These technical skills will help you manage and process data effectively:

• Structured Query Language (SQL) - This language allows you to organise, manipulate, and search structured databases.
• Programming languages - Use R for exploratory data analysis and data visualisation. Python enables you to automate tasks, clean data, and build ML algorithms.
• Statistical analysis - Understand how to use statistical methods to interpret data. For example, descriptive analytics evaluates historical data to understand events and patterns. Prescriptive analytics uses past and present data to recommend future actions.
• Artificial intelligence (AI) and ML - Companies increasingly rely on AI and ML to analyse data and predict future trends. Study foundational ML concepts like clustering algorithms, decision trees, and linear regression. You should also know how to use ML frameworks and libraries like PyTorch and TensorFlow.
• Data visualisation - Transform data into accessible and visually appealing graphics. Popular data visualisation platforms include Microsoft Power BI, Tableau, and Zoho Analytics.
• Reporting - Use business intelligence tools like Qlikview and Sisense to create interactive dashboards and reports for stakeholders.

Soft skills

Data Analysts need strong interpersonal skills to excel in the workplace, including:

• Adaptability - Business analytics evolves quickly, so prepare to embrace new approaches and tools.
• Collaboration - Share ideas and responsibilities with team members from different backgrounds and departments.
• Communication - Express your ideas clearly in conversations, presentations, and written reports. You should also learn to translate complex technical concepts for lay audiences.
• Critical thinking - Evaluate the accuracy of data, identify potential biases in the results, and assess potential recommendations for feasibility.
• Negotiation - Collaborate with multiple stakeholders to develop solutions that meet everyone’s needs.
• Problem-solving - Learn how to approach problems from different angles and devise novel solutions.

Steps to becoming a Business Data Analyst

There’s no universal blueprint to becoming a Business Data Analyst. You can use many resources and strategies to gain the knowledge and skills required for this career. Here are a few common pathways.

Study at university

A university education is a traditional — but not required — educational pathway for Business Analysts. Many colleges and universities offer degrees in business analysis, data science, mathematics, and other relevant fields.

Enrolling in a business data analytics program offers several benefits. A structured curriculum gives you a solid foundation in data management, statistical analysis, and other necessary skills. You’ll also receive feedback and guidance from faculty.

But a university education has a few drawbacks. First, a three-year degree requires a significant investment of time and money. In England and Wales, full-time students pay £9,250 on average annually for tuition, books, and other expenses, according to the BBC. You’ll also need to dedicate extensive time to studying and attending classes. People with full-time jobs, families, and other obligations may struggle to balance their responsibilities with a college education.

Many universities also provide limited hands-on experience. A student reading business data analytics may learn foundational theories but not be able to apply these concepts in the real world. As a result, they may lack the experience and portfolio needed to land a position.

Obtain relevant certifications

Certifications enable you to develop your skills and showcase your abilities to potential employers. Here are a few relevant credentials that could help you prepare for data analytics roles:

• Entry Certificate in Business Analytics (ECBA) - The International Institute of Business Analysis offers this certificate for aspiring and entry-level data professionals. The certification demonstrates foundational competencies in business analysis planning, elicitation and collaboration, and other areas.
• Professional in Business Analysis (PMI-PBA) - The Project Management Institute designed this certification for Business Analysts who use data to support projects.
• Certified Foundation Level Business Analyst - The International Qualification Board for Business Analysis offers this foundational certification. It demonstrates proficiency in business modelling and creating business solutions.

Certifications cost much less than the average four-year degree and typically take less than a year to earn. They can accelerate your professional development and prove your commitment to the field to potential employers.

Upskilling

If you’re an established professional, you may already have many skills needed to succeed in business analytics. But everyone has areas for improvement. Thankfully, upskilling can fill any gaps in your knowledge — making you more productive at work and better prepared to advance in your career.

In fact, according to Gartner, 75% of employees who participate in upskilling programs agree it contributes to career progression.

Multiverse’s Advanced Data Fellowship is one of the most effective ways to level up your skills. This free program allows you to immerse yourself in the field of business analytics — all while working for your current employer.

The apprenticeship includes 23 immersive modules that teach you how to make data driven decisions and improve business processes. You’ll also learn data analysis and visualisation skills you can immediately apply in. This fusion of structured learning and hands-on experience will help you grow your career in business analysis.

Gain hands-on experience

Developing practical experience strengthens your skills and gives you a competitive advantage in the job market. Look for opportunities to apply your skills with real data sets.

For example, you could volunteer to analyse customer data for your current employer and recommend ways to improve marketing initiatives. You could also help clients solve business problems as a freelancer or consultant.

As you create projects, assemble them into a digital portfolio. Include a detailed description of each project and highlight their measurable outcomes. Potential employers can review your portfolio to gauge your experience level and skills.

If you’re looking for hand-on projects, Multiverse’s Applied Analytics Accelerator equips you to upskill your data chops while staying in your current role.

Take the next step in your data analytics journey with Multiverse

As a Business Analyst, you play a critical role in business decision making and strategic planning. Your insights can help companies develop cutting-edge innovations, improve customer experiences, reduce costs, and more.

Prepare for a career in this in-demand field with a Multiverse apprenticeship. Apprentices get paid to upskill and gain hands-on experience with real business analytics projects. They also receive one-on-one tailored to their goals.

Tell us about yourself by completing our quick application, and the Multiverse team will get in touch with the next steps.

Online learning is growing in popularity. Research by the Oxford Learning College found that 78.2% of e-learning students "would recommend e-learning to someone else." Meanwhile, the UK's online education market continues to grow at pace.

Not only is online learning rising in popularity, but it can also help you develop technical job skills that are critical to career success. A recent Microsoft study revealed 82% of UK jobs already require digital skills and that 69% of leaders feel their organisation suffers from a digital skills gap.

To stay professionally competitive — now and in the future — upskilling or reskilling in durable tech skills through online training could be the solution.

The advantages of free online training

Why consider online training to build a more resilient career? There are plenty of reasons to give online training a look — if you haven’t already. But we think these four reasons in particular are key.

Affordability

The first advantage is right there in the name: Free online training is affordable. Unlike paid-for online classes and boot camps, you don't need to self-fund the programme. You also don't need to quit your job to complete the course. So not only will you save money, but you'll continue to earn money as you improve your existing skills or learn new ones.

Flexibility

The second advantage is flexibility. Because your instructor will deliver the training (or share course materials) online, you can complete the learning around any existing commitments.

Variety

The third advantage is variety. When you choose to learn for free online, you can access various courses across multiple industries. From upskilling in tech to reskilling in business, an array of free training courses are available online.

Accessibility

The fourth advantage is accessibility. There's no cost barrier, no strict scheduling requirements, and you can complete the training from anywhere. Plus, there's likely a course available for your existing industry or an industry you'd like to enter. As a result, free online training is accessible by nature.

How to find the best free online courses

Here's how to search for and evaluate the best free online training courses:

Course content

You likely have a specific end goal in mind for your online training. So, the first thing you should assess is the course's content. Specifically, ask yourself, "Does this course cover the topics and skills I need to know to achieve my goals?"

Let's say your end goal is progressing from your current role as a Junior Data Analyst to a Senior Data Analyst. To achieve this, your Manager has recommended improving your artificial intelligence (AI) skills. In this case, you'll need to find online training with course content covering AI skills.

Instructor credentials

When evaluating the quality of a free online training course, take instructor credentials into account. To start, you'll want to learn from someone who is an expert in their field.

Depending on the course, you'll also need to learn from an instructor who has the right level of credentials to begin with. Otherwise, you might not be able to obtain an accredited qualification.

Certification

An accredited qualification might not be necessary if you want to take a short online training course to improve in a specific area. However, a formal qualification is sometimes required if you have a specific career goal. Some roles require a particular accreditation, for example. In that case, double-check that you'll earn any accredited qualifications you need to reach your goals.

Peer reviews

You can learn a lot about free online courses based on what people who've taken the training say about it. To find out what others say about their experiences—positive or negative — check course reviews and testimonials.

At Multiverse, we share learner testimonials and reviews throughout our website. You can see this in action on our AI for Business Value training course page. We also share learner success stories via our blog.

Top platforms offering free training courses

From Coursera to our own offerings here at Multiverse, we discuss some of the top platforms providing free training courses online.

Coursera

Coursera is an online learning platform that offers training in many areas, from data science to arts and humanities. When it comes to free training, Coursera offers courses in tech. However, these options might not come with accredited and industry-recognised qualifications.

edX

edX offers free courses in a range of study areas, including the arts, science, and mindfulness training. Although you can view most edX courses for free, you must be a student registered in the paid verified track to gain a certificate of completion.

Udemy

Udemy offers free courses in multiple areas, including tech, personal growth and wellness. The free courses can range from 30 minutes to over four hours. Still, they're typically not as in-depth as Udemy's paid courses.

Khan Academy

Khan Academy provides free training in areas like maths, art, biology, and computer programming. The platform caters to various levels, from the Early Years Foundation Stage (EYFS) to Further Education (FE). Khan Academy also offers online "life skills" courses that apply to most people. That said, in the platform's own words:

"Khan Academy is not an accredited school, and work done on the site does not count towards a diploma or degree program. Khan Academy is best used as a supplement to your normal schooling..."

Multiverse

Multiverse provides high-quality apprenticeship programmes that combine work, training, and community. We focus on developing skills for the future, so our programmes cover the digital, tech and business industries. Courses cover various topics, from business operations to data science and software engineering.

Because we specialise in specific areas, we attract the best coaches and trainers who are experts in their field. We partner with your employer to make our courses tuition-free for learners, so you can develop critical skills at no cost. Upon completion, you'll also gain an industry-recognised and accredited qualification.

Key areas of learning available for free online

Let's break down some of the different categories of free training available online.

• Technology: You'll find courses in data analysis, machine learning (ML) and software engineering under the technology banner. However, you can also find short courses covering specific coding elements or even how to use certain software and tools.
• Business and entrepreneurship: Business and entrepreneurship are broad subject areas. That means you'll find a mixture of niche and more generalised courses. For example, at Multiverse, we offer an AI-Powered Productivity programme combining AI tech, prompting, and data to help busy professionals scale their productivity and take back their time at work. However, there are also more general training courses available online. Some platforms offer training in general business skills (like communication and negotiation) and even how to start a business.
• Science: Biology, chemistry, and physics are all examples of science subjects. However, you can also find free online training in multidisciplinary fields like data science. Data science incorporates methods — including the scientific method — from different disciplines.
• Creative arts: Online training in the creative arts can cover anything from theoretical courses about Asian art and storytelling to more practical courses. Photography, digital painting, or pattern making are examples of more hand-ons courses in the creative arts.
• Humanities: The humanities typically include subjects related to human society and culture. (So the study of languages, literature, the arts, history, or philosophy.)

Tips for staying motivated and engaged

It's easy to feel demotivated when you see everything you need to do to get from A to B in its entirety. Instead, try breaking down your learning journey into manageable steps by setting learning goals. Simply put, these are specific, measurable milestones for your learning.

Setting yourself a deadline for completing assignments or learning specific theories are examples of learning goals. Depending on the course, your coach or instructor can help you structure your learning goals in line with your learning needs and the course itself.

Once you have your learning goals, creating a study schedule will help you complete them around your existing commitments. Even something simple in a spreadsheet, using your email calendar, or a physical weekly planner will help you plan, visualise and stick to your schedule.

Completing online training can sometimes feel isolating. When you feel isolated, it can impact your motivation, but participating in course forums can help. If your training provider has course forums or a learning community, you can speak to others who are completing the same course.

The Multiverse Community is an example of this in action. Through this powerful network of apprentices and alumni, members can inspire each other and learn new career skills as they progress through their training.

Success story: Transformations through free online training

Online training has helped countless professionals — both entry-level and experienced — gain the vital skills they need to take the next step in their careers.

One such example is Multiverse apprentice Jeffrey Owusu. Owusu was promoted to a senior management role during his apprenticeship and helped his colleagues improve their productivity.

"I was able to improve not only what I was doing, but also make the lives of my coworkers easier," Owusu says.

Pro tip: If you've already completed an apprenticeship and want to explore progression routes, consider creating a Multiverse apprenticeship profile. If you're eligible, a member of our team can then discuss apprenticeship levels and opportunities with you.

Certifications and recognitions: Do they matter?

Do nationally and industry-recognised certifications matter? Yes and no. It really depends on your goals.

For example, if you want to complete a short online course for personal reasons (like learning the basics of data analysis to track advanced footballing metrics as a hobby), certification doesn't matter so much. But if after completing your short course, you realise you enjoy wrangling data so much that you want to elevate your current career trajectory to work with it more, then certification becomes crucial.

For the latter, a tuition-free Data Fellowship apprenticeship with online training elements will help you garner the skills you need to use data in the workplace. Aside from hands-on work experience upon completion, you'll also gain a Level 4 apprenticeship standard in Data Analytics. An industry-recognised certification like this will help you stand out in future job searches and progress within your tech career.

Future trends in online learning

It's hard to predict the future, but it seems like the future's already here in many respects. According to Oxford Learning College, "Almost half (49%) of e-learning students said that if an online course had a VR element, it would encourage them to sign up." It's no wonder then, that virtual reality (VR) and augmented reality (AR) courses are already on the horizon.

Microlearning, a less futuristic online learning trend but arguably more applicable to most learners, is also on the rise. Microlearning breaks down educational content into smaller, more digestible chunks. This bite-sized learning typically takes place in three—to five-minute intervals.

Given that research shows microlearning helps learners stay engaged, finish lessons faster, and increase knowledge retention, its growing popularity isn't surprising.

Aside from virtual reality (VR) courses and microlearning, the increasing credibility of online credentials is another rising trend. This increase is likely due to the fact that so much of our life and work takes place online now. So why should learning be any different? The advantages that we discussed earlier (flexibility, variety and accessibility) also help to improve the credibility of online training.

How to leverage free online training for career advancement

Maybe you want to advance in an existing career, or perhaps you'd like to pivot and start a new career entirely. Either way, the knowledge and skills gained from free online courses can help you achieve your goals. Let's explore each, starting with how to advance in an existing career through upskilling.

We'll return to an earlier example: You want to progress from Junior to Senior Data Analyst. Your Line Manager recommended improving your AI skills as a first step.

To demonstrate your commitment, you complete a short, free online AI training course. You then update your Line Manager, who recommends you for a tuition-free Advanced Data Fellowship programme. As a result of the training, you enhance your skill set and take on new responsibilities. Midway through the programme, your Line Manager promotes you from Junior to Senior Data Analyst.

Conclusion: the endless possibilities of free online training

83% of UK businesses say technology advancements have increased the need for new skills in the workforce. As a result, we cannot overstate the career value of completing high-quality free training courses online.

Whether you use the training to get a foot in the door of a new career path or to progress in an existing one, there's never been a better time to learn new tech skills. Contact us today to learn how Multiverse can help you take the next step in your career, for free.

Data science may be a good fit if you have technical skills and enjoy spotting patterns. It's also an in-demand specialism that commands above-average pay.

A 2021 study estimated there were at least 178,000 unfilled data specialist roles in the UK. Cut to 2023, and the availability of employees with these skills still doesn't match the demand. Aside from being a high-growth role, the average Data Scientist's salary is on the higher end — even for entry-level roles. If that all sounds appealing, keep reading to learn more about this field.

What is data science?

Data science aims to uncover meaningful insights from data. As a discipline, it uses mathematics, statistics and computer programming practices to achieve this. People who work in data science must also understand technology like artificial intelligence (AI) and machine learning algorithms.

Companies rely on data science specialists to help them gather, clean and organise data so they can make sense of it. Organisations can then access the data insights needed to improve decision-making and enhance products. Data science also helps companies solve complex problems and find growth opportunities otherwise hidden in raw data.

What is a Data Scientist?

A Data Scientist is a practitioner within the data science field. Data Scientists are similar to Data Analysts. But how these professionals use data is different.

Like Analysts, Data Scientists spot key trends, patterns and anomalies in business data. However, Data Scientists use more advanced mathematical and programming techniques. So, they may build custom programmes to automate data collection rather than rely on pre-built solutions.

Data Scientists also help uncover which questions companies must ask to solve problems and encourage growth. They then use data to help companies find answers to these questions. Because the role is technical and business-focused, Data Scientists must be technically and commercially minded.

A Data Scientist's responsibilities may include:

• Collecting, cleaning and organising raw data
• Using coding languages like Python to edit and analyse data
• Creating predictive models to forecast future business outcomes
• Building programmes that automate data gathering and processing
• Communicating data insights to non-technical colleagues
• Understanding business pain points, challenges and priorities
• Using data analysis methods to answer business-critical questions
• Keeping up with industry standards, tools and emerging technology like AI

Why does data science matter?

The World Economic Forum (WEF) forecasts Data Scientists will land in the top 10 fastest-growing roles from 2023 to 2027. But we don't need to look to the future to know that data science matters — data science skills are already in demand.

Research by The Department for Culture, Media and Sport (DCMS) shows 48% of UK businesses are "recruiting for roles that require hard data skills." Machine learning, programming, and advanced statistics are examples of the required skills. All of these are heavily involved in the data science landscape.

Given the business potential, the demand for data science skills within the workforce isn't surprising. Organisations can leverage data science to drive innovation, create efficiencies and encourage growth. And with ever more data at their fingertips, companies need people who can tell the stories it holds.

Business benefits aside, rapid technology advancements, alongside the sheer volume of data now available, mean data science is no longer a 'nice to have.' To stay competitive, organisations must be more innovative with how they use data. Many are already bridging this gap by investing in data science specialists, tools and infrastructure.

What is the data science process?

The data science process is a series of steps that help you solve a problem, gain insights and improve business outcomes using data. There are different types of data science processes — the one you use will depend on the project, task and company preferences.

CRoss Industry Standard Process for Data Mining (CRISP-DM) is a common type of data science process. To use CRISP-DM, you'll typically follow these steps:

1. Business understanding: You work with colleagues and stakeholders to determine business needs and goals. Finding better ways to market to specific customers is an example of a business goal.
2. Data understanding: You gather the data you need or already have and identify what needs to happen to it next. You may need to clean the data first, for example.
3. Data preparation: You decide how best to prepare the data for modelling. That could mean dividing your data into specific categories.
4. Modelling: You choose the modelling techniques you'll use based on the nature of the problem.
5. Evaluation: You review the performance of the models in line with the original business goal.
6. Deployment: You add the models into a production environment, including integrating them into existing systems. You also show stakeholders how to access any results.

Data science tools

Many tools can help you with data science tasks, and the ones you use will change depending on the nature of the task. That said, Microsoft Excel, Python, and Tableau are examples of everyday data science tools. Let's take a look at each.

Microsoft Excel

From simple analysis and reporting to finding complex insights from multiple datasets, Excel is an asset to any data scientist's toolkit. Excel's pivot tables and formulas are also handy for quick data exploration. Another useful feature of Excel is the ability to automate smaller, manual tasks like data cleaning.

Python with libraries

Python is a versatile programming language commonly used in data science. Data Scientists use Python to wrangle, cleanse and edit data. You can also use it to develop business insights based on statistics. You can use a Python library like Pandas for data manipulation and analysis. Meanwhile, other libraries like Seaborn and Matplotlib help with data visualisation.

Tableau

Tableau is a popular Business Intelligence (BI) tool. Data Scientists use it to connect to different data sources and share insights throughout a company. You can also use BI tools like Tableau to visualise and present data insights in ways that non-technical colleagues can understand.

Data science use cases

From driving efficiencies to data analysis, here's how UK companies use data science to impact business outcomes.

Creating efficiencies

Morgan Sindall Infrastructure delivers some of the UK's most complex and critical infrastructure. The organisation recently partnered with Multiverse to launch the Infrastructure Data Academy. The purpose is to upskill Morgan Sindall Infrastructure's workforce, focusing on data science skills like analytics, AI and predictive modelling.

Sarah Reid, Managing Director of the Morgan Sindall Infrastructure Highways business unit, says the data upskilling project "enables the business to become a digital-first organisation, creating efficiencies through new technology investments to further develop our culture around using data in everyday operations."

Example: A key challenge for a company working with infrastructure could be optimising highway groundwork with longevity top of mind. By leveraging data insights from past projects and building predictive modelling, the company could extend the lifespan of highway infrastructure. Doing this creates efficiencies by lowering the number of major repairs or reconstruction projects needed over time.

Data analysis

The Maritime and Coastguard Agency (MCA) has partnered with Multiverse as part of a government drive to boost data skills across the civil service. The MCA aims to train its workforce to "make better use of data" — a key focus will be data analysis upskilling.

"There is a growing understanding in government of just how much data analysis can add to an organisation – in all aspects of the business," says Charis Doidge, Head of Data Science and Analytics at the Maritime and Coastguard Agency. Charis cites building the MCA's in-house capabilities and driving efficiency as two benefits of better use of data through analysis.

Example: A company like the MCA could use data analysis to assess the performance of its search and rescue operations. To do this, the company would analyse data from past incidents. It could then find areas for improvement, including ways to reduce response times to emergencies at sea.

Uncovering data narratives

When you know how to interpret and handle data, you can analyse it more effectively. A more innovative approach to data analysis allows you to uncover patterns and insights that would otherwise stay buried in unstructured or unconnected data. These insights help you find and present meaningful data stories.

Example: Let's say you work for a retail company that wants to create better online customer experiences. To help with this goal, you use online engagement data to run a customer journey analysis. The data reveals which touchpoints lead to conversions and where customers leave the site. You can then use the insights to help you optimise what's working and improve what isn't. The result will be more custom experiences for your customers—and more conversions for your company.

Data science skills

If a data science career path sounds appealing, you should focus on developing specific skills. These include working on your technical, mathematical and statistical abilities. But remember, you also need a strong commercial focus. Let's take a closer look.

Technical ability

You'll need technical ability to work in data science. That means using SQL (Structured Query Language), Microsoft Excel, and data visualisation techniques. You'll also need computer programming skills, including knowing programming languages like Python.

Mathematical understanding

Understanding maths and statistics is crucial for data science. You'll need to know linear algebra, statistics and probability. With this knowledge, you can analyse and interpret complex datasets to uncover patterns. A mathematical understanding will also help you build predictive models and test ideas.

Commercially minded

Data science isn't just technical. How you communicate your findings to other stakeholders matters, too. To have a successful career in data science, you'll need to know and use the entire data science process to meet business needs. You'll also need to be able to share your data insights with non-specialists in a way they can understand.

What do data scientists make? Data scientist salary in the UK

The average Data Scientist salary in the UK is around £55,420 per year (Talent). But what you earn as a Data Scientist fluctuates depending on skill level and experience. That said, entry-level positions are higher than average compared to other professionals.

An entry-level Data Scientist can make around £42,500 on average per year. That's over £7,500 higher than the typical annual earnings in the UK. Then, as you progress in your career and gain more experience, you could be on track to earning up to £80,000 a year.

Aside from skills and experience, your location may also affect how much you make as a Data Scientist. For example, in a city like London, the cost of living is higher, so salaries are higher. According to Indeed, the five highest-paying cities for Data Scientists in the UK are:

• London
• Reading
• Birmingham
• Edinburgh
• Manchester

Here’s a breakdown of the UK's top-paying cities for Data Scientists.

Data science courses: best way to learn data science

Online courses, university and professional apprenticeships are three popular ways to learn data science — here's what you need to know about each.

Online courses

Some Further Education (FE) colleges may offer part-time online data science courses or qualifications. Platforms like Coursera provide the same, but they might not have qualifications or accreditations. You may also need to self-fund these courses if your employer doesn't fund professional development.

University

University is another way to learn data science. If you choose this option, you'll want to study for a degree or postgraduate qualification in a relevant subject like maths, statistics, or data science. But university is undeniably expensive. The combined cost of tuition fees and accommodation costs in England is around £50,000. You're also not guaranteed a relevant data science role when you graduate.

Professional apprenticeship

What if you don't want to pay nearly to earn a degree-level qualification or for continuing education — but you do want to progress within your data science career? In that case, consider a tuition-free alternative to university, like an apprenticeship.

Unlike university, you don't need to self-fund the programme — it's totally free for learners with no strings attached. And depending on the apprenticeship level of study, you can earn a degree equivalent qualification.

To top it off, you'll learn job-ready skills through an on-the-job training programme that fits around your work schedule. So, even if you're a few years into your career, you can keep working full-time while you learn without taking a career break.

Learn data science

To succeed in data science, you should learn to use Excel, Python and BI tools like Tableau. You'll also need technical, mathematical and business skills. With a Multiverse apprenticeship like the Advanced Data Fellowship programme, you can expand upon the data science skills you already use on the job for free. You'll also get paid your regular salary to learn and apply your new abilities in your day-to-day role.

To get started, simply create a Multiverse profile. Our team can then double-check your eligibility and discuss apprenticeship options with you.

A typical Data Analyst salary is around £8,000 more than the average full-time UK wage. And Data Analyst is fast becoming one of the crucial jobs of the future.

Keep reading to learn more about the role and how to make data analysis a core component of your career — whether you're an entry-level professional or a fledgling junior analyst.

What is a Data Analyst?

A Data Analyst collates, cleans, and reviews raw data. In some roles, Data Analysts may use data to solve business problems and make decisions. In others, they may share insights about trends or anomalies to help their colleagues create data-driven solutions to business problems.

In either case, a Data Analyst should focus on making it easier for colleagues and stakeholders to understand complex data.

Responsibilities

A Data Analyst’s responsibilities include:

• Finding, cleaning, and organising raw data
• Spotting trends, patterns, and irregularities in data
• Making raw data easy for non-specialists to understand
• Presenting and reporting upon data insights
• Helping Business Analysts build data-led processes and systems
• Keeping up with industry standards, tools, and emerging technology like Artificial Intelligence (AI)

Skills

Data Analysts will typically have the following skills:

• Mathematical understanding: Linear algebra, statistics,and probability are crucial for data analysis.
• Problem-solving: You find ways to solve project and technical challenges.
• Technical ability: You use SQL (Structured Query Language), Microsoft Excel, and data visualisation techniques.
• Database basics: You can manage and navigate databases using database management systems (DBMS).
• Business acumen: You know and use the entire data analytics process to meet business needs.
• Communication: You can share data insights with non-specialists through verbal, visual, and written communication.

How to start (or grow) your Data Analyst career

To become a Data Analyst, you’ll need to understand data analysis basics, SQL, Excel, and more. Here’s how to get a head start — or get ahead — on your Data Analyst career path.

1. Master data analysis basics

To become a Data Analyst, you must first learn the basics of data analysis. Learning the basics won’t just help you start your career path. It will also help you answer different questions and develop more advanced techniques. Plus, you’ll have a solid foundation to build upon should you want to progress in your career.

When it comes to data analysis, understanding the two primary forms of data analytics — qualitative and quantitative — is a great place to start. You’ll want to learn what they are and their differences. Here’s a quick lesson at a glance.

Quantitative analytics uses data you can measure with numbers like costs, revenue, and projections. Meanwhile, qualitative analytics uses data you can’t measure with numbers, like data gathered from customer satisfaction surveys.
You can use qualitative and quantitative data to answer different business questions. Quantitative data will help you answer ‘how much,’ ‘how often’ and ‘how many’ queries. In contrast, qualitative data can help you discover how customers interact with a company and the ‘why’ behind specific interactions.
Pro tip: To take this further, start learning specific data analysis methods like ‘cohort analysis’ (quantitative) or ‘discourse Analysis’ (qualitative).

2. Analyse real data

The best way to learn any new skill is to go beyond theory and put learning into practice. In the case of data analytics, this means experimenting with real data sets in a controlled environment.
Think of it this way: when you first start bowling, using the bumpers means you stay in the lane as you learn. That helps you build confidence and get better over time. The ‘bumpers’ here are existing data sets, which you can use to practice different analysis techniques.
The data is real, but it has no direct consequences to an employer, which means you can experiment without ‘breaking’ anything. You can use this to build your confidence, learn to spot trends and practice more advanced data analysis.
Pro tip: To get started, download and use free governmental data sets from the Office of National Statistics (ONS). You can also find similar open data published by the central government.

3. Learn SQL

As a Data Analyst, you’ll frequently interact with databases and database management systems. So understanding the ‘language’ they speak is a must. SQL is a type of programming language that you can use specifically for relational databases.

Data Analysts will typically use SQL for querying, updating, and deleting data. This includes tasks like selecting all records from a table, updating existing data, or managing access to database objects. You can also use SQL to add quality control measures, assuring data upload quality.

Pro tip: Aside from having proficiency in SQL, you may need to learn other programming languages like Python, R, and Java. But this depends on the specifics of your role. If you’re new to SQL and programming languages, YouTube tutorials are a great entry point. Alternatively, you can find free beginner courses on sites like Codecademy.

4. Master Excel

Data Analysts use Microsoft Excel for data manipulation, simple analysis, and reporting. Excel's pivot tables and formulas are valuable tools for quick data exploration. The right Excel knowledge makes it easier to clean and organise data.

To become a Data Analyst, focus on developing these Excel skills:

• Aggregating and joining data
• Pivot tables, visualisations, and data cleaning
• Using AI within Excel to work more efficiently

Pro tip: As with SQL, plenty of YouTube videos online can help you get started with the spreadsheet software. Then, Microsoft offers Excel video training, too. You could also download free governmental data sets and experiment with formulas and pivot tables within Excel.

5. Practise data visualisation

Being a Data Analyst isn’t just about the insights you find. How you show your findings to colleagues and stakeholders also matters. Data visualisation can help you make complex data accessible to non-specialists—a vital part of the Data Analyst’s role.

You can create various data visualisations (i.e., graphs and charts) in Excel. A business intelligence tool like Tableau can also help you explore and communicate insights from data. PowerBI is another popular data visualisation tool that can take you beyond basic graphs and charts.

Pro tip: If you have Microsoft 365, you can experiment with data visualisation in Excel as part of your subscription. PowerBI and Tableau aren’t free software, but PowerBI is the most cost-effective. You can also practice basic data visualisation techniques for free using something like Google Sheets.

6. Rehearse presenting your findings

Aside from data visualisation, your presentation skills are crucial to help non-specialists understand data and insights. Some people are naturally more confident and excel when presenting. But even if that’s not you, don’t worry—presenting is a skill you can hone with practice.

To practice presenting your findings, start a personal data visualisation project. That will give you data to explore, insights to visualise, and information to share. You can present these insights in blog posts, reports, or mock presentations to family, friends, or tutors.

When you present, explain the insights you found (i.e., trends) and how you found them (i.e., your analysis process). You should also explain why your findings matter and how they impact the project.

Pro tip: Remember, your audience is non-technical and unfamiliar with data. So speak clearly without jargon and use data visualisation to break down your findings. If you want to take it beyond charts and graphs, experiment with storytelling to make your conclusions more engaging.

7. Become an apprentice with Multiverse’s Data Fellowship

Without the proper support, becoming a Data Analyst — or upleveling your data skills — can take so much longer than it has to. Choosing a university path can cost you up to £9,250 per year just for tuition fees. Not to mention the opportunity costs of taking a career break to attend a full-time programme if you're already working.

The good news: If you're a junior Data Analyst looking to progress into mid or senior-level roles, earn a promotion, or just increase your data savviness, Multiverse can help. Through our Data Fellowship or Advanced Data Fellowship, junior or new Data Analysts can earn a nationally recognized qualification on the job without having to put their careers on hold. Also, because Multiverse partners with employers to sponsor programme costs, our apprenticeships cost nothing for learners.

Our data programmes help learners deepen expertise of highly sought-after technical skills, including Python, machine learning, data governance, and more. If you're interested in learning more, fill out our quick application form.

Pro-tip for career starters: Multiverse also offers apprenticeship programmes for entry-level learners and data enthusiasts. But unlike our upskilling programmes for junior Data Analysts, you have to be accepted by an employer, not Multiverse, to do such a programme.

This path would allow you to complete a valued Multiverse apprenticeship in conjunction with your responsibilities in a new role at a company in the UK. 

The bottom line: If you're looking for your first job in data, consider searching and applying for apprenticeships in England here.

8. Build a portfolio of data analysis work

A portfolio of your data analysis work demonstrates your capabilities at every stage of your Data Analyst career. You can improve your portfolio whenever you work on a new project or advance throughout your career. Using Multiverse programmes as an example, let’s put building a portfolio into practice.

As a Multiverse Apprentice, we’ll work with you to build a strong portfolio. As an aspiring or junior Data Analyst, you might start a Data Fellowship apprenticeship. You’ll gain a portfolio of work full of real-life projects showing you understand database fundamentals and have technical skills. Your portfolio will also demonstrate relevant experience for a new role or a career progression.

You could move onto the Advanced Data Fellowship programme if you want to progress and upskill as a Data Analyst. You’ll qualify with a body of work demonstrating your capability with advanced analysis methods and ability to deliver real-world business results.

Pro tip: Besides gaining a portfolio, both programmes offer industry-recognised certifications, further showing your competence.

9. Apply for Data Analyst roles

If you’ve worked on developing relevant skills and feel confident in your abilities, it could be time to start applying for Data Analyst roles. To simplify the application process, one of the first things you should focus on is developing your CV. If you’ve never crafted a CV, you can use a CV template to help.

Here are a few expert tips to help you get started:

• Aim to use keywords from the job description—especially for the tools, skills, and programming languages. You can add these to your CV summary, work experience, and skills sections.
• Use bullet points and shorter sentences to help recruiters scan your CV at a glance.
• Try to stick to one page, two pages max.
• Using an image of yourself on a UK CV is uncommon and can distract from your skills and experience. The safest option is to avoid doing so.
• Proofread your CV for spelling and grammar errors before applying for a job.

How long does it take to become a Data Analyst?

How long it takes to become a Data Analyst depends on your chosen path, so let’s compare the apprenticeship vs. university route.

If you choose to study a Bachelor of Arts (BA) in Computer Science at university, you could spend four years in a lecture hall. That’s before you even start your job search, apply for Data Analyst vacancies, get an interview, and (hopefully) land your first role.

In contrast, when you study a relevant apprenticeship, you become a Data Analyst from the start of your programme. It’s worth noting that different apprenticeship levels have varying completion times. That said, you can get the equivalent of a full Bachelor's degree in as little as three years, depending on your course structure.

What tools do data analysts use?

Here are the standard tools that Data Analysts use in their roles:

• Database management systems: This software helps users or applications access a database and work with the data. Some popular DBMS options include MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, and SQLite.
• Data visualisation tools: PowerBI and Tableau are two common software platforms. They help Data Analysts to visualise and present data-led insights in engaging ways.
• Microsoft Excel: Data Analysts use this spreadsheet software for data entry, calculations, and analysis. You can also create visual representations of data through charts and graphs.

Grow your career as a Data Analyst with Multiverse

Data Analysts must know SQL, Excel, data visualisation techniques, and a host of other skills. The Multiverse Data Fellowship programme covers all of this and more. Plus, you’ll earn an industry-recognised qualification and build a portfolio without putting your career on hold.

At Multiverse, we make it easy for you to get started. It takes less than 15 minutes to create a profile. Our team can then double-check your eligibility and discuss apprenticeship options with your employer.

What counts as OTJ?

‘OTJ’ counts as any time you’ve spent learning during your apprenticeship outside of the recurring tasks in your working role. There’s two types of OTJ activities you’ll need to complete in order to meet the UK government standard for apprenticeship.

Protected learning: Protected learning refers to dedicated periods within a work schedule that are set aside for learning and professional development.

Applied learning: Applied learning is defined as the hands on application of skills in the workplace

If you work 30 hours a week, 6 out of 30 hours need to be spent on OTJ related activities.

What can I do to complete my OTJ?

There’s lots of ways to fulfil your OTJ time and a portion of these will already be scheduled in your calendar by your coach during your apprenticeship. Other activities you will need to seek out yourself. Think about how the OTJ activities you choose relate to your apprenticeship before you complete them. Below are some areas you could focus on to collect OTJ hours and learn on your apprenticeship.

1. Apprenticeship bootcamps

During your apprenticeship, you’ll attend online bootcamps led by coaches who are experts in your apprenticeship field. The coaches will deliver your course curriculum, assign tasks to help you apply your learning and help you prepare for any exams you need to complete on your apprenticeship. Apprentice bootcamps will count as a big portion of your OTJ time, as they’ll be delivered in block sessions of 3 hours up to 1 day at a time.

OTJ time earned: 3 hours - 8 hours at a time (depending on session length)

2. Coach sessions

In-between bootcamp sessions, you’ll have a 1:1 monthly catch-up call with your coach to assess your progress and work on your personal and professional development.

OTJ time earned: 1 hour monthly

3. Applied learning

As well as professional learning that will be scheduled in by your coach, you’ll also need to seek out opportunities for ‘Applied learning’ which covers a broad range of activities you could complete. For an OTJ activity to qualify as ‘applied learning’ it needs to relate to the hands on application of skills in the workplace. For instance, using your recently learnt skills outside of your apprenticeship bootcamp. For example:

• Completing a coding project based on skills learnt during a software engineering bootcamp
• Completing an online course linked to your apprenticeship topic
• Organising a team building activity at work as a result of a related apprenticeship module

You’ll work with your coach on areas for personal and professional development and these areas could include development of key soft skills needed in the workplace. The following would count as applied learning for soft skills:

• Building on active listening skills by carrying out regular note taking during meetings or creating an organised note template and system for you to lookover
• Practising reviewing documentation in detail using examples and methods to retain information
• Researching tools and methods to help organise time effectively and then implementing them in your day to day schedule

OTJ time earned: 30 minutes - 8 hours plus

4. Community

The Multiverse community provides a wide range of opportunities to take part in in order for you to earn OTJ hours for your apprenticeship.

You could attend events. For example:

• Multiverse meet-ups which are social events organised by apprentices
• Lunch and Learn sessions with special guests
• Online seminar in a topic of your choice
• In-person workshops
  

The community hub also offers a great range of articles to read and video content to watch, all of which adds to OTJ time. To generate regular OTJ time, you could aim to attend 1 community event per week. Please note, not every community activity will count towards your OTJ time. Check with your coach if you're unsure. 

OTJ time earned: 20 minutes - 6 hours

5. Exam completion

Exam revision and completion during your apprenticeship also counts as OTJ time.

You can log:

• Time spent revising for upcoming exams
• Practise papers, questions or quizzes
• Actual time spent completing the exam
• Study sessions with other apprentices

OTJ time earned: 30 minutes - 4 hours plus

Where do I log OTJ time?

After you’ve completed an OTJ related activity for your apprenticeship, you’ll need to formally log it with evidence.

Multiverse apprentices have access to a specialised platform in order to do this, where they can log OTJ activities with categories, dates and times. The platform also helps apprentices organise their apprenticeship, track their progress and keep track of key upcoming dates like exams.

Multiverse apprentices also have access to a 1:1 coach they can reach out to for support. If you’re stuck for what to do for OTJ time or you’re falling behind, you could send an email to your coach asking for advice, ask peers on the community hub or reach out to your apprentice manager.

If you're looking for an apprenticeship, Multiverse offers apprenticeships in tech, data and digital skills all year round.

Start your application here.