How do I move into a more advanced data science position from an analyst role?

If you're working as a data analyst and thinking of taking the next step into data science, you’re not alone. The transition from analyst to data scientist is a natural progression for many professionals who are passionate about using data to solve complex problems and drive innovation.  

But how do you make the leap? Let’s break it down step by step in a way that’s easy to understand. 

Understanding the difference between a data analyst and a data scientist 

Before diving into how to progress, it’s important to understand what sets these roles apart. 

Data Analyst 

• Focuses on descriptive analytics—examining historical data to identify trends and patterns. 

• Uses tools like Excel, SQL, and Tableau. 

• Typically works with structured data and delivers insights to support decision-making. 

Data Scientist 

• Involves predictive and prescriptive analytics—using data models to predict future outcomes. 

• Utilises advanced tools like Python, R, and machine learning libraries. 

• Works with both structured and unstructured data, often developing custom algorithms. 

According to a report by LinkedIn, demand for data scientists in the UK grew by 29% year-on-year in 2023, making it one of the most sought-after roles in the tech sector. 

Steps to transition from analyst to data scientist 

1. Build a strong foundation in programming 

While data analysts often use SQL and Excel, data scientists frequently work with Python or R. If you’re not already familiar with these languages, now’s the time to learn. 

How to start: 

• Take online courses: Platforms like Coursera or Udemy offer beginner-friendly courses. 

• Practice daily: Use datasets from platforms like Kaggle to build small projects. 

Pro Tip: Python is particularly popular among UK data scientists due to its versatility in data manipulation, machine learning, and visualisation. 

2. Learn machine learning 

Machine learning is the backbone of many data science applications, from recommendation systems to fraud detection. Start with supervised learning techniques (e.g., linear regression, decision trees) and gradually move to advanced concepts like neural networks. 

Resources: 

• Free tutorials on YouTube (e.g., StatQuest by Josh Starmer). 

• Books like Hands-On Machine Learning with Scikit-Learn and TensorFlow. 

3. Develop advanced data handling skills 

Unlike analysts, data scientists often work with unstructured data (e.g., images, text, videos). Familiarity with tools like Pandas, NumPy, and Apache Spark can give you an edge. 

4. Strengthen your statistical knowledge 

Data science relies heavily on statistics to validate models and interpret data. Brush up on concepts like hypothesis testing, Bayesian inference, and probability distributions. 

5. Showcase your skills with projects 

Nothing demonstrates your capabilities better than a portfolio of real-world projects. Start by solving problems you're passionate about—whether it’s analysing sports data, predicting housing prices, or building a chatbot. 

Ideas for UK projects: 

• Predicting property price trends using historical data from the UK House Price Index. 

• Analysing weather data to forecast train delays in the UK. 

6. Pursue certifications 

Certifications can validate your skills and make your CV stand out. Some of the most recognised certifications include: 

• Google Professional Data Engineer Certification 

• Microsoft Certified: Azure Data Scientist Associate 

7. Network with data science professionals 

Join UK-based communities and events to connect with other data science professionals. Platforms like Meetup and LinkedIn often feature data science workshops and conferences. 

8. Apply for junior data science roles 

Don’t wait until you’ve mastered everything—start applying for entry-level data science positions or hybrid roles like “Data Analyst with Python” or “Data Engineer”. Many UK companies are open to hiring analysts transitioning into data science. 

Exploring data science contracting 

As you work toward advancing into a data science role, it's worth considering becoming a data science contractor. Contracting offers flexibility, higher earning potential, and opportunities to work on diverse projects. In the UK, data science contractors are increasingly in demand across industries like finance, healthcare, and e-commerce. 

Why choose contracting? 

• Higher pay: Contractors typically earn more than permanent employees. For example, the average day rate for a data science contractor in the UK ranges from £400 to £800, depending on experience and project scope. 

• Variety of work: Contracting allows you to work on short-term projects, gaining experience across different sectors and technologies. 

• Autonomy: Contractors often have more control over their workload and schedules. 

Data science contracting is an excellent path for professionals ready to embrace a more independent and rewarding career.  

If you're already considering transitioning into data science, it’s worth exploring this option to maximise your earning potential and broaden your experience.  

FAQs about moving to a data science role 

1. What’s the average salary for a data scientist in the UK? 

The average salary for data scientists in the UK is around £55,000 per year, according to Glassdoor, with senior roles often exceeding £80,000. 

2. Do I need a master’s degree to become a data scientist? 

While a degree in data science, machine learning, or a related field can help, it’s not always essential. Many professionals have transitioned through self-learning and certifications. 

3. What tools should I learn? 

For data science, you should be comfortable with: 

• Python (for programming and machine learning) 

• R (for statistical analysis) 

• SQL (for database management) 

• TensorFlow or PyTorch (for deep learning) 

4. What industries in the UK hire data scientists? 

Data scientists are in demand across various industries, including: 

• Finance: For fraud detection and risk analysis. 

• Healthcare: For predictive analytics in patient care. 

• Retail: For personalising customer experiences. 

5. How long does it take to advance to a data science role? 

With consistent effort, you can move into a data science role within 12-24 months, depending on your current skill set and the time you dedicate to learning. 

Moving into a data science role from a data analyst position may seem daunting, but it’s entirely achievable with the right approach. By building your skills, showcasing your expertise, and networking with professionals, you can take your career to the next level. 

Remember, the data science journey is as much about the process as the outcome. Every new skill you acquire not only brings you closer to your goal but also enhances your ability to make a meaningful impact with data.