What Is a Data Scientist? Salary, Skills, and How to Become One

Working as a data scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new technological advances. Data scientists have become more common and in demand as big data continues to be increasingly important to organisations' decisions. Here’s a closer look at what they are and do—and how to become one.

What does a data scientist do?

Data scientists determine the questions their team should ask and figure out how to answer them using data. They often develop predictive models for theorising and forecasting.

A data scientist might do the following tasks on a day-to-day basis:

• Find patterns and trends in datasets to uncover insights

• Create algorithms and data models to forecast outcomes

• Use machine learning techniques to improve the quality of data or product offerings

• Communicate recommendations to other teams and senior staff

• Deploy data tools such as Python, R, SAS, Power BI, Excel, or SQL in data analysis

• Stay on top of innovations in the data science field

Data analyst vs data scientist: What’s the difference?

The work of data analysts and data scientists can seem similar—both find trends or patterns in data to reveal new ways for organisations to make better decisions about operations. However, data scientists tend to have more responsibility and are generally considered more senior than data analysts. 

Data scientists are often expected to formulate their questions about the data, while data analysts might support teams that already have set goals. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyse data.

Many data scientists can begin their careers as data analysts or statisticians.

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

Data scientist salary and job growth

According to Glassdoor [1], a data scientist earns an average salary of £53,388 in the United Kingdom as of November 2024. 

Demand is high for data professionals—according to an article published by LinkedIn, several of the most in-demand jobs in the UK in 2022 involve data science and data management skills [2]. As big data continues to gain importance as the UK’s digital economy expands, data science skills will likely continue to rise in demand.

How to become a data scientist

Becoming a data scientist generally requires some formal training. Here are some steps to consider.

1. Earn a relevant qualification.

Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. Several methods can help you gain the knowledge and qualifications needed to enter this role. 

Gaining a university degree or postgraduate qualification in AI, data science, or a related field can provide you with the needed background to build data science skills. Try focusing coursework in maths, statistics, computer programming, data science, and operational research to ensure you have the high-level maths and data skills needed for this position. 

In general, you will want to focus programming efforts in languages such as R, Python, and SQL and learn software packages such as Power BI and Excel. To enter a degree programme in this area, you will typically need 2 to 3 A levels for undergraduate entrance and a degree in a similar area for postgraduate entrance. If you have a background in a different discipline, taking a master’s conversion course can help you transition into the data science field.

Another way to enter this career is through a degree or higher apprenticeship. To enter one of these programmes, you will need A levels and 4 or 5 GCSEs in grades 9 to 4. Focus on a relevant area when choosing an apprenticeship, such as the data scientist integrated degree apprenticeship or the artificial intelligence (AI) data specialist higher apprenticeship.

In some positions, a postgraduate can help set you apart from other candidates and open you up for higher-paying positions. If interested in this route, consider pursuing a master’s in data science, data engineering, or computer science.

Already have a bachelor's degree?

Consider getting a master’s in data science. At a master’s degree programme, you can dive deeper into your understanding of statistics, machine learning, algorithms, modelling, and forecasting and potentially conduct your research on a topic you care about. Several data science master’s degrees are available online.

2. Sharpen relevant skills.

If you can polish some of your hard data skills, consider taking an online course or enrolling in a relevant bootcamp. Here are some skills you’ll want to have under your belt.

• Programming languages: Data scientists can expect to use programming languages to sort through, analyse, and manage large chunks of data. Popular programming languages for data science include:

• Python

• R

• SQL

• SAS

• Data visualisation: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work:

• Tableau

• PowerBI

• Excel

• Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.

• Big data: Some employers may want to see that you are familiar with big data. Some software frameworks used to process big data include Hadoop and Apache Spark.

• Communication: Even the most brilliant data scientists won’t be able to affect change if they aren’t able to communicate their findings well. The ability to share ideas and results verbally and in written language is an often-sought skill for data scientists.

To network with industry professionals and learn more about what skills you may want to focus on, consider exploring professional sites such as the Institute of Analytics (IoA), Computer Technology Industry Association (CompTIA), and the Institute of Coding.

3. Get an entry-level position working with data.

Though there are many paths to becoming a data scientist, starting a related entry-level job can be an excellent first step. Seek positions that work heavily with data, such as data analyst, junior data scientist, business intelligence analyst, statistician, or data engineer. From there, you can work up to becoming a scientist as you expand your knowledge and skills. You can also specialise in a specific area, such as machine learning, artificial intelligence, or database management.

4. Prepare for data science interviews.

With a few years of experience working with data analytics or as a junior data scientist, you might feel ready to move into a more advanced data science position. Once you’ve scored an interview, prepare answers to likely interview questions. 

Data scientist positions can be highly technical, so you may encounter technical and behavioural questions. Anticipate both, and practice by speaking your answer aloud. Preparing examples from your work or academic experiences can help you appear confident and knowledgeable to interviewers. It is also a good idea to have your portfolio ready, complete with example projects and evidence you have the skills needed to be successful in the position.

Here are a few questions you might encounter:

• What are the pros and cons of a linear model?

• What is a random forest?

• How would you use SQL to find all duplicates in a data set?

• Describe your experience with machine learning.

• Give an example of a time you encountered a problem you didn’t know how to solve. What did you do?

Getting started

Becoming a data scientist might require some training, but an in-demand and challenging career can be waiting at the end. 

Are you just starting in data science? Get a basic crash course with IBM’s Data Science Professional Certificate on Coursera.