How to become a data scientist
Interested in becoming a data scientist? In this article you'll discover the benefits, qualifications, and different routes you can take to pursue a career in data science.
What is a data scientist?
Data scientists understand complex data using skills in advanced analytics and machine learning. These skills are in demand in many areas such as finance, healthcare, retail, and technology. Learn more about what data scientists do below.
- Analyse large data sets to identify patterns and inform business strategies
- Apply machine learning techniques to develop predictive models
- Utilise data mining techniques for decision-making
- Generate clear reports and visualisations for stakeholders
- Collaborate with departments to understand data needs and provide solutions
- Stay updated with technological advancements and improve data analysis methods
- Communicate complex data concepts clearly to non-technical audiences
- Expertise in Python, R, SQL, and familiarity with Hadoop and Spark
- Strong understanding of statistical methods and applications
- Experience with machine learning algorithms and frameworks
- Proficiency in tools like Tableau, Power BI, and Matplotlib
- Strong analytical and problem-solving skills
- Excellent communication skills to convey complex information clearly
- Ability to collaborate effectively in a team environment
Salaries expectations for data scientists in the UK can vary based on experience, location, and industry. Below are some typical salaries you might expect:
- Entry-level positions typically start from £30,000 to £45,000 per year
- Experienced professionals can earn £60,000 to £90,000 per year
- Senior roles and positions in high-demand industries may exceed £100,000 annually
Usually qualified to degree level, a data analyst collects, cleans, and studies data to help businesses make decisions. They utilise tools such as Excel and SQL. A data scientist, on the other hand, builds advanced models and uses machine learning to predict future trends. They need strong programming skills studying to a master's or PhD level.
Yes, demand for data scientists in the UK is robust. Businesses are increasingly relying on data-driven decision-making. Sectors such as finance, healthcare, retail, and technology are strong employers of data scientists.
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Book an Open DayRoutes to become a data scientist
Although formal qualifications are preferred, you may wish to consider a self-taught route. Each path offers unique challenges and insights which we explore below:
To start a data science career without attending Higher Education offers some benefits :
- Flexibility
- Cost-effectiveness
You will still need to learn the essential skills, here's how:
- Master basic data science concepts through free online courses
- Learn programming languages like Python and R
- Familiarise yourself with tools like pandas and NumPy for data analysis
- Learn how to understand machine learning principles by working on projects with public datasets
We also recommend you:
- Actively engage in online communities for networking
- Seek internships or volunteer roles for practical experience
- Continuously update your skills
- Apply for entry-level data science positions, showcasing your abilities
With dedication, you can establish a good foundation for a successful data science career.
A Bachelor's degree in a relevant field such as Computer Science, Mathematics, or a Masters in Applied Data Science will offer several advantages for a career in data science:
- Structured learning with solid foundation in data science concepts, theories, and practical skills
- Comprehensive training in areas such as mathematics, statistics, programming, and data analysis techniques
- Access to academics who are experts in the field, offering valuable mentorship, industry connections, and opportunities for research
- Hands-on projects and research experiences allow you to apply theoretical knowledge to real-world problems, develop critical thinking skills, and build a portfolio of work
- Network with peers, faculty, and industry professionals
- Internships, job placements
- Gives credibility to your skills and qualifications, enhancing job prospects and career advancement opportunities
What you'll typically learn at university in a related subject:
- Statistical analysis
- Programming proficiency (Python, R, SQL)
- Machine learning
- Data visualization
- Data management
- Data mining
- Database management
- Problem-solving
- Communication
- Collaboration
- Ethical considerations
How long does it take to become a data scientist?
The journey from university to a career in data science can take between 4 to 10 years, depending on your individual goals. Check out our timeline below to find out more.
Undertake a Bachelor's degree in a relevant field such as Computer Science, Mathematics, or Data Science.
Studies should equip you with foundational knowledge in:
- Mathematics
- Statistics
- Programming languages like Python and R
- Data analysis techniques
If you decide to study with us look out for our sandwich year option and gain real experience in industry. A sandwich year extends the duration from 3 to 4 years but provides valuable work experience and opens doors to employment.
Following your undergraduate studies, consider further education, such as a master's degree in Data Science or a related field. A masters degree typically takes 1 to 2 years. Our Master's Degree in Applied Data Science deepens your understanding of machine learning, big data, and business intelligence. Upon graduating you will qualify for higher-level roles and leadership positions.
Following your masters you may wish to pursue a PhD in Data Science or a related discipline. This can take between 3 to 5+ years. A PhD involves conducting original research, publishing papers, and gaining expertise in a specific area of data science. Completing a PhD opens new doors not only to your own unique research but also to highly competitive high level roles in academia, research, and industry. PhD graduates gain higher salaries along with recognition in their field.
"Maths opens so many doors in terms of career options. Data is becoming bigger than ever and most employers offer training to apply your maths knowledge to data science."
— Simon McCormick - BSc (Hons) Mathematics
Where next?
CourseMathematics, BSc (Hons)
Frequently asked questions
Undertaking a master's degree can offer several benefits, but it depends on your goals and circumstances. Here are some reasons why opting for a master's degree might be worthwhile:
- Advanced roles - While a bachelor's degree is often sufficient to become a data scientist, a master's can qualify you for more specialized and senior positions that require deeper knowledge
- Higher salary - Data science roles tend to pay well, and having a master's degree can lead to higher earning potential
- In-demand skills - The demand for data scientists is high, and job openings are expected to grow significantly in the coming years
- A master's degree adds credibility to your resume and demonstrates your commitment to the field
- The opportunity to specialise, in areas like machine learning or bioinformatics.
- Graduate programmes provide networking opportunities with professors, industry experts, and fellow students, which can be valuable for your career
GCSE's: You’ll need a minimum of 5 good GCSE passes in maths, English (Grade 4 at GCSE is considered a standard pass and grade 5 is considered a strong pass).
A-levels: A-levels are usually required but it depends on the training route you take.
UCAS Points: These can vary depending on which university you choose to go to. As an example if you study with us you would need 96-112 UCAS points to be accepted onto our BSc Computer Science degree course, or 120 UCAS points at A2 including Maths at grade A to be accepted onto our BSc Mathematics course. (Correct at the time of writing).