Should I get a master’s for a career in data science?

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 Find out about our Masters in Applied Data Science

Becoming a Data Scientist | Step by Step Guide

Q: What qualifications do I need to become a data scientist?

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).

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

Routes 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 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?

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

Find out about our Masters in Applied Data Science

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).

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How to become a data scientist

What is a data scientist?

Routes to become a data scientist

How long does it take to become a data scientist?

Where next?

Applied Data Science, MSc

Mathematics, BSc (Hons)

Computer Science, BSc (Hons)

Frequently asked questions

How to become a data scientist

What is a data scientist?

What's the main roles of a data scientist

Essential skills of a data scientist

Expected salary for a data scientist

What is the difference between a data scientist and a data analyst?

Are data scientists in demand?

Routes to become a data scientist

No formal education

University education

How long does it take to become a data scientist?

Where next?

Applied Data Science, MSc

Mathematics, BSc (Hons)

Computer Science, BSc (Hons)

Frequently asked questions

Should I get a master’s for a career in data science?

What qualifications do I need to become a data scientist?