Where to start
Data science is about making use of statistical data to predict behaviours and extract insights about the real world. Data scientists apply their quantitative skills to analyse the unprecedented amounts of data that are now being collected across all industries. You could be working as a data scientist in a large organisation, in a consultancy, or in a small start-up. Read more about this sector in the 2018 White Paper published by the Insight Data Science programme. Check out the job profile of a data scientist.
A data scientist has in-depth analytical and quantitative skills alongside more general skills such as communication, teamwork, attention to detail, innovation, project management, and commercial awareness. Many of the skills that make people good scientists are transferable to data science. Disciplines that are attractive to data science recruiters include physics, astronomy, astrophysics, physical chemistry, computational biology, neuroscience, mathematics, statistics, engineering, machine learning, operations research, economics, quantitative social sciences, and other data-heavy fields. If you've undertaken highly quantitative research, write code, or analyse data, then you might consider a data science career.
Gain experience in analysing data through work experience and personal projects. Knowing a coding language will get you underway, with Python or R being good starting points. Having critical thinking skills or experience in working with large datasets are also very important. Develop and test out your skills via a Kaggle or hackathon online. Find out what others are doing - try to join a local Meet-up.
Whether further study is required really depends on the employer and job. Many do not require any formal qualifications, valuing hands-on experience instead. Others are looking for PhD level candidates with experience working with large datasets. Explore job adverts and employers to assess the qualifications required for the roles that interest you. Try out some Kaggle resources and Coursera courses in relevant subjects (for instance data science or machine learning) to test out your interest.
There are several data science master's qualifications (big data, business analytics, data analytics, data science) which can be useful for learning core skills and acquiring experience. There are many online certifications for data science as well. Make sure you research the course to make sure it meets your needs.
The first place to find jobs is through job advertisements on Handshake. Other sites with a focus on data science job listings include Data Elixir, DataScientistJobs, DevITjobs, and OnlyDataJobs.com. Jobs are also available on more general technology job websites, such as CWJobs, technojobs and LinkedIn.
Search for training opportunities at:
- Science to Data Science: a five-week full-time intensive workshop in London, aimed at helping those with a PhD move into data science
- Insight Data Science Programme: a six-week full-time, intensive course in Silicon Valley or New York City, aimed at academic PhDs and postdocs moving into data science. It includes a full-tuition scholarship, and you can be interviewed and hired by mentor companies immediately following completion of the program.
- Pivigo: a recruitment agency specialising in scientists moving from academia into data science.
- See free online courses such as the Coursera Introduction to Data Science
- For learning programming languages you can try Code Academy, which is free.
- IT and data science skills need to be front and centre of your application materials. Include your Github (online code repository) if you have one.
- Data science is a collaborative area, with many people sharing their methodologies and insights. Communication skills are important - present your ideas and solutions in a clear and helpful way.
- Research the organisation thoroughly - prove you understand what they do, what makes them unique, the challenges they are facing, and any new strategic directions.
- Prepare for any numerical, analytical or coding tests, and use any sample resources provided. The Careers Service subscribes to several leading test suppliers and as a current student or member of staff you can use them for free.
- The University has several engineering societies
- The Cambridge University Data Science Society (CUDSS) and other student societies sometimes host hackathons
- The Cambridge Centre of Data-Driven Discovery is aimed at researchers (including PhDs)
- Contact other graduates working in data science
- Network with employers at our Engineering, Science and Technology fair and at the niche Data Science event. Details of all these events can be found on Handshake.
There is no one entry point to becoming a data scientist. Opportunities range from structured data science graduate training schemes offered by large employers to direct-hire roles in small start-ups. Be open to exploring potential entry points.
Now you have looked at this page, think about your next steps. Everyone's journey is different. There are many ways to move forward. Here are some actions you could take now:
- Look for roles using the resources above.
- Talk to alumni working in this field via Handshake or LinkedIn.
- Talk to a Careers Consultant - book a 1:1 appointment through Handshake.
- Attend events - watch for relevant events.
- Ready to apply? Use the CV and cover letter guide to draft a CV or an application. CareerSet is a tool you can use to review your CV and cover letter. Write a speculative application.
- Subscribe to the Careers in Data Newsletter