Sectors: Tech: Data, Machine Learning and Artificial Intelligence

AI Ecosystem

The tech sector is a rapidly growing and changing sector, with opportunities spanning deeply technical and non-technical roles alike. Technology has an impact across all sectors but here we explore organisations and opportunities that focus on providing services or solutions in tech.

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. Check out the job profile of a data scientist.

Machine learning and AI are applicable in almost every sector. · 

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To work out the best starting place for you, consider these questions:

Programming or not? Whether you already know how to code or want to start learning, there are many opportunities if you’re interested in software development. You’ll often be taught on the job: either building on coding experience you already have or teaching you from scratch. If you go down this route, make sure you enjoy detail, problem solving, and working independently. Read our blog by a former student. It offers comprehensive advice on courses and how he built up his coding knowledge. 

3 Things I Wish I Knew about Careers – unicamcareers blog

Application-focused or user-focused? Tech roles range from the more technical development of applications to more user-centred activity such as developing user interfaces and user experiences, front-end web development, and technical writing. Are you more interested in making systems and products, or focusing on how they are used?

What future roles attract you most? Career progression in tech tends to follow one of two routes: the technical specialist/architect/guru route, or the managerial/project management route with responsibility for systems and people. It is worth giving some thought to which path attracts you most. 

Hear more from The Accidental Technical Architect: Veronika on her squiggly career – unicamcareers blog 

View our full blog series to gain valuable insights across various roles.

Not a problem! Employers are increasingly willing to train candidates regardless of their background. Be sure to demonstrate both general aptitude (particularly for technical roles) and enthusiasm for the sector.

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. 

Read about John’s journey from a PhD to data Careers in Data

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.

Internships are a great opportunity to gain hands on experience, build professional connections, apply academic learning to real-life projects and discover your strengths and interests. Search for employers offering internship opportunities on Handshake. You can hear more about the benefits of an internship from Callum, who undertook a six week software internship at local Start-up Cambridge Kinetics. Life as an Intern at Cambridge Kinetics – unicamcareers blog.

Whether further study is required really depends on the employer and job. Many do not require any formal qualifications, valuing hands-on experience instead. Explore job adverts and employers to assess the qualifications required for the roles that interest you.

If you can demonstrate a proficiency in coding, sometimes no further study or certification is required. Your expertise in technical skills may be tested as part of the interview process for a job or internship. Check out our guide to succeeding in a technical interview.

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.

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. Try out some Kaggle resources and Coursera courses in relevant subjects (for instance data science or machine learning) to test your skill level. Some organisations hire PhDs who have used data science and machine learning skills in their research directly without formal training.

Generally, you'll require at least one of two things to get started in this industry: enthusiasm for technology and a portfolio (on Github, for instance) of relevant work to demonstrate your skills. Be prepared to talk about the problems you encountered in your projects, and how you overcame them. Your CV should have a prominent section outlining your technical skills.

  • 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.

Keep an eye out for competitions and hackathons that you can use to build up your coding skills. Opportunities to participate in hackathons and competitions are compiled by the Careers Service via our Scholarships & Competitions – blog.

Other hackathons and competitions are often sponsored by student societies, some relevant societies of which are listed below: 

The Cambridge Centre of Data-Driven Discovery is aimed at researchers (including PhDs)

How can the Careers Service help me?  

Further Study or Certification Required:

You don’t need formal qualifications to start coding – anyone can learn and build real projects without a further degree. Depending on your end goal(s) however, different qualifications can help.

To Start Coding

To start coding, the most important skills are problem solving and logic, while the most important attributes are patience and curiosity. Training providers include: Codeacademy, freeCodeCamp, CoderDojo and there are also online courses on Coursera and university MOOC platforms, including Harvard’s Introduction to Computer Science which is free and a very good starting point. The University of Cambridge offers its own Programming Concepts: Introduction for Absolute Beginners

Build - and show - a project on GitHub 

Once you build coding experience and create your own projects, GitHub allows you to publicly showcase these. Though not absolutely necessary, showcasing your work can help you show real code, how you structure projects and document your work. It can also build trust and transparency with future employers.

The role of coding bootcamps

Coding bootcamps are intensive short-term programmes designed to teach participants practical programming as well as applications in specific areas e.g. web development, cybersecurity etc. Bootcamps focus on real-world application of skills and are often team-based. Bootcamps can be added on to your CV to show your experience. Bootcamps are run across the University of Cambridge and beyond.

Intermediate level coding & beyond

If you wish to develop existing coding skills or to target specialisms e.g. building an app, getting a role in tech, game development or cloud computing, for example, then more specific skills and learning platforms will be beneficial. LeetCode and HackerRank, for example, can help you prepare for software interviews in Big Tech, while Google’s cloud certifications and those of AWS can help you achieve credentials for careers in cloud computing.

UIS Training

UIS provides 100+ courses across computing – from basics to advanced. The full list of courses available to you are listed here, while a sample of courses are included below:

AI Policy & Ethics 

Cyber security 

Data Analytics 

Data Science 

AI & Machine Learning 

Quant Finance & research 

User Experience / User Research / User Design 

Women in Tech

General Recruiting websites for Tech 

General Learning Platforms for Tech & Data

Google Career Certificates 

Coursera 

Learning signposts | IT Help and Support

LinkedIn Learning 

Prospects – roles across Information & Technology