AI, Machine Learning and Data Science

Artificial Intelligence (AI) is a broad field focused on creating machines that can perform tasks requiring human-like intelligence. Machine Learning (ML) is a specific method within AI that teaches machines to learn and make decisions by analysing data. Data Science is the field that combines statistics, programming, and domain knowledge to extract insights and make data-driven decisions from large and complex datasets. Be aware that AI, ML and Data Science are often used interchangeably by employers, so always read the details of the role that you are interested in before applying.

Careers in AI, ML and Data Science are diverse, offering opportunities in fields such as engineering, retail, tech, finance, healthcare, and more. Professionals in this area work on developing algorithms, building models, and analysing data to create intelligent systems that can perform tasks such as image recognition, natural language processing, and predictive analytics. Common roles include machine learning engineers, data visualisation analyst, AI researchers, data engineers, and NLP specialists, each requiring a strong foundation in programming, mathematics, and data analysis. These careers are highly in-demand on a global scale and often come with competitive salaries and opportunities to work on cutting-edge technology. Therefore, the need for skilled professionals in these areas continues to grow significantly.

Explore job roles

Many professionals in the field come from diverse academic backgrounds, including physics, engineering, statistics, and even social sciences, leveraging their analytical skills and supplementing them with AI and machine learning knowledge. If you have a degree in computer science, data science, or artificial intelligence, these qualifications can provide a strong foundation when applying for more specialised opportunities.

If you are pursuing a career in AI, ML or Data Science without a computer science degree, try to acquire the necessary skills through alternative paths like online courses, bootcamps, or self-study, and by gaining practical experience through projects, internships, or certifications in relevant areas such as data science, programming, and mathematics.

Use the job profiles below to find out about skills, entry routes and experience

Tip: Use the information about skills in the profiles to help you build a tailored CV.

Getting in and getting experience

To prepare for careers in Al, ML or Data Science, focus on building strong foundations in programming, mathematics, and data engineering / analysis. Also, consider pursuing internships or research projects to gain practical experience. It is essential to stay abreast of the latest developments in AI and networking with professionals in the field can open doors to international opportunities, where you can contribute to ground-breaking innovations and work in dynamic, cross-cultural environments.

Gain relevant experience and skills whilst you study

Use the job profiles above to check which skills are normally needed for the roles you are interested in.

Internships and Placements:

  • Secure internships or part-time roles with tech companies focused on AI, ML and Data Science.
  • Engage in Student Experience Internships (SEI) focusing on AI, ML and Data Science research projects under the guidance of academics.
  • Participate in 12-month industry placements as part of your degree.

Clubs and Societies:

Online Courses and Certifications:

  • Enrol in specialized AI and Machine Learning courses on platforms like Coursera, edX, FutureLearn or Udacity and obtain certifications to build and validate your technical skills.

Personal Projects:

  • Develop AI models or applications as part of your coursework or as personal projects in your free time, to build a strong portfolio.
  • Contribute to open-source AI projects on platforms like GitHub.

Networking:

  • Attend local AI meetups, conferences, and events in Manchester to network with industry professionals.
  • Engage with university alumni working in AI for mentorship and career advice.

Finding and applying for jobs

You can begin the process of finding and applying for AI, ML and Data Science jobs by exploring platforms such as LinkedIn, Indeed, and specialized tech sites such as Technojobs and CWJobs. Building and maintaining your network is important and attending industry events, conferences, and university career fairs to connect with potential employers can help expand your contacts in industry. Tailor your CV to emphasize relevant skills, including programming languages like Python and experience with AI tools like TensorFlow, Scikit-Learn, Pandas or Microsoft Azure ML/Google Cloud AI/AWS ML. Consider applying directly to AI-focused companies or consulting firms, and use CareerConnect to search for opportunities and seek application advice. Participating in internships, hackathons, and online AI communities can also open doors to job opportunities.

Find Vacancies

Next steps

When planning your next steps you may have additional questions or want to explore certain aspects in more detail: