Data Science: An Introduction



Overview

Data is transforming the way we live, work, and make decisions. This introductory course provides a comprehensive foundation in data science for those looking to deepen their understanding of this rapidly evolving field. Designed for professionals, lifelong learners, and self-taught enthusiasts, this course will equip you with the essential tools and insights to navigate today’s data-driven world.

Across ten sessions, you’ll explore how data is collected, organised, and analysed; learn how artificial intelligence—from neural networks to large language models—is reshaping industries; and examine the ethical and societal impacts of these technologies. With real-world case studies and hands-on project work, you’ll gain practical experience while engaging with cutting-edge topics in health discovery and prediction.

No prior experience in coding or mathematics is required.

This course combines online study with a weekly 1-hour live webinar led by your tutor. Find out more about how our short online courses are taught.

Programme details

This course begins on the 14 April 2026 which is when course materials are made available to students. Students should study these materials in advance of the first live meeting which will be held on 21 April 2026 (UK time).

  1. An avalanche of data
  2. Organizing data
  3. Asking a lot of data
  4. Artificial Intelligence: the big picture
  5. Neural networks: the artificial brain
  6. Language, intelligence and LLMs
  7. Talking to machines
  8. Data science and black swans
  9. The science of data science
  10. Retooling for the age of AI

Certification

Credit Application Transfer Scheme (CATS) points 

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework. All those enrolled on an online course are registered for credit and will be awarded CATS points for completing work at the required standard.

See more information on CATS points

Digital credentials

All students who pass their final assignment will be eligible for a digital Certificate of Completion. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so. 

Please note that assignments are not graded but are marked either pass or fail. 

Fees

Description Costs
Course Fee £360.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:

Concessionary fees for short courses

Tutor

Prof Daniel Wilson - Tutor

Professor Daniel Wilson is Director of Studies in Data Science at the Department for Continuing Education. Originally trained in biology and statistics, Daniel has conducted research in Oxford, Chicago, Lancaster, London, Paris and Mombassa. His work focuses on genetics and infectious diseases, analysing large datasets and developing new computational tools. He has led research studies into genetic risk factors for disease, the genetics of antibiotic resistance, the evolution of microbes, and the use of DNA to track the spread of outbreaks.

Course aims

This course aims to:

  • Provide a foundational understanding of data science, from data collection to analysis and interpretation
  • Explore real-world applications of data science, including health and societal impact
  • Develop confidence to engage with data-driven tools and concepts, with no prior coding or maths required

Teaching methods

This course takes place over 10 weeks, with a weekly learning schedule and weekly live webinar held on Microsoft Teams. Shortly before a course commences, students are provided with access to GitHub Codespaces, an online virtual learning environment, which houses the course content, including video lectures, complemented by readings or other study materials. Any standard web browser can be used to access these materials, but we recommend Google Chrome. Working through these materials over the course of the week will prepare students for a weekly 1-hour live webinar you will share with your expert tutor and fellow students. All courses are structured to amount to 100 study hours, so that on average, you should set aside 10 hours a week for study. Although the course finishes after 10 weeks, all learning materials remain available to all students for 12 months after the course has finished.

 

GitHub provides a complimentary monthly allowance for Codespaces (currently 150 hours of compute and 20 GB of storage). The recommended 10 hours of study per week fits comfortably within this allowance. If you choose to use additional compute or storage beyond the included quota, standard GitHub rates apply (currently $0.18 per compute hour and $0.07 per GB of storage).

 

All courses are led by an expert tutor. Tutors guide students through the course materials as part of the live interactions during the weekly webinars. Tutors will also provide individualised feedback on your assignments. All online courses are taught in small student cohorts so that you and your peers will form a mutually supportive and vibrant learning community for the duration of the course. You will learn from your fellow students as well as from your tutor, and they will learn from you.

Learning outcomes

By the end of the course students will be expected to:

  1. Describe key concepts in data science and artificial intelligence, including how data is structured, processed, and applied in real-world contexts
  2. Perform and interpret simple data analyses using accessible tools, drawing conclusions from patterns and trends in the data
  3. Evaluate and communicate the societal and ethical implications of data science applications, using appropriate examples and accessible language

Assessment methods

You will design, execute and report on a personal project, submitting two pieces of written work during the course. A project plan of 500 words (or equivalent) is due in the first half of the course. The writing does not count towards your final mark, but the plan, and tutor feedback on the plan, will guide the execution of a project on which your final written work is based. A project report of 1,500 words (or equivalent) is due at the end of the course, which will be marked pass or fail.

Application

Please use the 'Book now' button on this page. Alternatively, please complete an enrolment form.

 

Level and demands

This course is open to all and no prior knowledge is required.

This course is offered at FHEQ Level 4 (i.e. first year undergraduate level), and you will be expected to engage in independent study in preparation for your assignments and for the weekly webinar. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class. Our 10-week Short Online Courses come with an expected total commitment of 100 study hours, including those spent in live webinars.

English Language Requirements

We do not insist that applicants hold an English language certification, but warn that they may be at a disadvantage if their language skills are not of a comparable level to those qualifications listed on our website. If you are confident in your proficiency, please feel free to enrol. For more information regarding English language requirements please follow this link: https://www.conted.ox.ac.uk/about/english-language-requirements

IT requirements

Any standard web browser can be used to access course materials on our virtual learning environment, but we recommend Google Chrome. We also recommend that students join the live webinars on Microsoft Teams using a laptop or desktop computer rather than a phone or tablet due to the limited functionality of the app on these devices.

Terms & conditions for applicants and students

Information on financial support