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#IT-04-29 Get ready for a Masters in Data Science and AI

  • Categories IT
  • Duration 14h
  • Total Enrolled 7
  • Last Update January 15, 2021

About Course

Coventry University


Get a taste of life as a Data Science and AI Master’s student

On this course, you’ll have the opportunity to explore the disciplines involved in a Master’s degree in Data Science and Artificial Intelligence (AI).

You’ll be asked to work through the fundamentals of coding in Python, and be encouraged to discover other important elements of the field, like mathematics, statistics, and data science thinking.

You’ll be asked to produce your own piece of data science output, get introduced to data ethics, and be taught about the work of data scientists.

This course should help you to assess your understanding of data science and the surrounding topics and should help you to make an informed decision around choosing a Master’s in Data Science and Artificial Intelligence.

Usually courses are 6 hours per week. Based on the results of your self-assessment we advise you to choose the content most appropriate for you. But you are also welcome to complete the whole course which would be a total of 20 hours.

What topics will you cover?

  • Programming
  • Mathematics
  • Data fundamentals
  • Statistical thinking
  • Privacy and ethics
  • Context and environments

Who will you learn with?

Mark Johnston

Mark is an Assistant Professor in the School of Computing, Electronics and Mathematics at Coventry University in the UK.



Paul Matthews

Paul is Senior Lecturer in Information and Data Science in the
Department of Computer Science and Creative Technologies at UWE Bristol in the UK



James Davenport

Professor of Information Technology at the University of Bath, in both Mathematical Sciences and Computer Science. Is on ISO/IEC JTC 1/SC 42/WG 3: Trustworthiness of Artificial Intelligence.


William Sayers

Senior lecturer at the University of Gloucestershire. Researcher in artificial intelligence techniques applied to optimisation problems, and an interest in Data Science, AI & optimisation.



Daniel McCluskey

Daniel is a Research Assistant for the Creative Computing school at the University of Gloucestershire, where he writes course material for Artificial Intelligence and Programming courses.


Michael Tautschnig

Michael is a Lecturer at Queen Mary University of London, UK and Senior Security Engineer at Amazon Web Services. In his research, Michael focuses on automated software verification.



Who developed the course?

Coventry University logo

Coventry secured gold in the UK Government’s 2017 Teaching Excellence Framework (TEF) and is University of the Year for Student Experience in The Times & The Sunday Times Good University Guide 2019.

What Will I Learn?

  • Plan and prioritise options for study in order to enter the data science field
  • Explore the various elements of data science, including mathematics and Python programming
  • Enter further study with confidence
  • Produce a data science output

Topics for this course

24 Lessons14h


Welcome to the start of this course in Data Science and Artificial Intelligence.
The big question
Welcome to the course00:03:11
Getting the most out of your learning
A day in the life00:03:44
Studying at master’s level
What you will need for this course

Asking questions?

The basics of data science and how we, as humans, talk to the computer.

Designing a program?

Before you write code, you need to design a program.

Writing code?

Writing code for your program using Python.

Accessing your data?

Where and how is data stored?

Student Feedback


Total 4 Ratings

3 ratings
1 rating
0 rating
0 rating
0 rating

good course but I would probably prefer it to be a bit longer with more python work

The course is thorough in the basic foundations in Data Science which was pleasing to see, I am looking forward towards the content of the Artificial Intelligence they put out.

I enjoyed reading the content, articles and also doing python codes. There were plenty of examples. Nice work

In-depth overview of data science: discoveries of the possibilities of Python, criteria for taking into account data, decision elements on the analysis process.very good course


Material Includes

  • Official Certificate


  • We will be using Jupyter Notebook to learn Python. There are many ways to try Jupyter Notebook, including Google Colab, Try Jupyter classic notebook or JupyterLab, or install the Anaconda Individual Edition.

Target Audience

  • This course is designed for prospective Master’s students looking to enter the Data Science field who would like to learn more about the disciplines involved. This course is also for students who have an interest in Data Science and AI.
  • The course has been developed by a collaborative partnership of leading Higher Education Institutions led by Coventry University.