Develop your data science and analytics skills and improve your understanding of using data in the workplace.

3,881 enrolled on this course

  • Duration

    2 weeks
  • Weekly study

    6 hours

Have the opportunity to build data science skills to advance your career

On this course, you’ll be introduced to the tools and techniques used in applied data science.

You’ll explore the answers to important questions asked of business professionals today, such as why data science and machine learning have become so prevalent and what problems data science can address.

You’ll have the opportunity to improve your understanding of the fundamental aspects of applied data science methods and learn how to apply your newly acquired knowledge for the benefit of your organisation.

What topics will you cover?

  • The terminology used in the data science sector
  • Curve fitting and plotting of statistical distributions
  • Visualisation techniques
  • Making use of geolocation data
  • Classification and clustering
  • Feature extraction
  • Clustering text
  • Supervised and unsupervised learning
  • Application of machine learning to text and images

When would you like to start?

  • Date to be announced

Add to Wishlist to be emailed when new dates are announced

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Describe the principle methods of data cleaning and preparation, including working with unstructured text data
  • Identify different features in a distribution and the resulting meaning
  • Compare different visualisation techniques
  • Explore and interpret visualised data
  • Modify underlying Python code to change the look of a visualisation
  • Explore the use of multidimensional data using the example of geolocation
  • Identify patterns in data using clustering and classification techniques
  • Discuss the ethical considerations in the use of data for data science
  • Explore the use of data transformations involving different types of data and representation: text to vectors, n-grams, tagging data
  • Explain the purpose of distance metrics in high-dimensional data
  • Apply clustering to high-dimensional data
  • Explore how you can extract data from social repositories
  • Compare and contrast supervised and unsupervised models
  • Describe the core concepts behind neural networks and their applications
  • Compare the advantages and disadvantages of NN over other classification methods
  • Evaluate the results of machine learning algorithms based on simple techniques

Who is the course for?

This course is for anyone who’d like to learn more about data science.

The course will be useful for students, novice programmers, and any professionals who interact with data.

For professionals or students who are new to the subject, the course will provide a foundation for advancing your career using data science and data analytics skills.

For those already working in the IT industry, you’ll have the opportunity to strengthen and develop your knowledge and expertise in the area of data analytics and, more generally, data science.

There are no programming prerequisites for students taking this module, but you should have a basic understanding of mathematical thinking and elementary statistics.

Please note that the individuals detailed in the ‘Who will you learn with?’ section below, are current staff members and may be subject to change.

What software or tools do you need?

Python, Jupyter Notebook, Pandas, Samtla API, Google Colab

Who will you learn with?

Martyn is a Post-doc researcher and IoC Project Manager at Birkbeck University. His research focuses on developing approaches in Natural Language Processing, Information Retrieval, and text mining.

I am a Professor of Computer Science at Birkbeck University of London. My expertise lies in Data Science, Search Engine Technology, Applied Machine Learning and Computational Social Science.

Stelios is a computer scientist in the area of computing systems, working with algorithms that improve the performance of large scale systems.

Felix is a lecturer for Computer Science at Birkbeck University of London. His research is focused on structural properties of graphs and networks which can be leveraged to design faster algorithms.

Who developed the course?

Coventry University

Coventry secured 5 QS Stars for Teaching and Online Learning in the QS World University Ranking 2020 and has received No. 1 in the world for Massive Open Online Courses in MoocLab’s World University Ranking 2021.

  • Established

  • Location


Institute of Coding

The Institute of Coding is a family of learners, businesses and educators creating a new way to develop the digital skills needed at work and beyond.

Birkbeck, University of London

The Department of Computer Science and Information Systems is a leading centre of computing expertise, specialising in information and knowledge management, web and pervasive technologies, computational intelligence and information systems development.

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps to help you keep track of your learning
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

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