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Fundamentals of Data Science (Technical)

This course will equip you with the specialist knowledge and technical skills you need to effectively apply the powerful tools and insights of data science to your work.

Key benefits:

Developed by the pioneering Data Science team at the University of Southampton – ranked among the top 100 universities globally – this course is designed to introduce you to the theoretical components of data science and give you hands-on experience in producing solutions to data science problems using Python.

Taught by industry-leading experts in data science using a practical approach to learning data skills, this 6-week flexible online course will see you extend your knowledge and expertise across data mining, machine-learning, and data visualisation to become a highly-skilled and effective data scientist.

Extra tuition support with Python

If your technical knowledge of Python or statistics is minimal and you’d like extra support before commencing the course, we can offer an additional pack of 5 intensive 1-hour tutorials.

These supplementary tutorials will help you work through any technical issues you may encounter on the course, and enable you to rapidly gain the confidence you need to use Python for data science.

Hear about students' experience on the course:

The course runs over 6 weeks and is broken down into manageable weekly topics:

Week 1: Welcome and course information

  • Welcome and introduction to the course
  • What data science is and why it’s important
  • A ‘hands-on’ Jupyter familiarisation activity
  • Python Primer
  • Glossary of terminology

Week 2: Introduction to core concepts and technologies

  • The data science process
  • A data science toolkit
  • Types of data and example applications

Week 3: Data collection and management

  • Sources of data
  • Data collection and APIs
  • Exploring and fixing data
  • Data storage and management
  • Using multiple data sources

Week 4: Data analysis

  • Introduction to statistics
  • Basic machine-learning algorithms

Week 5: Data visualisation

  • Types of data visualisation
  • Data for visualisation
  • Technologies for visualisation

Week 6: Future of data science

  • An exploration of the future of data science

After successfully completing the course, you’ll be able to:

  • Understand key concepts in data science and their real-world applications
  • Explain how data is collected, managed and stored in the context of data science
  • Implement data collection and management scripts using MongoDB
  • Demonstrate an understanding of statistics and machine-learning concepts vital for data science
  • Produce Python code to statistically analyse a dataset
  • Plan and generate visualisations from data using tools such as Python and Bokeh
  • Work effectively with live data and utilise the opportunities presented by cloud services

Professor

Les Carr

Les is Professor of Web Science at the University of Southampton’s School of Electronics and Computer Science. He’s also a Director of the Web Science Institute and former Director of the Web Science Centre for Doctoral Training. His research on Open Access and Open Data led to the establishment of Eprints – offering Open Access publication and data services, training and support to the research industry.

Professor

Elena Simperl

Elena is a Professor of Computer Science within the Web and Internet Science research group – part of the University of Southampton’s School of Electronics and Computer Science. Her primary research domain concerns the intersection between knowledge technologies and crowd computing. Elena is interested in socially and economically-motivated aspects of creating and using semantically-enabled content on the Web, and in paradigms, methods and techniques to facilitate large-scale collaboration and incentivise participation.

Doctor

Rob Blair

Rob is Senior Teaching Fellow at the University of Southampton’s School of Electronics and Computer Science. He holds an MSc Information Systems from the University of East Anglia and an MSc Web Science from the University of Southampton. Qualified to teach Physics, Mathematics and Computer Science, Rob is a highly-experienced classroom teacher and online tutor specialising in data science.


David Millard

Dave is Associate Professor of Computer and Web Science at the University of Southampton, David is a founding member of the Web and Internet Science research group within the School of Electronics and Computer Science (ECS). He represents ECS on the steering group for the Web Science Centre for Doctoral Training. David is currently Vice-Chair for ACM SIGWEB.

Dr

Manuel León Urrutia

Dr. Manuel León Urrutia is Research Fellow in the Web and Internet Science research group of the Electronics and Computer Science department of the University of Southampton. Manuel is specialised in learning technologies, with experience in learning design and research interests in MOOCs and learning analytics. Prior to joining the Computer Science department in 2012, he worked for 6 years as an editor in a publishing company, and 6 more years as a language teacher in the University of Southampton. Manuel currently has an academic, senior tutoring and learning design role in the Southampton Data Science Academy.


Gary Wills

Gary is an Associate Professor in Computer Science at the University of Southampton. He graduated from the University of Southampton with an Honours degree in Electromechanical Engineering, followed by a PhD in Industrial Hypermedia Systems. Gary is a Chartered Engineer, a member of the Institute of Engineering Technology, and a Principal Fellow of the Higher Educational Academy. He is also a visiting Associate Professor at University of Cape Town and a research professor at RLabs.

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