Data Science for Digital Marketing

Why this course?

This course will teach you how to gain deeper marketing insights through the power of data science and enable you to become a better digital marketer.

Developed by the pioneering Data Science team at the University of Southampton – ranked among the top 100 universities globally – this course will teach you how to effectively use data science techniques to improve your marketing insights, better understand your customers, and manage customer interaction in web-based environments.

Taught by industry-leading experts in data science using a practical approach to learning data skills and real-life business examples, this 6-week flexible online course will enable you to interpret how your customers engage and interact with your digital marketing efforts – helping you to become a more effective digital marketer.

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

Week 1: Introduction to common web analytics tools

  • Googleverse
  • How people interact with sites
  • Setting goals
  • Basic site design – what Google wants
  • Google Analytics and search console
  • Facebook advertising

Week 2: Analytics for digital marketing

  • A/B testing
  • Reporting
  • Traffic and site journeys
  • Sources and conversations
  • Reporting dashboards
  • Integrating traffic with other business metrics

Week 3: Social network analysis

  • The role of social networks
  • Facebook advertising campaigns
  • Micro-segmentation
  • Graph theory
  • Tools for social media analytics
  • Content analysis methods

Week 4: Machine-learning

  • Definitions of data science
  • What is machine learning?
  • Different types of machine learning methods and models for marketers
  • Decision trees
  • Cluster analysis
  • How machine learning can be applied to website data
  • How to choose training data and evaluate the effectiveness of machine learning

Week 5: Application of machine learning in marketing

  • Using machine learning for attention, persuasion and retention
  • Your strategic role in applying machine learning to your work
  • Limitations of Artificial Intelligence (AI) and ethical considerations

Week 6: Big data and future applications

  • What is big data?
  • Volume, variety, velocity, and veracity dimensions and how they apply to marketing data sources
  • Cloud computing and distributed computing architectures
  • Data analysis in the cloud
  • Novel visualisations and applications
  • Future technological directions

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

  • Understand the role data science plays in digital marketing
  • Describe tools for web analytics and understand how customer journeys and sources can be tracked using these tools
  • Describe different ways in which social network analysis can be used to gain marketing insights
  • Apply content analysis methods to gain insights into your customer base
  • Explain what machine
  • learning is and what it can be used for
  • Understand what big data is and how it applies to your business problems
  • Identify what data needs to be collected to ensure your digital marketing process is optimised


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 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.


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.


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.

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.

Ready to boost your data literacy?

Take our flexible online course and learn how to leverage data to achieve your business goals.

Apply now