Data Science for Executives

  • Executive
  • Department of Management
  • Application code EE908
  • Starting 2020
  • Short course: Open
  • Location: Houghton Street, London

This course provides a comprehensive survey of the core vocabulary and methods of data science, data analytics, and machine learning.

Data Science participant video still_2

Virtual 4 week course starting 1 June 2020 (Application deadline 11pm Wed 27 May)

Due to the COVID-19 pandemic, we have made the decision to move the June delivery of this course from on-campus to virtual learning. It will still maintain the same outstanding content but delivered in a new format that enables our participants to learn at a distance. For more information, please visit our dedicated virtual delivery page and view the course timetable.

5 day intensive programme running 2 - 6 November 2020

Data science is a rapidly spreading field that combines statistical analysis, data management, computation, and substantive expertise, with the goal of improving decision-making in business, government, administration, law, and just about every other field.

One of the key challenges for decision-makers and managers is to understand what makes for good data science, and how the evidence from this field should be used in evaluation and decision-making.

The focus of this course is on examples of good and bad data science, with real-world applications from government, business, and law. By the end of the course, students will be familiar with the concepts of data science and will have learned how to evaluate quantitative evidence and how to design new studies using big data and data scientific tools.

On-campus tuition fee: £6,295 | Virtual course tuition fee: £4,295

Fees cover all LSE tuition and course materials. On campus fees also include daily lunches and networking events. You will also be awarded an LSE certificate of completion at the end of the five days.

Part of LSE Executive Education Courses

Programme details

Entry requirements

All LSE executive education participants are required to have:

  • Fluency in English.
  • A good undergraduate degree or significant work experience in a relevant role(s).
  • Minimum two years’ professional experience. Typically our participants have more than ten years’ work experience.
  • If you're a participant on our virtual course, please ensure that you have access to a laptop or computer, with a webcam and a working microphone.  You will also need a reliable internet connection.

Programme structure

Day 1: Data and Data Science

  • Introduction and overview of data science
  • The vocabulary of data, the structure of data and the types of data
  • Introduction to the tools used to record, structure, link and retrieve data
  • Databases and their role in building a business or organisational infrastructure

Day 2: Data Science Analysis and Data Regulation

  • Interpreting Data Science
  • Hypothesis tests, Type I and Type II errors, p-values, statistical significance, coefficients, regression analysis, model fit, and common association measures
  • Bayesian reasoning and probability
  • Data protection and the law
  • Data governance and ethical considerations

Day 3: Machine Learning and Artificial Intelligence

  • An overview of machine learning and how to interpret it
  • Practical guide to machine learning methods and their potential pitfalls and limitations
  • Supervised and supervised methods of machine learning
  • Business uses of data and AI
  • Recent developments in AI such as embeddings, neural networks and deep learning

Day 4: Collecting the interpreting survey data

  • How does survey data fit into other types of data
  • Translating managerial problems into a survey
  • Approaches in survey research
  • Implementing survey research
  • Items: Statements and scales
  • Pitfalls to avoid in survey research
  • Good and bad practices, e.g. the net promoter score

Day 5: Social Listening: Deriving actionable insights from social media and textual data

  • How new sources of data can be used to derive consumer behaviour insights
  • Social listening and machine learning
  • Advances in machine learning
  • Text mining and sentiment analysis
  • Course conclusions

Virtual course structure

Rather than an intensive five-day course, the virtual courses will be delivered over four weeks and you will be able to manage them alongside your working hours.

Week one will be a course orientation where participants can access your pre-course readings and meet fellow classmates, as well as a live welcome session hosted by our world-leading faculty.

Following that, your course will be taught over the course of three weeks with 10 contact hours each week, matching the level of contact time you would get on an on-campus course. Teaching will typically occur on weekdays during regular UK university hours. During this time, you'll have dedicated slots to meet your fellow participants, a virtual event per programme, full technical support by phone, email or live-chat and a 'virtual graduation' ceremony to end the course.

 View the course timetable.

Course outcomes

  • A comprehensive top-level understanding of the core concepts and methods of data science, including data management, data analysis, machine learning, and statistical learning.
  • The ability to evaluate evidence from statistical learning and data science, in order to make informed decisions.
  • A thorough awareness of the core issues in designing new data scientific studies.
  • Practical and applied knowledge of the core material through applications drawn from business, government, and law, including at least one presentation from a practitioner in one or more of these areas.

Faculty and guest speakers

This course will be taught by:

Ken Benoit is Professor of Computational Social Science at the Department of Methodology, LSE. His research focuses on automated, quantitative methods of processing large amounts of textual and other forms of big data – mainly political texts and social media – and the methodology of text mining. Find out more here

In February 2016 Professor Sabine Benoit (nee Moeller) joined the University of Surrey as a Professor of Marketing. She is a member of the Department of Retail and Marketing at Surrey Business School. Find out more here

Edgar is an Associate Professor (Reader) of Information Systems. Edgar has a BSc (Econ) and PhD in Information Systems, both from the LSE. He is the co-editor of Information Technology and People, Senior Editor for the Journal of Information Technology and the AIS Transactions of Replication Research and an Associate Editor for the Journal of the AIS. Find out more here


Muzz Adams, Head of Performance, Investec Asset Management

"Six months on from the course, it’s already had a big impact on my work. I’m speaking the language of a quantitative analyst and I can’t put a price on the confidence that’s given me in my conversations."

Read Muzz's full profile here.

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