Programmes

Data Science for Decision Making

  • Executive
  • Department of Management
  • Application code EE908
  • Starting 2019
  • Short course: Open
  • Location: London

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

5 day intensive programme running 10 - 14 June and 4 - 8 November 2019

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.

Tuition Fee: £5,995

Includes all LSE tuition, course materials, 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

Programme structure

Course outline is to be confirmed.

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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:

  • Professor Kenneth Benoit

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