This course provides a comprehensive survey of the core vocabulary and methods of data science, data analytics, and machine learning.
5 day intensive programme (date TBC)
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.
Find out both how to transform businesses with AI as well as the associated risks with this article 'AI and business: How what you put in shapes what you get out'
Read the article
Tuition fee: £6,295
Covers all tuition, course materials, daily lunches and networking events. You will receive an LSE certificate of completion at the end of the course.
Part of LSE Executive Education Courses
Find out more about the course:
Download course brochure
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.
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
- 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.
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.