Course Overview
Timberlake's Data Science and Econometric MSc programme is built to be flexible. It allows you to tailor your learning to your specific interests, learning online around your career. Best-in-field experts lead all specialist courses for unrivalled quality.
​
There are 6 core modules and 10 electives to choose from. You can join us for as little as one module, or build up to a full Masters of Science (MSc).
​
Core Modules
Dr Arthur Charpentier & Prof. Russell Davidson
This course offers a first overview of data science techniques at the graduate level. It takes participants through basic data types and properties, supervised learning techniques, and unsupervised learning methods. The course also addresses prediction and classification techniques using parametric, non-parametric and ensemble methods.
20 Credits
_edited_edited.jpg)
The purpose of this course is to provide students with an introduction to
statistics and econometrics, to successfully study a complete course in econometrics. In addition, the
course is designed to enable students to analyse the classical linear regression model, its statistical
foundations, and its various estimation techniques.
.jpeg)
20 Credits
Prof Badih Ghatthas
Understand the fundamental concepts of time series econometrics and forecasting. Gain the practical skills to use econometric software to model and forecast economic time series and identify models with the best forecasting abilities.
​
​
20 Credits
.png)
Gain an in-depth understanding of panel data econometrics, presented from a microeconometrics perspective. The course will cover linear panel data models with unobserved heterogeneity, including discussions of the strengths and weakness of the various estimation methods.
20 Credits
Regis Amichia & Thomas Pical
The aim of this course is to provide students with programming skills that can be used to perform data management operations and several types of statistical and econometric analysis in an efficient and reproducible way.
10 Credits
Understand the fundamental concepts of time series econometrics and forecasting. Gain the practical skills to use econometric software to model and forecast economic time series and identify models with the best forecasting abilities.
​
​
20 Credits
Electives
The purpose of this course is to provide students with an introduction to automatic model selection and its limitations and uses in practical econometric modelling.
10 Credits
Victor Levallois
This course gives an introduction to data visualisation. The course highlights key aspects of data visualisation techniques and best practices. Further, it covers data import and preparation, creating visualisations, and publishing reports.
10 Credits
Woody Pan & Thomas Pical
This course provides an introduction to SQL and NoSQL databases. The course clarifies differences between SQL and NoSQL databases and covers the principles of database design and management, including data modelling, normalisation, and indexing. Further, it introduces popular SQL and NoSQL databases, such as MySQL, PostgreSQL, and MongoDB.
10 Credits
The course covers the estimation and usage of discrete choice models that are increasingly estimated using simulation methods. Discrete choice models are used to examine the choices of individual consumers, households, firms and other agents.
10 Credits
Regis Amichia & Thomas Pical
This course is an introduction to web scraping. It covers basic techniques of web scraping, reviews common libraries and frameworks for web scraping in Python, extraction from HTML and XML, and discusses more advanced techniques. This course also covers the basics of data engineering, including data ingestion, cleaning, and transformation, as well as data storage and retrieval.
10 Credits
TBC
This course introduces cloud computing and MLOps. It covers the basics of cloud computing from a user point-of-view as well as the principles and practices of MLOps, including model training, deployment, and monitoring. The focus is on giving practical knowledge to participants which gives them the right tools to deploy, test and maintain data science models at scale and structure data science projects correctly.
10 Credits
Prof Emmanuel Flachaire
This course is an introduction to the principles and practices of causal machine learning. It covers the basics of causal inference, including the identification and estimation of causal effects, as well as the applications of causal machine learning in areas such as health, finance, and social sciences. The course also includes an introduction to common methods and algorithms for causal machine learning, such as instrumental variables, synthetic controls, AB-testing and propensity scores.
10 Credits
Prof Wouter Verbeke & Dr Guillaume Coqueret
The aim of this course is to present several concrete applications of ML techniques in Economics and Finance.
​
10 Credits
Regis Amichia & Thomas Pical
This course covers best practices in Python programming. It focuses on structuring code, commenting code, using versioning tools like Github, and setting up data science projects in Python which allow for effective collaboration and maintenance of code while working with other coders in large projects.
10 Credits
The purpose of this course is to introduce students to the theory and practice of applying natural language processing (NLP) in economics and business. We cover all steps in the data science pipeline of transforming textual data into numbers that are relevant for decision making.
10 Credits