Help organisations make strategic decisions
In the Data Analytics track, you learn how to interpret large amounts of (un)structured data and make predictions that help business and government shape their strategies and economic policies. You will unravel and interpret relations among the petabytes of complex data, generated every day by social media and the internet in general.
- Prepare for a career as a big data specialist in the economics and business environment.
- Acquire a deep insight into developments that are revolutionising today's economy.
- Become an expert in a new field that taps into the surge of consumer data.
Why choose the Data Analytics track?
- This track focuses on machine learning, microeconometrics and quantitative models, in addition to the 4 general courses of the MSc Econometrics.
- This track is designed with the help of leading big data companies. This way, we make sure that today’s data science developments connect with the econometrics curriculum.
- After graduation, you have an excellent job prospect at e.g. the research department of large public and private organisations.
Apart from the 4 general courses of the full programme, you will follow 5 track-specific courses and electives.
Machine Learning for Econometrics
In this course you will learn to understand machine learning theory and apply your newly gained knowledge on large real-life datasets in computer lab sessions. You will be using software in Python.
Mandatory electives, semester 1
Choose 1 out of 2 electives:
- Asset Pricing
- Complex Economic Dynamics
Mandatory electives, semester 2
Choose 1 elective in data science (5EC):
- Economic and Financial Network Analysis
- Machine Learning in Finance
- Quantitative Finance and Algorithmic Trading
- Advanced People Analytics
In this course you will discuss about 8 recent empirical papers that apply microeconometric estimation techniques. These papers usually concern issues like individual choice behaviour in the labour and consumer markets. You will apply the techniques during the computer lab sessions with MatLab or R.
Quantitative Models in Online Marketing
In this course you learn about quantitative models for automated online marketing with large amounts of data. The topics we cover are today’s hot topics in automated online marketing. We discuss how techniques from statistics, econometrics, machine learning and OR are used for these real-world topics.
One of the most essential problems in air traffic is delays of flights. They have important negative consequences in terms of costs, passenger demand, fares and airline reputation. Employ supervised machine learning techniques to predict the value of arrival delay in minutes. Combine historical flight details, weather conditions and aircraft characteristics of departing and arriving flights to define a unique set of features.
- Classification methods are used to classify observations belonging to one or another group. E.g. will a company go bankrupt? Will a visitor of a website buy a product?
- Artificial Networks are methods that mimic processes in the brain. In the end the brain is used to make decisions. The idea is to build a network (of neurons) that can be used to analyse problems.
- Decision trees simplify a choice process by modelling in dichotomous steps.
- Random forests are a random collection of decision trees that improve the quality of the predictions.