Content, progress and pedagogy of the module

M1 intends to provide an opportunity to sample the core techniques of data science and machine learning, understand their intuition and application cases. It also aims at showing best practice of how to select specific and appropriate methods for the particular data science project, as well as how to efficiently and autonomously acquire further knowledge of the rapidly evolving field. Insights and techniques learned in this module can be applied to real-world problems in, e.g. marketing (How do you classify customers who are likely to spend a lot?), management (How do you identify performance bottlenecks in the organisation?) or finance (Is this person likely to default on their mortgage?).

Upon completion of the module students will have built a solid and expandable knowledge foundation in modern data science and will have acquired a broad range of skills enabling them to carry out own data analysis projects. More specifically the module will cover foundations of data manipulation, exploratory data analysis, supervised and unsupervised machine learning. Students will be capable of autonomously managing and evaluating complex projects and problems associated with data management, description, and analysis.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

• the main workflow routines and techniques how to obtain, store, manipulate, and analyse data using relevant software and foundational machine learning approaches.
• how to use a wide variety of techniques to explore, visualise, describe and present their data, including unsupervised and supervised machine learning techniques.
• the differences and complementarities between the prediction focussed data science approach, and the causality seeking approach of traditional scientific statistics.

Skills

The objective is that the student after the module possesses the necessary skills in:

• reading, importing, exporting, manipulating, cleaning and (pre)processing data in most widely used data formats using relevant software.
• selecting and applying standard techniques from 'traditional' statistics and data science to solve empirical problems of data exploration, classification, optimisation, and forecasting including model performance evaluation, fine-tuning and optimisation.
• understanding, interpreting, critically reflecting upon, and explaining the results of data analysis.

Competences

The objective is that the student after the module possesses the necessary competences in:

• comprehending and participating in current professional and academic discussions in applied statistics and data science as well as autonomosly expanding their knowledge in the field.
• critically reflecting possibilities and constraints related to the implementation and evolution of data-driven methods, including Identify problems which can be wholly or partially solved by the use of data analytics.
• describing and communicating the results of data analysis in a precise, understandable and informative manner, using appropriate data description and visualisation techniques.

Type of instruction

For information see § 17.

Exam

Prerequisite for enrollment for the exam

• A prerequisite for participating in the exam is that the student has participated actively in developing written material during the module.

Exams