Content, progress and pedagogy of the
Aim: M1 intends to provide an opportunity to
sample the core techniques of data science, 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?).
This module is an introduction to the main ideas behind (social)
data science, and the essential principles and techniques in the
data scientist's toolbox. It aims at providing a broad overview
by taking a "bird's eye perspective" and presenting a
range of topics briefly instead of focusing on a single topic in
depth. The Introduction to Social Data Science will survey the
foundational issues in data science, namely:
- Data Sourcing: Where and how to get the right data
- Data Manipulation
- Data Analysis with Statistics and Machine Learning
- Data Communication with Information Visualization
- Data at Scale - Working with Big Data
- Data at Scope - Working with non-traditional data-sources such
as text, geographical data, relational data, and more
- Data at Mess - Working with incomplete, ill-structured,
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. Students will be capable of
autonomously managing and evaluating complex projects and problems
associated with data management, description, and analysis
- Understand and explain the main workflow routines and
techniques how to obtain, store, manipulate, and analyse data.
- Identify the commonly used programming languages, software and
other tools used in data science.
- Explain how to select and execute the most common data analysis
- Show an understanding of how to use a wide variety of
visualisation techniques to explore and describe their data.
- Explain the differences and complementarities between the
prediction focussed data science approach, and the causality
seeking approach of traditional scientific statistics.
- Provide an overview over the current state-of-the-art in
applied statistics and data science.
- Install and use relevant software packages in data
- Read, import, export, and process data in most widely used data
- Execute common data manipulation techniques such as
data-merging, aggregation, pivoting, and treatment of missing
- Select and apply standard techniques from 'traditional'
statistics and data science to solve empirical problems of data
exploration, classification, optimisation, and forecasting.
- Evaluate model performance, fine-tune and optimize models.
- Understand, interpret, critically reflect upon, and explain the
results of data analysis.
- Comprehend and participate in current professional and academic
discussions in applied statistics and data science.
- Critically reflect possibilities and constraints related to the
implementation and evolution of data-driven methods.
- Identify problems which can be wholly or partially solved by
the use of data analytics.
- Apply a data-driven logic, structure, and workflow to
- Describe and communicate the results of data analysis in a
precise, understandable and informative manner, using appropriate
data description and visualisation techniques.
- Expand their knowledge in various data science topics of
interest and relevance via self-learning.
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.
|Name of exam||M1: Applied Data Science and Machine Learning|
|Type of exam|
Group examination with max. 6 students.
|Assessment||7-point grading scale|
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination
Policies and Procedures|