Content, progress and pedagogy of the module

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?).

Content:

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, decentralised data

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

Learning objectives

Knowledge

Knowledge:

• 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 techniques.
• 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.

Skills

Skills:

• Install and use relevant software packages in data science.
• Read, import, export, and process data in most widely used data formats.
• Execute common data manipulation techniques such as data-merging, aggregation, pivoting, and treatment of missing values.
• 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.

Competences

Competencies:

• 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 problem-solving.
• 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.

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