Big Data Analytics

About the course

Subjects

Statistics Quantitative methods Methodology IT

Course content

This course equips students with knowledge of key concepts, methods, techniques, and tools of big data analytics from a business and societal perspective. The course covers how to find interesting real-world datasets and how to manipulate, transform, analyze, visualize and report big data in order to create business value. The course focuses on using big data for predictive rather than explanatory modeling purposes with the goal of improving organizational decision-making and (automated) allocation of resources. Course topics are listed below:

Big Data Foundations: data science concepts, organizational opportunities, and evolving legal and ethical challenges of big data
Data: Data types and tabular formats, observational vs. experimental data, and data cleaning and pre-processing pipelines
Machine Learning: statistical fundamentals of AI and machine learning with a focus on interpretable supervised machine learning methods
Visual Analytics: data visualization techniques for exploration and reporting
Text Analytics: document classification
Time series forecasting: regression-based forecasting

The course uses R and R Studio for all activities. This software is available for free.

See course description in course catalogue

What you will learn

To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:

Characterize the phenomena of big data analytics and predictive modelling and apply relevant concepts, methods, and tools for analysing big data in real-world organizational/societal contexts.
Understand the linkages between business intelligence/analytics and the potential costs and benefits for organisations and relevant stakeholders.
Formulate a compelling and realistic use case for the applicability of various analytical techniques, evaluation methods, and algorithms on big/organizational/social/open datasets to improve decision-making and resource allocation.
Concisely present and evaluate the findings of a big data analytics project in terms of stated project goals and reflect on the project solution's potential impact on organisations and relevant stakeholders.
Create and accurately interpret relevant and useful data visualizations for predictive modelling contexts.
Display nuanced, critical awareness of the impact of data science project pipeline choices and employ writing skills appropriate for academic standards in the written report.

Course prerequisites

This course is a part of the minor in Data in Business. The course takes a practical and hands-on approach to data science in organizational and societal settings. It may not be suitable for students interested in the mathematical foundations of predictive modelling. Moreover, this is a fast-paced and intensive course comprising visual, predictive, and text analytics modules. Students are expected to have some prior background in quantitative methods and basic statistics to follow the analytical techniques and algorithms taught in the course. Students without prior R coding experience may need supplemental practice to get up to speed.

Facts

Level

Master

Type

Elective

ECTS

7.5

Code

CDSCV1900U

Teaching periods

Autumn – semester

Academic year

2026/2027

Status

Available places

Teaching language

English

Teaching Format

Hybrid

Exams

Written assignment and oral exam on campus
Group exam, winter

Assessment type

7 point grading scale