Introduction to Data Science

Content:

Data science is a rapidly developing field with numerous application areas. In this course you will learn basic tools of data science. You will also become familiar with advanced methods involving deep learning and their practical applications. In the first part of the course you will get an introduction to fundamental statistical methods underpinning data science. You will also learn techniques for analyzing and visualizing datasets of different modalities like text, images and tabular. You will dive deep into data-driven prediction methods from machine learning and deep learning. In the final parts of the course we will introduce you to advanced topics, including recent progress in large language modelling and use of data-driven decision making in a trustworthy manner.

Learning Outcomes:

At the end of this course, you would be familiar with:

  1. Key contemporary methods for data-driven prediction
  2. Methods for processing, exploring and visualizing data of different modalities like image, text and tabular
  3. Building proof-of-concept code bases for solving real-world data science problems
  4. Issues around trust and potential remedies in applications of data science
Learning Methods:

Each session of the course consists of a lecture part, introducing the theoretical concepts, and a practical part, providing you with hands-on experience using Jupyter Notebooks.

Exam:

(1) Excercises during the semester and (2) written final module examination of 120 minutes

Both parts must be passed with at least 50%. The final grade is calculated as follows: 30% excercises and 70% written exam. There will be an excercise about every two weeks and a tutorial where the solutions are discussed. 

Lecturers

Details

Course type
Lectures
Credits
5
Term
Winter Term 2025/2026
E-Learning
moodle course available

Dates

Lecture
Takes place every week on Monday from 08:00 to 12:00 in room MC 1/30 + 31.
First appointment is on 13.10.2025
Last appointment is on 02.02.2026

Requirements

Basic knowledge of calculus, linear algebra and programming in Python

About the INI

The Institut für Neuroinformatik (INI) is a research unit of the Faculty of Computer Science at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de