Foundations of Data Literacy and Data Science

INFO: This course will next take place in SoSe25. Please contact l.musiolek@tu-berlin.de with any questions.

Instructor: Hamid Mostofi-Darbani, Lea Musiolek, Fatma Deniz

• Overview
• Content
• Learning Goals
• Components
• Exam

Overview

Content

This course integrates three key perspectives: inferential thinking, computational thinking, and practical application. It explores methods for analyzing data to gain insights into the underlying processes. Students will learn essential skills in programming and statistical inference while engaging in hands-on analysis of real-world datasets, including indicators, document repositories, geographic data, and social networks.

Learning Goals

• Understanding the basic concepts of data, including data types and data structures, and real-world data sources
• Learning concepts and skills in scientific programming and statistical inference using Python
• Identifying and retrieving relevant information from data, analyzing and interpreting it using statistical methods and visualization techniques, critically evaluating results

Components

The course consists of the weekly main lecture (Vorlesung), the weekly lab (Übung), and weekly homework assignments. During the lecture, the foundational concepts for each week are introduced. In the lab sessions, students will be guided in practical programming exercises. Students receive programming homework assignments, to be uploaded at regular deadlines. Homework assignments are graded. The module ends with a portfolio exam consisting of a programming project (including a short presentation), a written exam and the homework grades.

Exam