RWTHx: Basics of Data Science

"Basics of Data Science" gives a comprehensible overview of many fundamental concepts and tools of data science, including data quality and data preprocessing, supervised and unsupervised learning techniques including their evaluation, frequent itemsets and association rules, sequence mining, process mining, text mining, and responsible data science.

Basics of Data Science

An intermediate-level course by RWTHx

Course Description

"Basics of Data Science" is a comprehensive and engaging intermediate-level course designed to provide students with a solid foundation in the exciting world of data science. This course covers a wide range of fundamental concepts, challenges, and tools essential for anyone looking to dive into the field of data science.

What Students Will Learn

A comprehensive overview of data science concepts and techniques
Understanding of data science's role in society and business
Ability to conceptualize and perform basic data analysis
Skills to evaluate and interpret data analysis outcomes
Knowledge of responsible data science practices
Understanding of machine learning, data mining, and AI limitations
Proficiency in writing Python programs and using popular libraries
Application of various data analysis techniques
Handling of data quality issues and preprocessing
Implementing data science techniques while maintaining confidentiality and fairness

Prerequisites

While this course is open to individuals from all disciplines with an interest in data science, prior knowledge in mathematics (mathematical notations, linear algebra, stochastics, and statistics) is advantageous but not mandatory.

Course Content

Data science infrastructure and big data concepts
Data exploration and visualization
Supervised learning techniques (decision trees, regression, SVMs, neural networks)
Unsupervised learning and clustering
Pattern mining and process mining
Text mining and natural language processing
Data preprocessing and quality management
Evaluation of machine learning models
Ethical considerations in data science
Responsible data science practices (fairness, accuracy, confidentiality, transparency)
Hands-on exercises using Python and Jupyter notebooks

Who This Course Is For

Students from various disciplines interested in data science
Professionals looking to transition into data science roles
Business analysts seeking to enhance their data analysis skills
Researchers wanting to incorporate data science techniques into their work
Anyone curious about the power and potential of data science in today's world

Real-World Applications

Analyzing business data to make informed decisions
Developing predictive models for various industries
Improving process efficiency through data-driven insights
Extracting valuable information from text data
Implementing responsible data practices in organizations
Enhancing product development through data analysis
Conducting market research and consumer behavior analysis
Optimizing operations and supply chain management
Contributing to scientific research across various fields
Developing data-driven solutions for societal challenges

Syllabus

Week 1: Introduction, Data Exploration & Visualization

Week 2: Supervised Learning Techniques

Week 3: Evaluation of Supervised Learning, Data Quality & Preprocessing

Week 4: Clustering, Frequent Itemsets

Week 5: Association Rule Mining, Sequence Mining

Week 6: Process Mining

Week 7: Text Mining

Week 8: Responsible Data Science

Week 9: The Bigger Picture