INFOMDWR

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Materials for Applied Data Science course INFOMDWR

Data Wrangling and Data Analysis

This webpage contains all materials required for the Applied Data Science course INFOMDWR.

The materials on this website are CC-BY-4.0 licensed.

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Syllabus

You can find the course syllabus as a web page here or as a pdf here. The course schedule with required reading materials is in the syllabus as well, specifically here.

Lectures

Week Date Topic
1 2024-09-05 Introduction to the course
1 2024-09-06 Data models
2 2024-09-09 Data extraction with SQL
2 2024-09-10 Integrity constraints in databases
2 2024-09-11 Functional Dependency & Data Integration
3 2024-09-16 Hetero. data analysis & string similarity
3 2024-09-17 Data extraction in Python
3 2024-09-18 Data preparation 1
4 2024-09-23 Data preparation 2
4 2024-09-24 Guest lecture on cloud computing
4 2024-09-25 Data visualization
5 2024-09-30 Exploratory data analysis
5 2024-10-01 Supervised learning: Regression
5 2024-10-02 Q&A
6 2024-10-07 Supervised learning: model evaluation
6 2024-10-08 Supervised learning: classification
6 2024-10-09 Deep learning
7 2024-10-14 Missing data 1: Mechanisms
7 2024-10-15 Missing data 2: Solutions
7 2024-10-16 Clustering
8 2024-10-21 Model-based clustering
8 2024-10-22 Text mining 1
8 2024-10-23 Text mining 2
9 2024-10-28 Time series
9 2024-10-29 Data streams
9 2024-10-30 Algorithmic fairness
10 2024-11-05 No lecture: study time
10 2024-11-06 Q&A

Labs

Week Date Topic
1 2024-09-05 Introduction lab: setting up your computer
1 2024-09-06 Data models
2 2024-09-09 Data extraction with SQL
2 2024-09-10 Integrity constraints in databases
2 2024-09-11 Functional Dependency & Data Integration
3 2024-09-16 Hetero. data analysis & string similarity
3 2024-09-17 Data extraction in Python
3 2024-09-18 Data preparation 1
4 2024-09-23 Data preparation 2
4 2024-09-24 Cloud computing
4 2024-09-25 Data visualization using ggplot
5 2024-09-30 Exploratory data analysis in R
5 2024-10-01 Supervised learning: Regression models in R
5 2024-10-02 No lab, time to study
6 2024-10-07 Supervised learning: model evaluation
6 2024-10-08 Supervised learning: classification
6 2024-10-09 Deep learning
7 2024-10-14 Missing data mechanisms
7 2024-10-15 Imputation methods
7 2024-10-16 Clustering
8 2024-10-21 Model-based clustering using MClust
8 2024-10-22 Text mining 1
8 2024-10-23 Text mining 2
9 2024-10-28 Time series
9 2024-10-29 Data streams
9 2024-10-30 Algorithmic fairness
10 2024-11-05 No lab: study time
10 2024-11-06 No lab: study time

Deadlines

Week Date Topic
4 2024-09-23 10:00AM assignment 1
5 2024-10-04 Midterm exam
6 2024-10-07 10:00AM assignment 2
8 2024-10-21 10:00AM assignment 3
10 2024-11-04 10:00AM assignment 4
10 2024-11-08 Final exam
10 2025-01-06 Resit exam