README.md

DataScienceSS20

Course materials for the Data Science online class in SS20.

Block 1 - Introduction

Static Slides

• Into + Orga
• Data Science 101

Interactive Notebooks

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• HowTo Jupyter Notebooks
• Use Case: NY Taxi

Lab

Python Intro

• Python Intro Part 1
• Python Intro Part 2
• Python Intro Part 3
• Python Intro Part 4
• Python Intro Part 5
• Python Intro Part 6
• Python Intro Part 7

MatPlotLib

• Intro MatPlotLib

Exercises

Please upload your solutions via Moodle!

• Exercise 1_a --Solution--
• Exercise 1_b --Solution--

Block 2 - Recommender Systems

Static Slides

• Recommender Systems I
• Tensor Algebra
• Recommender Systems II
• Use Case

Notebooks

• Tensor Algebra
• Use Case

Lab

• NumPy

Exercises

Please upload your solutions via Moodle!

• Exercise 2_a --Solution--

• Exercise 2_b --Solution--

Block 3 - Data Wrangling

Static Slides

• Data Wrangling
• Basic Statistics
• The Group By Pattern

Notebooks

• Data Wrangling
• Basic Statistics
• The Group By Pattern

Lab

• Pandas Intro Part 1
• Pandas Intro Part 2
• Pandas Intro Part 3
• Pandas Intro Part 4
• Pandas Intro Part 5
• Pandas Intro Part 6
• Pandas Intro Part 7

Exercises

• 3_a_Basic Pandas -- solution --
• 3_b_Baby Names -- solution --
• 3_c_Titanic -- solution --
• 3_d_GroupBy I -- solution --
• 3_e_GroupBy II -- solution --
• 3_e_GroupBy III -- solution --

Block 4 - Machine Learning I-II

Static Slides

• Data Storage
• Machine Learning I - Intro
• Machine Learning II - Clustering
• Scikit-Learn

Notebooks

• Data Storage
• Use Case: Clustering

Lab

• HDF5
• Scikit-Learn

Exercises

• 4_g_Clustering_I -- solution --
• 4_h_Clustering_NY_Taxi -- solution --

Block 5 - Machine Learning III-IV

Static Slides

• Statistics II
• Machine Learning III - Naive Bayes
• Machine Learning IV - Linear Models

Notebooks

• Statistics II

Exercises

• 5_a_Classification_MNIST -- solution --
• 5_b_Regression_NY_Taxi -- solution --

Block 6 - Machine Learning V-VII

Static Slides

• Machine Learning V - Non-Linear Models I
• Machine Learning V - Non-Linear Models II - SVM
• Machine Learning VI - Outlier Detection
• Machine Learning VII - Model Selectionn
• Python Lambda Expressions

Notebooks

• Python Lambda Expressions

Exercises

• 6_a_Non-Linear_Classification_MNIST -- solution --
• 6_b_Bayes_Optimization_Regression_NY_Taxi -- solution --

Block 7 - Deep Learnig

Static Slides

• Feature Extraction
• Introduction to Deep Learning

Notebooks

• Keras Intro

Exercises

• 7_a_Outlier_Detection_with Autoencoders -- solution --
• 7_b_Image_Classification_with_CNNs -- solution --

Block 8 - Deep Learnig

Static Slides

• Text Analysis
• Graph Analysis
• Recommender Systems III

Notebooks

• Use Case: Social Media Graph Analysis
• Tutorial: NetworkX

Exercises

• no exercises -> project work