Data Science Bootcamp

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The listed learning units belong to the course Data Science Bootcamp. Do you want to access all course content?

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Week 1 (Intro to Data Science and Visualization)

We start this week with an overview of the data science cycle, combined with the reasons and perspectives you need as a data scientists. Then, we introduce some general variable types and some visualization approaches. The week ends with an introduction to basic database queries.

Welcome to Week 1

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Overview of Data Science and Its Applications

Video

Essential Skills for Data Scientists

Video

Overview of the Data Science life-cycle

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Data Types and Basic Data Visualization Techniques

Video

Code-snippets: Data visualization basics

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Careful about Pie Charts

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Introduction to SQL

Video

Introduction to SQL

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Introduction to SQL

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Introduction to SQL

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Two more important things on tables...

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Self-test on SQL

Learning materials

Solution sheet: SQL Quiz

Learning materials

Assignment Week 1

Learning materials

Week 2 (EDA and Statistical Analysis)

What is an EDA - and how can you apply it? We focus on this question, while giving you plenty of chances to use your new knowledge about fundamentals of statistical models.

Welcome to Week 2

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Introduction to EDA

Video

Notebook on EDA and 911 dataset

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Solution sheet: Titanic EDA Quiz

Learning materials

Techniques for Handling Missing Values

Video

More on Handling Missing Data in Practice

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Univariate Analysis

Video

Bivariate Analysis Quiz

Learning materials

Code-snippets: Univariate Analysis

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Data Formatting and Normalization

Video

Bivariate Analysis

Video

Notebook on Bivariate Analysis

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Live-Stream #1 (Optional, but educational ;-)

Video

Forum activity: How well do descriptive metrics describe a dataset?

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Solution sheet: Anscombe's Quartet Quiz

Learning materials

Multivariate analysis (MVA)

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Multivariate analysis (MVA)

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Multivariate analysis (MVA)

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Multivariate analysis (MVA)

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More remarks on Multivariate analysis (MVA)

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Self-test on MVA

Learning materials

Solution sheet: MVA Quiz

Learning materials

Assignment Week 2

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Week 3 (Machine Learning)

We reach the machine learning section of the course! You will learn what often used terms mean and how to enhance your data models with existing libraries.

Linear Regression

Video

Welcome to Week 3

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Introduction to Machine Learning

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Linear Regression Quiz

Learning materials

Decision Trees Part 1

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Decision Trees notebook

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Decision Trees Quiz

Learning materials

Decision Trees Part 2

Video

Logistic Regression

Video

Logistic Regression Notebook

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K-Nearest Neighbor (KNN)

Video

K-Nearest Neighbor (KNN)

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Solution sheet: Logistic Regression

Learning materials

K-Nearest Neighbor (KNN)

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K-Nearest Neighbor (KNN)

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Additional info on KNN

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Linear Regression Notebook

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Self-test on KNN

Learning materials

Solution sheet: KNN Quiz

Learning materials

Assignment Week 3

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Week 4 (Final Exam 28.6 - 4.7)

This week is for your final exam: One assignment to finish in one sitting, testing your knowledge for the CORE track. After that, we continue with the PROJECT track in week 5 and 6.

Final Exam

Learning materials

PROJECT track & Peer Review

The chance to put it all together! You will work on a project for a week, combing various elements, commands and lessons you learned in the CORE track. The true test of this data science bootcamp. You will review the project submission of others, who have worked on the same assignment for the last week as you did. You feedback and evaluation will be used to grade the work of others. Only if you participate in both phases - submitting your own project and reviewing another project - you get feedback and a grade on the PROJECT track.

Introduction to the PROJECT phase

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Project Track on OpenJupyter

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Peer Review: Grading criteria - This is a MUST-read.txt!

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Peer Assessment

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