概貌 | Data Science Bootcamp

列出的学习单元属于课程Data Science Bootcamp。您想访问所有课程内容吗？

Introduction - Course Overview and Housekeeping

In this first short section, we want to give you an overview of the course, learn more about you and start the bootcamp.

Welcome to the Bootcamp! Important information

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Course Survey Beginning

调查

Welcome! Course & Team Intro

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Self-Reflection Questionnaire

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10 Questions for the Teaching Team | Q&A

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Your existing knowledge

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Introduction to JupyterLab

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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

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Essential Skills for Data Scientists

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Overview of the Data Science life-cycle

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Data Types and Basic Data Visualization Techniques

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Code-snippets: Data visualization basics

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Careful about Pie Charts

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Two more important things on tables...

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Self-test on SQL

自我测试

Solution sheet: SQL Quiz

分级测试

Assignment Week 1

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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

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Notebook on EDA and 911 dataset

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Solution sheet: Titanic EDA Quiz

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Techniques for Handling Missing Values

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More on Handling Missing Data in Practice

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Univariate Analysis

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Code-snippets: Univariate Analysis

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Data Formatting and Normalization

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Bivariate Analysis

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Notebook on Bivariate Analysis

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Bivariate Analysis Quiz

自我测试

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

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Solution sheet: Anscombe's Quartet Quiz

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

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More remarks on Multivariate analysis (MVA)

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Self-test on MVA

自我测试

Solution sheet: MVA Quiz

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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.

Welcome to Week 3

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Introduction to Machine Learning

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Linear Regression

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Linear Regression Notebook

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Linear Regression Quiz

自我测试

Decision Trees Part 1

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Decision Trees Part 2

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Decision Trees notebook

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Decision Trees Quiz

自我测试

Logistic Regression

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Logistic Regression Notebook

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Solution sheet: Logistic Regression

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Additional info on KNN

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Self-test on KNN

自我测试

Solution sheet: KNN Quiz

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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

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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

奖励性同伴评估

I like, I wish

I like, I wish - what is that?

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