Data Preparation (Import and Cleaning) for Python

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Chris Behrens

1:56:41

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01 Introduction

001 Course Introduction.mp4

01:18

002 The Python Packages Used in This Course.mp4

04:08

002 The SciPy Stack.txt

002 pandas.txt

002 pandas-profiling.html

002 scikit-learn.html

003 Introduction to Python Development.txt

003 Prerequisite and Related Courses.mp4

00:54

003 Using Pythons Math Science and Engineering Libraries.txt

003 Using Python for Data Management and Reporting.txt

02 Database Access

001 DB-API PEP 249.txt

001 Section Introduction The Python DB-API.mp4

02:56

002 Relational Databases.mp4

09:45

003 MongoDB Python module.txt

003 NoSQL (Non-Relational) Databases.mp4

10:13

003 Redis Python module.txt

004 Embedded Databases.mp4

04:20

005 Section Recap Database Access.mp4

02:12

03 Data Visualization

001 Section Introduction Why Data Visualization.mp4

01:35

002 Python Documentation.html

002 What Kind of Data Can Python Read.mp4

04:01

002 data.txt

003 Reading and Writing Tabular Data.mp4

03:21

003 data.txt

004 Calculating Summary Statistics.mp4

06:41

004 data.txt

005 Course Introduction to Jupyter Notebooks.txt

005 Data Profiling.mp4

10:25

005 data.txt

005 pandas profiling API Documentation Advanced Usage.html

006 Section Recap Data Visualization.mp4

02:24

04 Data Cleansing

001 Section Introduction Making Your Data Sparkle and Shine.mp4

01:33

002 Missing and Invalid Data.mp4

06:24

002 missing-data.txt

003 Outlying Data.mp4

06:41

003 data.txt

004 String Processing.mp4

05:40

005 Section Recap Data Cleansing.mp4

01:12

05 Preprocessing Data for Machine Learning

001 Section Introduction What Is the sklearn.preprocessing Package.mp4

02:12

002 Sample Dataset.txt

002 Standardizing Your Dataset.mp4

04:10

003 Non-Linear Transformation.mp4

04:57

003 Sample Data.txt

004 Normalization and Discretization.mp4

04:06

004 Sample Data.txt

005 Categorical Features.mp4

03:19

005 Sample Data.txt

006 Polynomial Features and Custom Transformers.mp4

03:54

006 Sample Data.txt

007 Section Recap Preprocessing Data for Machine Learning.mp4

01:38

06 Conclusion

001 Python Pitfalls.mp4

05:10

001 Sample Data.txt

002 Course Summary.mp4

01:05

003 Conclusion and Whats Next.mp4

00:27

Description

This course explores the tools available in the Python language for data preparation.

What You'll Learn?

Python can be a powerful tool for data preparation. In this course, we will quickly cover how to connect to various database types. Then, we will jump into using the pandas Python package for data preparation. We will look at examples of cleansing missing and outlying data as well as data visualizations and exploration. In addition to the pandas package, we will also look at preprocessing data for machine learning using the scikit-learn Python package. Before beginning this course, you should have a strong knowledge of Python and data approaches. Check out the **Prerequisite and Related Courses** lesson in the Introduction section for a starting point.

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Python

Data Science

Chris Behrens

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