Data Cleaning in Python Essential Training

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Miki Tebeka

49:25

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[1] Introduction

[1] Why is clean data important.mp4

00:34

[2] What you should know.mp4

00:33

[2] 1. Bad Data

[1] Types of errors.mp4

00:56

[2] Missing values.mp4

02:03

[3] Bad values.mp4

02:49

[4] Duplicates.mp4

01:19

[3] 2. Causes of Errors

[1] Human errors.mp4

01:01

[2] Machine errors.mp4

00:45

[3] Design errors.mp4

00:59

[4] Challenge UI design.mp4

00:17

[5] Solution UI design.mp4

00:46

[4] 3. Detecting Errors

[1] Schemas.mp4

02:06

[2] Validation.mp4

01:18

[3] Finding missing data.mp4

02:24

[4] Domain knowledge.mp4

00:59

[5] Subgroups.mp4

01:00

[6] Challenge Find bad data.mp4

00:29

[7] Solution Find bad data.mp4

01:04

[5] 4. Preventing Errors

[1] Serialization formats.mp4

02:12

[2] Digital signatures.mp4

01:49

[3] Data pipelines and automation.mp4

02:26

[4] Transactions.mp4

02:33

[5] Data organization and tidy data.mp4

00:53

[6] Process and data quality metrics.mp4

01:46

[7] Challenge ETL.mp4

00:54

[8] Solution ETL.mp4

01:51

[6] 5. Fixing Errors

[1] Renaming fields.mp4

01:40

[2] Fixing types.mp4

01:36

[3] Joining and splitting data.mp4

02:13

[4] Deleting bad data.mp4

01:13

[5] Filling missing values.mp4

02:00

[6] Reshaping data.mp4

01:05

[7] Challenge Workshop earnings.mp4

00:53

[8] Solution Workshop earnings.mp4

02:29

[7] Conclusion

[1] Next steps.mp4

00:30

Description

If you’re looking for more efficient ways to prepare your data for analysis, it’s time to level up your skill set and reassess your approach to data cleaning. In this course, instructor Miki Tebeka shows you some of the most important features of productive data cleaning and acquisition, with practical coding examples using Python to test your skills. Learn about the organizational value of clean high-quality data, developing your ability to recognize common errors and quickly fix them as you go. Along the way, Miki offers cleaning strategies that can help optimize your workflow, including tips for causal analysis and easy-to-use tools for error prevention.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.

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Python

Data Mining

Miki Tebeka

Instructor's Courses

Teaching effective hands-on workshops all of the world. Consultant solving hard problem with the right tools (Java and C++ are *not* the right tools ;). Book author, LinkedIn learning Author, open source contributor and convention organizer, meetup co host and coding for fun in my spare time. Specialties: Python & Scientific Python (Expert), Go (Expert), C/C++, Clojure, JavaScript, bash, ... Information retrieval - tokenization, summarization, clustering, search ... Concurrency - Multi process, multi threaded, Hadoop ... Web development - REST APIs, jQuery, JavaScript, CSS, (X)HTML Assemblers, Linkers, Debugger, Simulators SCM tools (git, Mercurial, Perforce, subversion, CVS, ClearCase) Linux, OS X and Windows Functional Programming, OOD, OOP Databases - SQL (BigQuery, PostgreSQL, MySQL, Oracle) and NoSQL (Redis, MongoDB, CouchDB)

Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.