Author: Brian Lipp
Pub Date: 2020
Size: 127 Mb
Simplify your ETL processes with these hands-on tips, tricks, and best practices
Though huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. The Data Wrangling Workshop teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.
The workshop begins by demonstrating how to work with data structures using Python. Through examples and activities, you’ll understand why you should stay away from traditional ways of data cleaning as done in other languages and take advantage of the specialized pre-built routines in Python. As you progress, you’ll learn how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the workshop teaches you how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool.
By the end of the workshop, you would have learned several data wrangling techniques and best practices that’ll give you the confidence to extract, clean, transform, and format your data efficiently from a diverse array of sources.
What you will learn
- Get to grips with the fundamentals of data wrangling
- Learn how to model data with random data generation and data integrity checks
- Discover how to examine data with descriptive statistics and plotting techniques
- Explore how to search and retrieve information with regular expressions
- Delve deep into the commonly used data science libraries of Python
- Learn how to handle and compensate for missing data