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Data Wrangling with Python

You're reading from   Data Wrangling with Python Creating actionable data from raw sources

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789800111
Length 452 pages
Edition 1st Edition
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Authors (2):
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Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
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Table of Contents (12) Chapters Close

Data Wrangling with Python
Preface
1. Introduction to Data Wrangling with Python FREE CHAPTER 2. Advanced Data Structures and File Handling 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Application of Data Wrangling in Real Life Appendix

An Extension to Data Wrangling


This is the concluding chapter of our book, where we want to give you a broad overview of some of the exciting technologies and frameworks that you may need to learn beyond data wrangling to work as a full-stack data scientist. Data wrangling is an essential part of the whole data science and analytics pipeline, but it is not the whole enterprise. You have learned invaluable skills and techniques in this book, but it is always good to broaden your horizons and look beyond to see what other tools that are out there can give you an edge in this competitive and ever-changing world.

Additional Skills Required to Become a Data Scientist

To practice as a fully qualified data scientist/analyst, you should have some basic skills in your repertoire, irrespective of the particular programming language you choose to focus on. These skills and know-hows are language agnostic and can be utilized with any framework that you have to embrace, depending on your organization and...

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