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Data Wrangling with Python 3.x [Video]

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  • Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset. 
  • Retrieving data from different data sources (CSV, JSON, Excel, PDF) and parse them in Python to give them a meaningful shape.
  • Learn about the amazing data storage places in an industry which are being highly optimized.
  • Perform statistical analysis using in-built Python libraries.
  • Hacks, tips, and techniques that will be invaluable throughout your Data Science career.

You might be working in an organization, or have your own business, where data is being generated continuously (structured or unstructured) and you are looking to develop your skillset so you can jump into the field of Data Science. This hands-on guide shows programmers how to process information.

In this course, you will gather data, prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and more! This course will equip us with the tools and technologies, also we need to analyze the datasets using Python so that we can confidently jump into the field and enhance our skill set. The best part of this course is the takeaway code templates generated using the real-life dataset.

Towards the end of the course, we will build an intuitive understanding of all the aspects available in Python for Data Wrangling.

All codes and supporting files are placed on GitHub at this link: https://github.com/PacktPublishing/-Data-Wrangling-with-Python-3.x

Style and Approach

This hands-on course demonstrates concepts via slides, to make sure they're explained in simple ways. Throughout the course, we will be using datasets downloaded from the UCI Machine Learning Repository and various sources on the public web for conceptual practical intuition.

In every section, we will be looking into the theoretical concepts related to the section and then jump on practical examples using the number one IDE for Data Science i.e. Spyder IDE.

Each line of code will be explained in detail and the output will be instantly shown in the variable explorer of the IDE.

  • Perform effective data wrangling to achieve your analytical goals by working with real-world problems.
  • A step-by-step guide to acquiring and then pre-processing datasets to draw useful insights from them.
  • Use the in-built features of Python to acquire, clean, analyze, and present data efficiently.
Course Length 3 hours 35 minutes
Date Of Publication 31 Jan 2019
Installing Anaconda Navigator on Windows/Linux
Importing and Parsing CSV in Python
Importing and Parsing JSON in Python
Scraping Data from Public Web – Part 1
Scraping Data from Public Web – Part 2
Importing and Parsing Excel Files – Part 2
Manipulating PDF Files in Python – Part 1
Manipulating PDF Files in Python – Part 2
Encoding/Mapping Existing Values – Part 1
Encoding/Mapping Existing Values – Part 2
Rescale/Standardize Column Values
Common Cleaning Operations
Exporting Datasets for Future Use
Types of Column Names/Features/Attributes in Structured Data
Split-Apply-Combine (Performing Group By Operation)
Descriptive Statistics Using Python – Part 1
Descriptive Statistics Using Python – Part 2


Jamshaid Sohail

Jamshaid Sohail is a Data Scientist who is highly passionate about Data Science, Machine learning, Deep Learning, big data, and other related fields. He spends his free time learning more about the field and learning to use its emerging tools and technologies. He is always looking for new ways to share his knowledge with other people and add value to other people's lives. He has also attended Cambridge University for a summer course in Computer Science where he studied under great professors and would like to impart this knowledge to others. He has extensive experience as a Data Scientist in a US-based company. In short, he would be extremely delighted to educate and share knowledge with, other people.