Machine Learning and Data analytics in general follows the garbage-in/garbage-out principle. If you want to learn from or predict based on your data, you need to make sure that data is well constructed and cleaned. This course, Data Wrangling with Python 3, is aimed at helping you do exactly that. First, you’ll see how to merge data from different sources using the methods concat, append, and merge. Next, you’ll discover how to combine data into groups. The primary function used here is groupby. In the next two sections, you’ll explore how to transform and normalize data. You’ll learn why these processes are necessary, and then proceed to see how they work in practice. Finally, you’ll examine important processes such as One Hot Encoding, which enables further processing during data analysis. When you’re finished with this course, you’ll have thorough knowledge of data wrangling which will help you immensely during your data analysis and machine learning projects.
Author Name: Pratheerth Padman
Author Description:
Pratheerth is a Data Scientist who has entered the field after an eclectic mix of educational and work experiences. He has a Bachelor’s in Engineering in Mechatronics from India, Masters in Engineering Management from Australia and then a couple of years of work experience as a Production Engineer in the Middle East. Then when the A.I bug bit him, he dropped everything to dedicate his life to the field. He is currently working on mentoring, course creation and freelancing as a Data Scientist.

Table of Contents

• Course Overview
  1min
• Concatenating and Merging Data from Different Sources
  24mins
• Combining Data into Groups
  12mins
• Normalizing Data with Pandas
  11mins
• Reshaping Data with Python
  13mins
• Data Encoding with Python
  15mins