Data Wrangling with Python
In this course, Data Wrangling with Python, you'll learn about various functions and procedures that will help you get your data in order, providing a clean and well-constructed dataset for further data analysis and machine learning.
What you'll learn
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, 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.
Table of contents
About the author
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.
