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.
Course info
Rating
(25)
Level
Intermediate
Updated
Jan 8, 2020
Duration
1h 19m
Table of contents
Description
Course info
Rating
(25)
Level
Intermediate
Updated
Jan 8, 2020
Duration
1h 19m
Description

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.

About the author
About the author

Pratheerth is a freelance Data Scientist who has entered the field after an eclectic mix of educational and work experiences.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
[Autogenerated] Hi, everyone. My name is pretty apartment and welcome to my course on data wrangling with fighting. Currently, I'm a freelance data scientist. I used to work in manufacturing, but because of my love for all things data, I completely pivoted to data science, machine learning and data analytics and general followed the garbage in garbage out principle. If you want to learn from our predict something based on your data, you need to make sure that the data is well constructed, clean and suitable for your statistical on machine learning models. This ghost is filled with functions and procedures aimed at helping you do exactly that. Some of the media topics that we call would include contaminating, merging data from different sources, normalizing data, using different scaling methods, using multiple functions to reshape data and data and goading with fighting. When you finish with this ghost, you'll have the knowledge of data wrangling which will help you immensely during your data analysis and machine learning projects. There's just a single prerequisite for the course, which is an immediate level knowledge. Our fighting program, some familiarity with our data flows through in a machine learning pipeline would be helpful. But not absolutely necessary. I hope you'll join me on this journey to learn all about constructing the suitable data set for further data analysis with the data wrangling with fighting coast at your side.