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Data Cleaning is fundamental for every Data Science project. Regardless of where you're getting the data from, it's never perfect. Some invalid values, some odd types, outliers or statistically insignificant values will arise at some point. This course will teach you all the common issues found with messy data, and how to fix it with simple and clear techniques.
Duration
6 hours
Difficulty
professional
INE's world-class IT trainingInstructor for this course
Santiago Basulto
This course is composed by the following modules
INE's world-class IT training
Module 1
Dealing with Missing Data13 videos
Intro to Data Cleaning
Missing Values in Python and NumPy
Count NaN objects
Filter inf NaN Objects
Clean NaNs from array
Missing Values in Pandas
Identify NaN Values
Checking missing values
Percent of correct values
Dealing with Missing Data in Pandas
Dropping movies with null values
Dropping null columns from movies
Imputing missing duration on movies
Module 2
Duplicated Values and Invalid Types7 videos
Dealing with Duplicated Values in Pandas
Finding duplicated movies
Dropping duplicated names
Dealing with Invalid Types
Parse invalid numeric columns
Parse invalid date columns
Parse invalid boolean columns
Module 3
String Handling and Invalid Values9 videos
Pandas String Handling
Identify comedy movies
Replace country names
DataFrame columns reshaping
Salary with dollar sign and separators
Dealing with Invalid Values
Fixing bedrooms and bathrooms
Fixing properties area
Using Statistics to clean data and find outliers
Module 4
Final Project1 video
New York City Airbnb Open Data
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