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Python for Data Analysis: Step-By-Step with Projects
Introduction
Introduction (4:02)
Course Overview (5:31)
Python Crash Course
Setting Up Python environment (7:19)
Overview of Data Types, Numeric, Define Variables (8:19)
Strings, Common Functions, and Methods (14:31)
Lists, Tuples, Sets, Dictionaries, Booleans (13:56)
If Statements, Loops (19:11)
Define Functions, Use Packages (15:11)
Lambda Functions, Conditional Expressions (15:04)
Importing Data
Pandas Data Structures Overview (14:32)
Loading Data (8:00)
Previewing Data (6:30)
Pandas Data Types Overview (17:43)
Exporting Data (6:32)
Exploring Data
Combining Datasets (13:49)
Renaming Columns (7:42)
Selecting Columns (4:19)
Selecting Rows and Setting the Index (1) (14:36)
Selecting Rows and Setting the Index (2) (13:38)
Subsetting Both Rows and Columns (10:24)
Modifying Values (14:11)
Making a Copy (5:54)
Sorting Data (10:14)
Capstone Practice Project I
NBA Games Project Overview (2:45)
Cleaning Data
Data Cleaning Overview (2:15)
Removing Unnecessary Columns/Rows (10:58)
Missing Data Overview (17:02)
Tackling Missing Data (Dropping) (8:14)
Tackling Missing Data (Imputing with Constant) (18:03)
Tackling Missing Data (Imputing with Statistics) and Missing Indicators (20:05)
Tackling Missing Data (Imputing with Model) (7:38)
Handling Outliers (1) (15:23)
Handling Outliers (2) (15:57)
Cleaning Text (15:39)
Transforming Columns/Features
Extracting Date and Time (20:19)
Binning (17:55)
Mapping New Values (11:42)
Applying Functions (16:11)
Capstone Practice Project II
Czech Bank Project Overview (3:31)
Exploratory Data Analysis
EDA Overview (2:44)
Aggregating Statistics (23:09)
Group By (21:48)
Pivoting Tables (17:17)
Distribution of One Feature (17:20)
Seaborn Library Overview (14:03)
Relationship of Two Features (1) (11:38)
Relationship of Two Features (2) (16:28)
Relationship of Multiple Features (11:40)
Seaborn Library Recap (3:15)
Capstone Practice Project III
Olympic Games Project Overview (2:57)
Thank You
Course Wrap Up (0:36)
Tackling Missing Data (Imputing with Model)
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