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Data Wrangling Bootcamp: Turn Alien Data into AI
Section 01: Introduction
Introduction (4:20)
Exercise: Meet Your Classmates and Instructor
Understanding Your Video Player
Course Resources
Set Your Learning Streak Goal
Section 02: Data Wrangling 101
Introduction to Data Wrangling (5:52)
Our Data Wrangling Framework (4:55)
The Importance of Exploratory Data Analysis (EDA) (5:18)
A Note to Students - PLEASE READ
Section 03: Programming Basics for Data Wrangling
What is Programming? (6:28)
The Programming Environment (13:22)
Disabling Colab's AI Tools (5:08)
Values and Types (8:20)
Functions (10:17)
Expressions (10:04)
Expressions in COLAB (5:03)
Variables (13:29)
Naming Variables (6:19)
Exercises - Part 1
Errors (6:43)
Comments (5:39)
Text Cells (20:46)
Colab Tips and Tricks (14:45)
Objects, Attributes, and Methods (9:13)
Using Python Modules (12:17)
Lists (12:07)
Tuples (9:21)
Dictionaries (17:41)
Exercises - Part 2
Let's Have Some Fun (+ More Resources)
Section 04: DataFrames and Datasets
IMPORTANT - DOWNLOAD EXAMPLE DATASETS
Introducing DataFrames (10:47)
Introducing Our Datasets (2:39)
'read_csv' and DataFrames - Part 1 (10:48)
'read_csv' and DataFrames - Part 2 (4:35)
Providing Column Names (5:57)
Inspecting DataFrames (7:59)
The 'info' Method (11:16)
Renaming Columns (7:14)
Dropping Columns (7:00)
Selecting Columns (4:25)
Exercises
Course Check-In
Section 05: Series
Series 101 (9:13)
Converting Series with to_numeric (10:57)
Converting Series with to_datetime (6:08)
Adding Columns (Series) to DataFrames (10:04)
Creating Derived Columns (16:21)
The 'assign' Method (12:40)
Exercises
Unlimited Updates
Section 06: Exploratory Data Analysis with Pandas
The 'sum' Method (12:50)
The 'count' Method (10:26)
Mean and Median (12:40)
The 'describe' Method (12:38)
The 'describe' Method (12:38)
Using 'describe' on Non-Numeric Fields (11:01)
The 'unique' and 'nunique' Methods (10:36)
The 'value_counts' Method (6:49)
Exercises
Implement a New Life System
Section 07: AI Tools in Colab
AI Tools in Colab - Overview (2:47)
Enabling AI Tools in Colab (3:29)
In-Cell Gemini AI - Part 1 (9:32)
In-Cell Gemini AI - Part 2 (12:05)
AI-Powered Code Completion (11:01)
The Data Science Agent (DSA) (15:14)
DSA Demos (12:58)
Prompting Best Practices (6:07)
Global Superstore Data Wrangling Project: Introduction (2:01)
Global Superstore Data Wrangling Project: Solution (16:41)
Exercise: Imposter Syndrome (2:55)
Section 08: Data Wrangling Project - Phase 1
Data Wrangling Project - Phase 1 Introduction (4:45)
Data Wrangling Project - Phase 1 Requirements
Data Wrangling Project - Phase 1 Solution (6:59)
Section 09: Indexing and Sorting
The 'iloc' Method (14:42)
Indexing Basics (14:01)
The 'loc' Method (6:48)
Sorting by Index (11:36)
Sorting by Columns (16:04)
Dropping Rows by Index (9:39)
Exercises
Section 10: Selecting Data with Criteria
Filtering DataFrames with a Boolean Series (11:49)
Applying Other Logical Conditions (11:34)
The 'between' and 'isin' Methods (11:54)
Combining Conditions Using the AND Operator (18:50)
Combining Conditions Using the OR Operator (6:07)
Combining AND and OR (18:41)
Negation (11:26)
The 'isna' Method (16:22)
Exercises
Section 11: Updating DataFrames
Updating DataFrame Values with loc (9:24)
Replacing DataFrame Values (10:04)
Updating Values with Boolean Masks (15:49)
Removing Null Values (14:53)
Replacing Null Values (9:51)
Identifying Duplicate Data (9:29)
Removing Duplicate Data (11:03)
Exercises
Section 12: Working with String Data
The 'upper', 'lower', and 'capitalize' Methods (7:53)
The 'len' Method (4:19)
Regular Expressions (15:07)
Matching Digits (6:53)
The 'contains' Method (14:17)
The 'replace' Method - Part 1 (8:30)
The 'replace' Method - Part 2 (8:13)
Exercises
Section 13: Data Wrangling Project - Phase 2
Data Wrangling Project - Phase 2 Introduction (3:37)
Data Wrangling Project - Phase 2 Requirements
Data Wrangling Project - Phase 2 Solution (11:58)
Section 14: Combining Datasets
Stacking Datasets Vertically - Part 1 (11:20)
Stacking Datasets Vertically - Part 2 (9:07)
Importing All Excel Sheets Into a DataFrame (10:32)
Joining DataFrames with 'merge' - Part 1 (10:26)
Joining DataFrames with 'merge' - Part 2 (8:53)
Left and Right Joins (12:54)
Full Outer Joins (7:52)
Combining More Than Two Tables (11:03)
Exercises
Section 15: Data Wrangling Project - Phase 3
Data Wrangling Project - Phase 3 Introduction (4:25)
Data Wrangling Project - Phase 3 Requirements
Data Wrangling Project - Phase 3 Solution (8:46)
Section 16: Grouping and Aggregation
Grouping and Aggregating 101 (15:40)
Applying Multiple Aggregations (14:33)
Grouping by Multiple Columns (9:05)
The 'transform' Method (15:22)
Pythonic Pivot Tables (14:22)
Exercises
Section 17: Working with Datetime Data
Using Datetime Values as Criteria (15:00)
The 'datetime' Module - Part 1 (10:29)
The 'datetime' Module - Part 2 (8:51)
Date Math in Pandas (15:10)
The 'shift' Method - Part 1 (12:55)
The 'shift' Method - Part 2 (10:19)
Rolling Averages (14:03)
Getting Data Out of Colab (7:51)
Exercises
Section 18: Data Wrangling Project - Phase 4
Data Wrangling Project - Phase 4 Introduction (4:19)
Data Wrangling Project - Phase 4 Requirements
Data Wrangling Project - Phase 4 Solution (9:34)
Section 19: Functional Programming in Python
Note to Students - PLEASE READ
Apply-ing Functions to Data Analysis (4:39)
If Statements (10:43)
Applying Multiple Logical Conditions (11:27)
Incorporating "And" and "Or" Logic (13:32)
Creating Custom Functions (10:17)
Returning Values From Functions - Part 1 (8:30)
Returning Values From Functions - Part 2 (8:51)
Exercises
Section 20: Leveraging the 'map' and 'apply' Methods
The 'map' Method (10:38)
Using 'map' with Functions - Part 1 (9:35)
Using 'map' with Functions - Part 2 (10:47)
The 'apply' Method (10:11)
Applying 'apply' to Multiple Columns (7:15)
Exercises
Section 21: Feature Engineering & Preprocessing for Machine Learning
Introducing Machine Learning (5:15)
From Data to Predictions (10:18)
Data Cleaning for Machine Learning (15:28)
Encoding Categorical Data (12:15)
Training Your First Machine Learning Model (14:18)
Improving Model Accuracy with 'get_dummies' (9:11)
Feature Engineering for Machine Learning (13:02)
Section 22: Data Wrangling Project - Phase 5
Data Wrangling Project - Phase 5 Introduction (5:29)
Data Wrangling Project - Phase 5 Requirements
Data Wrangling Project - Phase 5 Solution (14:27)
Section 23: BONUS PROJECT - Fine-Tune a Transformer Model
From Tables to Text (8:34)
Fine-Tuning Transformers (9:40)
Data Wrangling for Transfomers - Project Introduction (9:41)
Data Wrangling for Transfomers - Project Requirements
Data Wrangling for Transformers - Project Solution (8:48)
Creating the Label Column (7:33)
Setting Up the Training (12:00)
Training and Testing (11:40)
Deploying with Gradio (17:12)
Section 24: Python In Excel
Introducing Python in Excel (7:05)
READ THIS: Do You Have Python in Excel?
Sharing Python-Powered Excel Workbooks
Working with Values and Cells (15:12)
Working with Ranges and Tables (16:22)
Row-Major Order - Part 1 (11:41)
Row-Major Order - Part 2 (10:29)
Separation of Concerns (9:09)
Adding Dynamic Inputs (9:55)
Incorporating Power Query (19:09)
Incorporating AI Tools (9:14)
The Python Editor (9:09)
Machine Learning Demo - Part 1 (12:52)
Machine Learning Demo - Part 2 (6:36)
Data Visualization Demo - Part 1 (13:56)
Data Visualization Demo - Part 2 (10:57)
Sentiment Analysis Project: Introduction (5:53)
Sentiment Analysis Project: Solution (17:33)
Where To Go From Here?
Thank You! (1:17)
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The Importance of Exploratory Data Analysis (EDA)