Example Curriculum
Section 01: Introduction
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Section 02: Data Wrangling 101
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Section 03: Programming Basics for Data Wrangling
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- 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
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- 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
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Section 06: Exploratory Data Analysis with Pandas
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Section 07: AI Tools in Colab
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- 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
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Section 09: Indexing and Sorting
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Section 10: Selecting Data with Criteria
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- 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
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Section 12: Working with String Data
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Section 13: Data Wrangling Project - Phase 2
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Section 14: Combining Datasets
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- 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
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Section 16: Grouping and Aggregation
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Section 17: Working with Datetime Data
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Section 18: Data Wrangling Project - Phase 4
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Section 19: Functional Programming in Python
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- 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
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Section 21: Feature Engineering & Preprocessing for Machine Learning
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Section 22: Data Wrangling Project - Phase 5
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Section 23: BONUS PROJECT - Fine-Tune a Transformer Model
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- 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
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- 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?
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