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Zero To Mastery

Data Wrangling Bootcamp: Turn Alien Data into AI

via Zero To Mastery Path

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Overview

Learn Data Wrangling with Python and Pandas from scratch - no prior knowledge necessary! You'll tame and analyze messy real-world datasets (including real-world data on UFOs!), combine tables, automate transformations, preprocess data for AI & machine learning, and turn raw data into useful real-world results for the AI Age.
  • Turn messy real-world data into clean, analysis-ready datasets
  • Quickly explore unfamiliar files and spot problems before they cause trouble
  • Filter, sort, group, and reshape data to uncover useful insights
  • Clean up missing values, duplicates, messy text, and awkward dates
  • Combine multiple datasets confidently with joins and concatenation
  • Build flexible transformations with custom functions, map, and apply
  • Use AI to debug errors, generate code, and learn new techniques faster
  • Prepare data for machine learning, feature engineering, and AI models

Syllabus

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

Taught by

Travis Cuzick

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