Python Programming:

• Introduction to Python
• Programming in Python
• The print() Function
• Python Syntax
• Computer Programs
• Code Comments
• Arithmetical Operations

Python Variables:

• Saving Values
• Variable Names
• Updating Variables
• Syntax Shortcuts

Python Data Types:

• Integers and Floats
• Conversion Between Types
• Strings
• Escaping Special Characters
• String Concatenation
• String Conversion
• Multi-line Strings

Python Lists:

• Storing Row Elements into Variables
• Storing Rows as Lists
• List Length
• List Indexing
• Retrieving Values from Lists
• Negative Indexing
• Retrieving Multiple List Elements
• List Slicing
• List of Lists
• Retrieving from List of Lists

Python For Loops:

• Repetitive Processes
• For Loops
• For Loop Structure
• Opening a File
• Kinds of Errors
• Dealing with Errors
• Method to Calculate an Average and its alternate

Python Conditional Statements:

• If Statements
• Booleans
• If Statement Fundamentals
• Multiple Conditions
• The OR Operator
• Combining Logical Operators
• Comparison Operators
• Comparison Operator Applications
• The else Clause
• The elif Clause
• Else vs. elif
• Python Dictionaries:
• Dictionaries
• Creating a Dictionary and its alternate
• Key-Value Pairs
• Checking for Membership
• Updating Dictionary Values
• Counting with Dictionaries
• Finding the Unique Values
• Proportions and Percentages
• Looping over Dictionaries
• Frequency Tables for Numerical Columns
• Filtering for the Intervals

Python Functions:

• Functions
• Built-In Functions
• Customized Functions
• The Structure of a Function
• Parameters and Arguments
• The Return Statement
• Reusability and Multiple Parameters
• Keyword and Positional Arguments
• Combining Functions
• Debugging Functions
• Interfering with the Built-In Functions
• Variable Names and Built-In Functions
• Default Arguments
• Multiple Return Statements
• Returning Multiple Variables
• Tuples
• No Return Statement
• Function Behavior
• Scopes — Global and Local
• Scopes — Searching Order

List Comprehensions and Lambda Functions:

• The JSON Format
• Reading a JSON file
• Deleting Keys
• List Comprehensions to Transform and Create Lists
• List Comprehensions to Reduce a List
• Functions as Arguments
• Lambda Functions
• Lambda Functions to Analyze JSON data
• JSON files into pandas
• Exploring Tags Using the Apply Function
• Extracting Tags Using Apply with a Lambda Function

Cleaning, Preparing and Data Analysis:

• Introducing Data Cleaning
• Replacing Substrings with the Replace Method
• Cleaning Columns
• String Capitalization
• Errors During Data Cleaning
• Parsing Numbers from Complex Strings
• Summarizing Data
• Inserting Variables into Strings
• Creating a Summary Function
• Formatting Numbers Inside Strings

Python Object-Oriented:

• Introduction
• Classes and Objects
• Defining a Class
• Instantiating a Class
• Creating Methods
• Understanding “self’”
• Creating a Method that Accepts an Argument
• Attributes and the Init Method
• Creating Other Method
• Creating and Updating an Attribute

 Python Dates and Times:

• Importing Modules
• The Datetime Module
• The Datetime Class
• Strptime to Parse Strings as Dates
• Strftime to Format Dates
• The Time Class
• Comparing Time Objects
• Calculations with Dates and Times

Introduction to Pandas:

• Introduction to Series
• Introducing DataFrames
• Series vs DataFrames
• Selecting a Column from a DataFrame by Label
• Selecting Rows from a DataFrame by Label
• Value Counts Method
• Selecting Items from a Series by Label

Data Exploration with Pandas:

• Vectorized Operations
• Series Data Exploration Methods
• Series Describe Method
• Method Chaining
• Dataframe Exploration Methods
• Dataframe Describe Method
• Assignment with Pandas
• Boolean Indexing with Pandas Objects
• Boolean Arrays to Assign Values
• Creating New Columns
• iloc to select by integer position
• Pandas methods to create boolean masks
• Working with Integer Labels
• Pandas Index Alignment
• Pandas Boolean Operators
• Sorting Values
• Loops with Pandas

Line Graphs and Time Series:

• Matplotlib
• Customizing a Graph
• Types of Growth Types of Change
• Comparing Line Graphs

Scatter Plots and Correlations:

• Seasonal Trends
• Scatter Plots
• Correlation
• Pearson Correlation Coefficient
• Measuring Pearson’s r
• Correlation vs. Causation

Bar Plots, Histograms, and Distributions:

• Bar Plots and its customization
• Frequency Tables
• Grouped Frequency Tables
• Histograms
• The Normal Distribution
• The Uniform Distribution
• Skewed Distributions

Grid Charts:

• Pandas Visualization Methods
• Comparing Graphs
• Grid Charts and Subplots

Relational Plots:

• Seaborn
• Variable Representation;
• Color
• Size
• Shape
• Spatial Separation

Audience Design:

• The Familiarity Principle
• Matplotlib Interfaces
• The OO Interface
• Maximizing Data-Ink
• Erasing Non-Data Ink
• Erasing Redundant Data-Ink
• Title and Subtitle
• Reshaping Data with the Melt Function

Storytelling:

• Data Stories
• Grid Charts in Matplotlib
• Faster Workflow
• Modifications
• Structural Elements
• Progress Bar

Gestalt and Attentive Attributes:

• Gestalt Principles
• Proximity
• Similarity
• Enclosure
• Connection
• Visual Hierarchy
• Pre-Attentive Attributes

Styles:

• Matplotlib Styles
• FiveThirtyEight Style
• Labels
• Signature
• Coloring

Data Aggregation:

• Loops to Aggregate Data
• GroupBy Operation
• GroupBy Objects
• Aggregation Methods with Groupby
• Agg() Method
• Multiple and Custom Aggregations
• Aggregation with Pivot Tables

Data Combining:

• Data Concatenation
• Combining Dataframes with Different Shapes
• Joining Dataframes with the Merge
• Left Joins
• Join on Index

Data Transformation:

• Function Element-wise Using the Map and Apply Methods
• Function Element-wise to Multiple Columns Using Applymap Method

Working with Strings:

• Apply to Transform Strings
• Vectorized String Methods
• Exploring Missing Values with Vectorized String Methods
• Finding Specific Words in Strings
• Extracting Substrings from a Series
• Extracting All Matches of a Pattern from a Series
• Extracting More Than One Group of Patterns from a Series

Missing And Duplicate Data:

• Identifying Missing Values
• Correcting Data Cleaning Errors
• Visualizing Missing Data
• Using Data From Additional Sources
• Identifying Duplicates Values
• Correcting Duplicates Values
• Handle Missing Values
• Analyzing Missing Data
• Imputation

Regular Expression:

• The Regular Expression Module
• Matches with pandas Methods
• Regular Expressions to Select Data
• Quantifiers
• Character Classes
• Matching Text with Capture Groups
• Negative Character Classes
• Word Boundaries
• Matching at the Start and End of Strings
• Flags to Modify Regex Patterns
• Capture Groups to Extract Data
• Lookarounds to Control Matches
• BackReferences
• Substituting Regular Expression
• Multiple Capture Groups
• Named Capture Groups to Extract Data

Git:

• Introduction to Version Control Systems
• The git Folder
• Creating Files in the Repository
• Checking File Status
• Configuring Identity in Git
• Committing Changes
• Viewing File Differences
• Making a Second Commit
• Reviewing the Commit History
• Viewing Commit Differences

Git Remotes:

• Introduction to Remote Repositories
• Making Changes to Cloned Repositories
• Overview of the Master Branch
• Pushing Changes to the Remote
• Viewing Individual Commits
• Commits and the Working Directory
• Switching to a Specific Commit
• Pulling From a Remote Repo
• Referring to the Most Recent Commit
• Git Branches:
• Introduction to Git Branches
• Switching Branches
• Pushing a Branch to a Remote
• Merging Branches
• Deleting Branches
• Checking Out Branches From the Remote
• Finding Differences Across Branches
• Branch Naming Conventions
• Branch History

Merge Conflicts:

• Introduction
• Aborting a Merge
• Resolving Conflicts
• Resolving Multi-Line Conflicts
• Resolving Multiple Conflicts
• Accepting Changes From Branch
• Ignoring Files
• Removing Cached Files