Python for Data Analysis Beginners
Unlock the power of data with Python: a beginner-friendly course to master data analysis essentials.
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Introduction to Python for Data Analysis
Unit 1: Why Python for Data Analysis?
Data Analysis Defined
Python's Data Prowess
Python vs. Spreadsheets
Unit 2: Setting Up Your Python Environment
Anaconda Navigator
Launching Jupyter
Your First Notebook
Unit 3: Python Fundamentals
Variables Explained
Data Type Basics
Math Operators
Comparison Operators
Logical Operators
Unit 4: Writing and Executing Python Scripts
Hello, World!
Input from Users
Scripting with Variables
Python Data Structures
Unit 1: Lists in Python
Intro to Python Lists
Creating Python Lists
Accessing List Elements
Modifying Lists
List Operations
Unit 2: Tuples in Python
Intro to Python Tuples
Creating Python Tuples
Accessing Tuple Elements
Tuple Operations
Tuple Immutability
Unit 3: Dictionaries and Sets
Intro to Dictionaries
Creating Dictionaries
Accessing Dictionary Data
Intro to Sets
Creating Sets
NumPy Fundamentals
Unit 1: Introduction to NumPy Arrays
Why Use NumPy?
Installing NumPy
Importing NumPy
Unit 2: Creating NumPy Arrays
Array from Python List
Zeros and Ones
Arrays with `arange`
Arrays with `linspace`
Random Number Arrays
Unit 3: Array Indexing and Slicing
Basic Indexing
Array Slicing
Boolean Indexing
Unit 4: Array Operations and Broadcasting
Element-wise Operations
The Magic of Broadcasting
Math Functions
Pandas DataFrames and Series
Unit 1: Introduction to Pandas
What is Pandas?
Pandas Series
Pandas DataFrames
Unit 2: Creating DataFrames
From Lists to DataFrame
Dicts to DataFrames
Importing CSV Files
Reading Other File Types
Unit 3: Indexing and Selection
Column Selection
Row Selection
Slicing DataFrames
Boolean Indexing
Unit 4: Basic Data Exploration
First Look: Head and Tail
DataFrame Info
Descriptive Statistics
Value Counts
Data Cleaning and Transformation with Pandas
Unit 1: Handling Missing Data
Identifying Missing Data
Dropping Missing Data
Filling Missing Data
Imputation Strategies
Unit 2: Data Cleaning Techniques
Removing Duplicates
Correcting Data Types
Renaming Columns
String Manipulations
Unit 3: Applying Functions to DataFrames
Apply: Row-wise
Apply: Column-wise
Element-wise with Map
Applymap for DataFrames
Unit 4: Data Aggregation and Grouping
The Power of Groupby
Aggregation Functions
Transform and Filter
Data Visualization with Matplotlib
Unit 1: Getting Started with Matplotlib
Matplotlib Intro
Anatomy of a Plot
Your First Plot
Pyplot vs. OO Interface
Figures and Axes
Unit 2: Basic Plot Types
Line Plots
Scatter Plots
Bar Charts
Histograms
Pie Charts
Unit 3: Customizing Plots
Titles and Labels
Legends
Annotations
Axis Limits and Ticks
Colormaps
Advanced Data Visualization with Seaborn
Unit 1: Introduction to Seaborn
Seaborn: A Visual Upgrade
Loading Sample Datasets
Seaborn's Plotting Styles
Unit 2: Distribution Plots
Histograms with Seaborn
Kernel Density Plots
Box Plots
Violin Plots
Unit 3: Relational Plots
Scatter Plots
Line Plots
Relational Plots with Hue
Relational Plots with Size
Unit 4: Categorical Plots
Bar Plots
Count Plots
Categorical Box Plots
Categorical Violin Plots
Unit 5: Customization and Styling
Color Palettes
Data Input and Output
Unit 1: Reading Data with Pandas
Reading CSV Files
CSV: Index as Column
Reading Excel Files
Excel: Header and Index
Reading JSON Files
Unit 2: Writing Data with Pandas
Writing to CSV Files
CSV: Indexing Options
Writing to Excel Files
Excel: Formatting
Writing to JSON Files
Unit 3: Advanced I/O Techniques
JSON Orient Options
Handling Large Datasets
Working with URLs
Compression
Pickling DataFrames