Python for Data Analytics: A 3-Hour Crash Course
Unlock the power of data with Python in this fast-paced course designed for developers, covering essential libraries and techniques for data analysis and machine learning.
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Data Wrangling with Pandas
Unit 1: Pandas Fundamentals
Intro to Pandas
Creating DataFrames
Loading CSV Files
Loading Excel Files
DataFrame Basics
Unit 2: Data Selection and Filtering
Column Selection
Row Selection
Conditional Filtering
Advanced Filtering
Unit 3: Data Cleaning and Transformation
Handling Missing Data
Data Type Conversion
Adding New Columns
Renaming Columns
Applying Functions
Numerical Computing with NumPy
Unit 1: NumPy Fundamentals
Why NumPy?
Installing NumPy
NumPy Array Basics
Array Attributes
Array Creation Functions
Unit 2: Indexing, Slicing, and Reshaping
Basic Indexing
Slicing Arrays
Reshaping Arrays
Stacking Arrays
Splitting Arrays
Unit 3: Numerical Operations and Statistics
Element-wise Operations
Matrix Multiplication
Descriptive Statistics
Aggregation Functions
Random Number Generation
Data Visualization with Matplotlib and Seaborn
Unit 1: Matplotlib Fundamentals
Intro to Matplotlib
Anatomy of a Plot
Your First Line Plot
Scatter Plots
Bar Charts
Unit 2: Advanced Matplotlib
Subplots
Histograms
Legends & Annotations
Customizing Ticks
Saving Your Plots
Unit 3: Seaborn for Statistical Visualization
Intro to Seaborn
Distributions with Seaborn
Box Plots
Heatmaps
Scatterplot Matrix
Predictive Modeling with Scikit-learn
Unit 1: Regression Models with Scikit-learn
Intro to Scikit-learn
Linear Regression
Evaluating Regression
Train/Test Split
Feature Scaling
Unit 2: Classification Models with Scikit-learn
Logistic Regression
Evaluating Classifiers
Confusion Matrix
Support Vector Machines
Decision Trees
Unit 3: Model Selection and Tuning
Cross-Validation
Grid Search
Randomized Search
Model Persistence
Pipelines