Course Outline
- 1. Introduction to Data Analysis and Python Basics
- What is Data Analysis?
- The Role of a Data Analyst
- Python Basics: Variables, Data Types, and Control Structures
- Python Libraries for Data Analysis (Pandas, NumPy, Matplotlib)
- 2. Data Wrangling and Cleaning with Python
- Intro to Data Wrangling & Cleaning
- Handling Missing Data in Pandas
- Data Transformation Techniques
- Merging and Joining DataFrames
- Detecting and Handling Outliers
- Data Normalization and Standardization
- 3. Exploratory Data Analysis (EDA)
- Intro to EDA
- Descriptive Statistics in Python
- Correlation and Covariance
- Analyzing Data Distributions
- 4. Data Visualization Techniques
- Introduction to Data Visualization
- Matplotlib Fundamentals
- Data Visualization with Seaborn
- 5. Advanced Data Manipulation with Python
- GroupBy Operations in Pandas
- Creating and Analyzing Pivot Tables
- Data Aggregation and Resampling
- Time Series Data Analysis
- Week 6: Statistical Analysis and Hypothesis Testing
- Introduction to Statistics
- Understanding Probability Distributions
- Hypothesis Testing with Python
- ANOVA and Chi-Square Tests
- Week 7: Introduction to Machine Learning
- Introduction to Machine Learning
- Building a Linear Regression Model
- Classification Techniques
- Evaluating Model Performance
- Week 8: Practical Projects
- Data Visualization: E-commerce Customer Behavior Analysis
- Statistical Analysis: Housing Market Analysis
- Machine Learning: Credit Card Fraud Detection
- Time Series: Stock Portfolio Analysis
Data Visualization: E-commerce Customer Behavior Analysis
Week 8: Practical Projects
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