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Python Data Analytics Course
Learn industry-relevant Python skills with hands-on projects, real datasets, and expert guidance. This course covers Python end-to-end analysis.
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Course Syllabus
Module 1: Python Basics
- Introduction to Python
- Python Installation and IDE Setup
- Variables and Data Types
- Operators
- Conditional Statements (if, if-else, nested if)
- Loops (for, while)
- Functions and Lambda Functions
- List, Tuple, Set, Dictionary
- String Manipulation
- Exception Handling
- File Handling
Module 2: Python for Data Handling
- Introduction to Libraries: NumPy, Pandas
- Arrays, Indexing, Slicing, Mathematical Operations
- Pandas: Series and DataFrames
- Data Import/Export (CSV, Excel)
- Data Selection and Filtering
- Handling Missing Data & Cleaning
- GroupBy, Pivot Tables, Merging, Joining
Module 3: Data Visualization
- Matplotlib & Seaborn Basics
- Line, Bar, Pie, Scatter, Histogram
- Customizing Graphs: Labels, Legends, Colors
- Advanced Seaborn Charts
- Correlation Matrix & Heatmaps
Module 4: Exploratory Data Analysis (EDA)
- Understanding Data & Summary Statistics
- Outlier Detection
- Data Distribution Analysis
- Handling Duplicates
- Data Transformation Techniques
- Encoding Categorical Variables
Module 5: Working with Real-Time Datasets
- Importing Data from APIs
- Web Scraping Basics (Optional)
- Time Series Data Handling
- Case Studies with Real Datasets
Module 6: Statistical Analysis (Optional)
- Descriptive Statistics
- Probability Basics
- Hypothesis Testing
Module 7: Project Work
- End-to-End Data Analysis Project
- Data Cleaning & EDA
- Visualization
- Insights and Recommendations