Data Analysis with Python – Course Outline
Module 1: Introduction to Data Analysis
- What is Data Analysis?
- Importance of Python in Data Analytics
- Applications of Data Analysis
- Installing Python and Required Tools
- Introduction to Jupyter Notebook
Module 2: Python Basics
- Variables and Data Types
- Operators and Expressions
- Conditional Statements
- Loops (for, while)
- Functions and Modules
- Lists, Tuples, Dictionaries, Sets
Module 3: NumPy for Numerical Computing
- Introduction to NumPy
- Creating Arrays
- Array Operations
- Indexing and Slicing
- Mathematical Functions
- Statistical Operations
Module 4: Pandas for Data Analysis
- Introduction to Pandas
- Series and DataFrames
- Importing CSV and Excel Files
- Data Cleaning Techniques
- Handling Missing Values
- Filtering and Sorting Data
- GroupBy and Aggregation
Module 5: Data Visualization
- Introduction to Matplotlib
- Creating Line, Bar, Pie, and Scatter Charts
- Customizing Charts
- Introduction to Seaborn
- Heatmaps and Distribution Plots
- Data Storytelling Techniques
Module 6: Data Cleaning and Preparation
- Detecting Duplicate Data
- Handling Outliers
- Data Formatting
- Feature Engineering Basics
- Data Transformation Techniques
Module 7: Exploratory Data Analysis (EDA)
- Understanding Dataset Structure
- Descriptive Statistics
- Correlation Analysis
- Trend Analysis
- Creating Dashboards and Reports
Module 8: Working with Real-World Data
- Analyzing Sales Data
- Customer Data Analysis
- Social Media Data Analysis
- Business Insights Generation
- Mini Projects
Module 9: Introduction to SQL with Python
- Basics of SQL
- Connecting Python with Databases
- Running Queries using Python
- Importing Database Data into Pandas
Module 10: Basic Machine Learning for Data Analysis
- Introduction to Machine Learning
- Supervised vs Unsupervised Learning
- Linear Regression Basics
- Classification Basics
- Model Evaluation
Module 11: Final Project
- Real-Time Data Analysis Project
- Data Cleaning
- Visualization
- Insight Presentation
- Project Report Creation
Tools & Libraries Covered
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Seaborn
- SQL
- Scikit-learning