This repository contains comprehensive materials for a Data Analytics course, including lectures, examples, assignments, and resources. The materials are designed to provide hands-on experience with various data analytics concepts and techniques.
Data-Analytics/
├── lectures/ # Course lecture materials
│ └── sql/ # SQL-specific lecture materials
├── assignments/ # Student assignments and tests
├── examples/ # Example code and implementations
├── resources/ # Additional resources and datasets
├── solutions/ # Solution sets
└── tools/ # Utility scripts and tools
The course covers the following key areas:
- Loading data from various sources
- Creating effective visualizations
- Using different visualization libraries
- Descriptive and predictive analytics
- Exploratory Data Analysis (EDA)
- Confirmatory Data Analysis
- A/B Testing
- Pandas library and DataFrames
- Data reviewing and summary statistics
- Handling missing values
- Grouping data and pivot tables
- Table joins and relationships
- Basic plots (scatter, line, bar, box)
- Advanced visualizations
- Geospatial data visualization
- Distribution visualization
- Graph and tree visualization
- Matrix visualization
- SQL fundamentals
- Working with SQL databases
- SQL visualization
- Database optimization
- Dimensionality reduction
- Clustering techniques
- Data segmentation
- Performance optimization
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Clone the repository:
git clone https://github.com/yourusername/Data-Analytics.git
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Follow the setup instructions in SETUP.md
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Install the required dependencies:
pip install -r requirements.txt
If you're a teaching assistant or contributor, please read our CONTRIBUTING.md file for guidelines on how to contribute to this repository.
This project is licensed under the MIT License - see the LICENSE file for details.
For questions or support:
- Create an issue in the repository
- Contact the course coordinator
- Join the discussion forum
See CHANGELOG.md for a list of changes and updates to the repository.