This repository majorly deals with a hands-on experiences for the following
- Data Cleaning: Use Pandas library for data manipulation and cleaning. Refer to the Pandas documentation for comprehensive guides and examples.
- Data Ingestion: Use Python scripts to automate data ingestion processes. Consider using Apache Airflow for orchestrating the data pipelines.
- Data Transformation: Utilize Pandas and SQL for data transformation tasks. You can also explore libraries like Dask for handling large datasets.
- Data Warehousing: Implement data warehousing using cloud solutions like Amazon RDS, Google BigQuery, or Snowflake. Refer to their respective documentations for setup and best practices.
- Data Visualization: Use libraries like Matplotlib, Seaborn, or Plotly for creating visualizations. For more interactive dashboards, consider using tools like Tableau or Power BI.
- Collaboration: Consider using version control systems like Git and platforms like GitHub or GitLab for collaborative work and code management.
- Interim Feedback: Schedule periodic reviews and feedback sessions with your instructor or peers to stay on track and address any challenges early.
- Advanced Analytics: Towards the end of the project, explore integrating machine learning models for predictive analytics to enhance your insights.
- Documentation and Reporting: Maintain thorough documentation of your processes and findings. Prepare clear and concise reports to present your results effectively.
Also we will be providing the datasets as well as the alternative codes inorder enhance your understanding.