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DATA MINING WITH PYTHON
This paper will be based on theory paper DAC 331. Students will be able to apply and practice the concepts of Data mining.
Course Outcomes (COs):
Course outcome (at course level) | Learning and teaching strategies | Assessment Strategies |
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Students will be able to: CO11. Apply python libraries like pandas to create data frames for managing data CO12. Implement data mining techniques using scikit libraries and other related libraries of python CO13. Create visualizations using matplotlib library CO14. Evaluate results generated using Python libraries CO15. Generate a report based on analysis drawn from data mining techniques | Approach in teaching: Interactive Lectures, Discussion, Demonstrations, Group activities, Teaching using advanced IT audio-video tools
Learning activities for the students: Effective assignments, Giving tasks.
| Assessment Strategies Class test, Semester end examinations, Quiz, Practical Assignments, Individual and group projects
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The practical covers the following topics:
- Review of Python
- Overview of Python tools for Data Analysis
- Python has for data cleaning and processing -- pandas
- Data exploration & analysis libraries for Data Science: Pandas, Numpy
- Open-source software for mathematics, science, and engineering: SciPy
- Data visualization/ plotting library: Matplotlib
- Machine learning library: scikit-learn
