Data Mining With Python (Practical)

Paper Code: 
24DAC333
Credits: 
4
Periods/week: 
2
Max. Marks: 
100.00
Objective: 

This course will be based on theory paper DAC 331. This course will enable students  to apply and practice the concepts of Data mining.

 

Course Outcomes: 

Course

Course outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

 

24DAC333

Data Mining With Python

(Practical)

CO13. Apply python libraries like pandas to create data frames for managing data

CO14. Implement data mining techniques using scikit libraries and other related libraries of python

CO15. Create visualizations using matplotlib library

CO16. Evaluate results generated using Python libraries

CO17. Generate a report based on analysis drawn from data mining techniques

CO18. Contribute effectively in course-specific interaction

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, Practical Assignments, Individual and group projects

 

The practical covers the following topics:

  1. Review of Python
  2. Overview of Python tools for Data Analysis
  3. Python has for data cleaning and processing -- pandas
  4. Data exploration & analysis libraries for Data Science:Pandas, Numpy
  5. Open-source software for mathematics, science, and engineering:SciPy
  6. Data visualization/ plotting library: Matplotlib
  7. Machine learning library: scikit-learn

 

ESSENTIAL READINGS: 
  1. Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers, Third Edition.
  2. Mohanty, Soumendra, “Data Warehousing: Design, Development and Best Practices”, Tata McGraw Hill, 2006
REFERENCES: 

Suggested Readings:

  1. W. H. Inmon, “Building the Data Warehouse”, Wiley Dreamtech India Pvt. Ltd., 4th  Edition, 2005

e-Resources:

  1. https://www.slideshare.net/
  2. https://nptel.ac.in/courses/106106222
  3. https://spoken-tutorial.org/??/
  4. www.kaggle.com

Journals:

1.   Journal of the Brazilian Computer Society, SpringerOpen

2.   Journal of Internet Services and Applications, SpringerOpen

 

Academic Year: 
2024-25
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