Data Mining Practices Lab’s detailed syllabus for Cloud Computing & Big Data (CC) for the 2021 revision curriculum has been taken from the SITTTRs official website and presented to the Cloud Computing & Big Data (CC) students. For course code, course name, number of credits for a course and other scheme-related information visit the full semester subjects post below.
For the Cloud Computing & Big Data 5th Sem scheme and its subjects, visit the Cloud Computing & Big Data (CC) 5th Sem 2021 regulation scheme. For the Professional Elective-II scheme and its subjects, refer to the Cloud Computing & Big Data (CC) Professional Elective-II syllabus scheme. The detailed syllabus of the data mining practices lab is as follows.
Course Objectives:
- Understand data and data preprocessing
- Gain knowledge about data mining techniques
Course Outcomes:
On completion of the course, students will be able to:
- Implement Matrix operations and linear algebra on matrices using python
- Practice loading and Preprocessing data using python
- Demonstrate Visualization of data using python
- Use data mining in Python
Module 1:
- Implement multi-dimensional arrays and find its shape and dimension
- Reshape and flatten data in the array
- Apply indexing and slicing on array
- Practice Dot and matrix product of two arrays
- Compute the multiplicative inverse of a matrix
Module 2:
- Loading data from CSV file
- Compute the basic statistics of given data – shape, no. of columns, mean
- Practice Preprocessing techniques for missing data, redundancy, null value, and noisy data
- Splitting a data frame on values of categorical variables
Module 3:
- Practice data split for training & testing and visualize the data
- Perform various graphical representations of data- Line Plot, Scatter Plot, Box plot, Point plot, Count plot, Violin plot, Swarm plot, and Bar plot
Module 4:
- Practice web scraping
- Open-ended projects- Design and develop Python programs to analyze social media data or e-commerce data
Text Books:
- J. Han, M. Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann.
- M. Kantardzic, “Data mining: Concepts, models, methods and algorithms, John Wiley &Sons Inc.
Reference Books:
- M. Dunham, “Data Mining: Introductory and Advanced Topics”, Pearson Education
Online Resources
- https://www.dataquest.io/blog/sci-kit-learn-tutorial/
- https://www.ibm.com/support/knowledgecenter/en/SS3RA7_sub/modeler_tutorial_ ddita/modeler_tuto rial_ddita-gentopic1.html
- https://archive.ics.uci.edu/ml/datasets.php
For detailed syllabus of all other Cloud Computing & Big Data subjects, 2021 revision curriculum, visit Cloud Computing & Big Data (CC) 5th Sem subject syllabuses for 2021 revision.
To see the syllabus of all other branches of the diploma 2021 revision curriculum, visit the SITTTR diploma all branches syllabus..
To see the results of Cloud Computing & Big Data of diploma 2021 revision curriculum, visit SITTTR diploma results..
For all Cloud Computing & Big Data academic calendars, visit the Cloud Computing & Big Data all semesters academic calendar direct link.