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Practical-I
Objective: This paper is designed so that the student get familiar with statistical software for solving the problems based on various mathematical operations and also how to deal and analyse the probability of different data.
Students will be able to
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Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
Paper Title |
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STT-125 |
Practical-I |
CO 21: Write python programs using programming and looping constructs to tackle any decision-making scenario.
CO 22: Identify and resolve coding errors in a program and design and develop real life applications using python.
CO 23: Write python programs using programming and looping constructs to tackle any decision-making scenario.
CO 24: Generate statistical problems graphically and interpret them. CO 25: Handle data in tabular form, fitting and generate results from them.
CO 26: Learn the knowledge of statistical software and interpretation from them. Also helps them in further enhancement in their career |
Approach in teaching: Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students: Assignments Seminar Presentation Subject based Activities |
Software based Assignments Individual Presentation Class Test |
1. Data frame, data types in R
2. Call directories in R
3. Indexing and slicing of data in R
4. Creating matrices and their operation in R
5. Merging , importing and exporting of data in R
6. List and its operation in R
7. Graphical representation in R
8. Table manipulation in R
9. Descriptive statistics in R
10. Conditional statement in R
11. Construct multiple plot and sub plots in python
12. Data types in python
13. Panda and numpy library in Python
14. Input output function in python
15. String function in python
16. List function in python
17. For loop in python
18. Descriptive statistics in python
19. Array in python
20. Basic matrix operation in python
Note: Practical exercises will be conducted on computer by using Python/R.
· Madhavan (2015): Mastering Python for Data Science Packt
· McKinney (2017). Python for Data Analysis. O’ Reilly Publication
· Curtis Miller(2015) ”Hands-On Data Analysis with NumPy and Pandas",Packt,
