Practical-I

Paper Code: 
STT 125
Credits: 
4
Contact Hours: 
120.00
Max. Marks: 
100.00
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

Course

Learning outcomes (at course level

Learning and teaching strategies

Assessment Strategies

 

Paper Code

Paper Title

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.

 

Essential Readings: 

·         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, 

Academic Year: 
2022-2023
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