Data Analytics Using R

Paper Code: 
24CBDA414
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

The course will enable the students to

1. To study and learn the concepts of predictive analysis with the tool R.

2. To develop their skills on data analysis on large data sets using R.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course

Code

Course

Title

24CBDA414

Data Analytics Using R

(Practical)

 

CO217. Apply analysis tool R to solve practical problems in a variety of disciplines.

CO218. Apply basic and advanced statistical techniques used in data science research.

CO219. Analyse large sets of data to gain useful problem understanding.

CO220. Implement R methods for data visualisation 

CO221. Evaluate the statistical tests on real world data sets.

CO222.Contribute effectively in course-specific interaction

Approach in teaching: Interactive Lectures, Discussion, Demonstration

 

Learning activities for the students: Self-learning assignments, Quiz activity, Effective questions, presentation.

Class test, Semester end examinations, Quiz, Assignments, Presentation.

 

Using R Studio:

1) Introduction to R: Logical Arguments, Missing Values, Characters, Factors and Numeric, Help in R, Vector to Matrix, Matrix Access

2) Data Frames, Data Frame Access, Basic Data Manipulation Techniques, Usage of various apply functions – apply, lapply, sapply and tapply, Outliers treatment. 

3) Charts (Bar, Pie, Histogram)

4)  Exploratory Analysis: Measures of Central Tendency, Measures of dispersion

5) Discrete Probability Distributions: Binomial, Poisson, Continuous Probability Distribution: Normal Distribution.

6) Parametric tests (applications of chi-square test, t test and F test)  

 

ESSENTIAL READINGS: 
SUGGESTED TEXT BOOKS
  1. Maindonald,John,Braun john ,”Data Analysis and Graphics Using R”, Cambridge University Press,2007
  2. Gardener Mark,”Beginning R: The Statistical Programming Language “ Wiley India Pvt. Ltd. 2015
  3. Srivasa K.G., Siddesh G M,Shetty,” Statistical Programming in R”, Oxford University Press 2017
  4. Business Statistics: Naval Bajpai, Pearson

 

REFERENCES: 
SUGGESTED REFERENCE BOOKS
  1. Braun W J, Murdoch D J (2007): A First Course in Statistical Programming with R. Cambridge University Press. New York
  2. Rakshit, Sandip(2007):R Programming for Beginners
  3. Cotton, Richard(2016) Learning R: A Step-by-Step Function Guide to Data Analysis

e RESOURCES

  1. R , w3school,http://www.w3schools.com.
  2. NOC: Essentials of Data Science With R Software 1: Probability and Statistical Inference, IIT Kanpur: https://nptel.ac.in/courses/111104146
  3. R, Spoken Tutorial: https://spoken-tutorial.org/

 JOURNALS

  1. Journal of the Brazilian Computer Society, SpringerOpen, https://journal-bcs.springeropen.com/
  2. Journal of Internet Services and Applications, SpringerOpen: https://jisajournal.springeropen.com/

 

Academic Year: 
2024-25
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