DATA ANALYTICS USING R

Paper Code: 
CBDA 414
Credits: 
3
Periods/week: 
3
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 (COs).

Course Outcome (at course  level)

Learning and teaching strategies

Assessment Strategies

On completion of this course, the students will:

CO181. Apply analysis tool R to solve practical problems in a variety of disciplines.

CO182. Apply basic and advanced statistical techniques used in data science research.

CO183. Analyze large sets of data to gain useful business understanding.

CO184. Describe and demonstrate R methods for data visualisation 

CO185. Evaluate the statistical tests on real world data sets.

Approach in teaching: Interactive Lectures, Discussion, Demonstration

 

Learning activities for the students: Self-learning assignments, Quiz activity, Effective questions, presentation, flip classroom, project development

Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation, Individual and group projects and peer review.

 

Using R Studio:

1) 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)  Measures of Central Tendency

5) Measures of dispersion

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

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

 

ESSENTIAL READINGS: 
  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: 
  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

 

 JOURNALS

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

 

Academic Year: 
2023-24
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