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 | Learning outcome (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Title | |||
25CBDA414 |
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)
1. Maindonald,John,Braun john ,”Data Analysis and Graphics Using R”, CambridgeUniversity 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
SUGGESTED READINGS:
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].
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/
Links:
[1] https://www.csit.iisuniv.ac.in/courses/subjects/data-analytics-using-r-2
[2] http://www.w3schools.com/
[3] https://www.csit.iisuniv.ac.in/academic-year/2025-26