Data Science and Modelling using R

GANPAT UNIVERSITY

FACULTY OF ENGINEERING & TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (CBA/BDA/CS)

Semester

V

Version

1.0.0.1

Effective from Academic Year

2022-23

Effective for the batch Admitted in

June 2020

Subject code

2CSE50E27

Subject Name

DATA SCIENCE & MODELING USING R

Teaching scheme

Examination scheme (Marks)

(Per week)

Lecture(DT)

Practical(Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit

3

0

1

0

4

Theory

40

60

100

Hours

3

0

2

0

5

Practical

30

20

50

Pre-requisites:

Probability & statistics, Probability distribution

Learning Outcome:

After completion of the course, student will be able to:

• Learn the fundamentals of data analytics and the data science stream
• Apply statistical methods, regression techniques and related algorithms to both large and small data sets in R Programming for prediction.
• Demonstrate knowledge of statistical data analysis techniques utilized in decision making.
• Implement  hypothesis testing, various algorithms using various software platforms.

Theory syllabus

Unit

Content

Hrs

1

Descriptive and Inferential Statistics

Descriptive Statistics, Inferential Statistics through hypothesis tests

8

2

ANOVA & correlation coefficient

ANOVA (Analysis of Variance), Coefficient of correlation,

7

3

Optimization

Introduction to optimization,Constrained optimization, Unconstrained optimization,Linear optimization, Gradient-based methods

8

4

Regression

Differentiating algorithmic and model based frameworks, Regression: Ordinary Least Squares,Ridge Regression, Lasso Regression, Logistic Regression K Nearest Neighbours,

8

5

Fundamentals of R:

R Data Structures, Common Vector operations, Matrices, Arrays, Lists and Data Frames.

7

6

Reproducible Research Using R

Reproducible Research using R and Rstudio (knitr, rmarkdown, bookdown, interactive document, shiny presentation, shiny web application)

7

Self-Study Topics

• Two-way ANOVA.

Practical Content

• Practical’s are to be designed in such a way that students must help students understand various types of regression models.
• The labs must emphasize on practical aspects of clustering, rule mining, hurdles dusting big data analytics.
• The labs must focus on active learning of practical concepts so as to achieve the course outcome with effectiveness.

Suggested Practical List

 practicals will be based on following criteria

• Initial practical will introduce programming tools and its feature/power of implementation of problems.
• Practical related to inferential statistics and various hypothetical tests are implemented.
• Practical related to advanced topics like regression, clustering and classification are implemented with various aspects.
• Covered topics will create a concrete platform to advance techniques of machine learning, deep learning, reinforcement learning.

Suggested Softwares: R Programming, Excel. SPSS

   Mooc Course

Course Name:  Data Science for Engineers

   Link: https://onlinecourses.nptel.ac.in/noc20_cs72/course

Text Books

1

Hastie, Trevor, et al. The elements of statistical learning.  springer,

Reference Books

1

Montgomery, Douglas C., and George C. Runger. Applied statistics and probability for engineers. John

Wiley & Sons, 2010

2

Bekkerman et al. Scaling up Machine Learning

3

Research Methodology Methods & Techniques, C. R. Kothari, Second Edition,2009

4

Vincent Granville, Developing Analytic Talent: Becoming a Data Scientist, wiley, 2014.

Course Outcomes:

COs

Description

CO1

Learn the fundamentals of data analytics and the data science stream

CO2

Apply statistical methods, regression techniques and related algorithms to both large and small data sets in R Programming for prediction.

CO3

Demonstrate knowledge of statistical data analysis techniques utilized in decision making.

CO4

Implement  hypothesis testing, various algorithms using various software platforms.

Mapping of CO and PO: