President Award Click here CAREER Click here
President Award Click here CAREER Click here
ADMISSION ENQUIRY - 2024
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:
|
||||||||||||
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 |
||||||||||||
|
||||||||||||
Practical Content |
||
|
||
Suggested Practical List |
||
practicals will be based on following criteria
Suggested Softwares: R Programming, Excel. SPSS |
||
Mooc Course |
||
Course Name: Data Science for Engineers |
||
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:
|