Predictive Analytics with R Programming

(PEC-IV)

Course Syllabus

Lab Manual

Text Book:

1. K G Srinivas ,G M Siddesh  “Statistical programming in R”, Oxford Publications.

Reference Books:

1. Mark Gardener, Beginning R: The Statistical Programming Language, Wrox  
2. Y. Anchang Zhao: R and Data Mining: Examples and Case Studies . Elsevier in December 2012
3. Avril Coghlan :  A Little Book of R For Time Series

Course Schedule

Distribution of Hours in Unit – Wise

Unit	Topic	Chapters	Total No. of Hours
Text Book
I	Basics of R	Ch-1	9
II	Factors and Data Frames & Lists	Ch-2,Ch-3	9
III	Iterative Programming in R & Functions in R	Ch-4,Ch-5	9
IV	Apply Family in R	Ch-6	8
V	Data Interfaces & Statistical Applications	Ch-7,Ch-8	10
Total contact classes for Syllabus coverage	45
Tutorial Classes : Assignment Tests : (Before Mid1 & Mid2 Examinations)  Online Quiz:

Lecture Plan:

S. No.	Topic	No of Lecture Hours	Teaching Learning Process
UNIT-1
1	Introduction to R	1	Presentation  Video
2	R-Environment Setup (Install swirl package)	Swirl
3	Programming with R, Basic Data Types	1	Presentation Video
4	Creating and Naming Vectors, Vector Arithmetic	1	Presentation Video
5	Vector Subsetting	1	Presentation Video
6	Creating and Naming Matrices, Matrix Subsetting,	2	Presentation Video
7	Arrays	1	Presentation Video
8	Class Presentation	1
UNIT-2
1	Introduction to Factors, Factor Levels	1	Presentation Video
2	Summarizing a Factor  Ordered Factors ,Comparing Ordered Factors	1	Presentation Video Video
3	Introduction to Data Frame, Subsetting of Data Frames	1	Presentation Video
4	Extending Data Frames, Sorting Data Frames	1	Presentation Video Video
5	Creating a Named List, Accessing List Elements, Manipulating List Elements, Merging Lists, Converting Lists to Vectors	1	Presentation Video Video
6	Conditionals and Control Flow: Relational Operators, Relational Operators and Vectors	2	Presentation Video
7.	Logical Operators, Logical Operators and Vectors, Conditional Statements	2	Presentation Video  Video
UNIT-3
1	Iterative Programming in R:  While Loop, For Loop, Looping Over List	2	Presentation
2	Functions in R: Introduction, Writing a Function in R, Nested Functions	2	Presentation
3	Function Scoping, Recursion	1	Presentation
4	Loading an R Package, Mathematical Functions in R	1	Presentation
5	Cumulative Sums and Products, Calculus in R	2	Presentation
6	Input and Output Operations.	1	Presentation
Unit-IV
1	Apply Family of Functions in R: Using Apply(), Using Lapply(), Using Sapply().	2	Presentation
2	Using Tapply(), Split Function, Using Mapply()	2	Video-Tapply Video-Mapply
3	Charts and Graphs : Pie Chart,Chart Legend, 3D Pie Chart, Bar Chart,	2	Video-Pie & Bar Chart Reference Website
4	Box Plot, Histogram, Line Graph: Multiple Lines in Line Graph, Scatter Plot.	2	Reference Website
UNIT-5
1	Data Interfaces: CSV Files, Syntax, Importing a CSV File,	1	Video Reference Website
2	Excel Files: Syntax, Importing an Excel file.	1	Material
3	Binary Files: Syntax, XML Files, Web Data, Databases	2	Binary Files  XML Material Web Material Database
4	Statistical Applications: Basic Statistical Operations, Linear Regression Analysis	2	Material  Linear Regression Video https://www.youtube.com/watch?v=2Sb1Gvo5si8 Multiple Linear Regression Video https://www.youtube.com/watch?v=q1RD5ECsSB0 Outliers Video https://www.youtube.com/watch?v=tOAJi9-qDm0
5	Chi-Squared Goodness of Fit Test, Chi-Squared Test of Independence	2	Material https://www.youtube.com/watch?v=1RecjImtImY
6	Multiple Regression, Time Series Analysis.	2	Material   https://www.youtube.com/watch?v=iTq6fNfi4Rs
Total contact classes for Syllabus coverage: 45

IV Year B.Tech. CSE – I Sem                                                           L       T / P / D     C

                                                                                                                      0           0            1

PREDICTIVE ANALYTICS WITH R PROGRAMMING LAB

(PROFESSIONAL ELECTIVE- IV LAB) (A57221)

Prerequisites : C / Python programming language

Course Objectives :

1. To learn and apply R programming
2. To Get exposure on various R data types
3. To apply appropriately the iterative programming concepts
4. To apply visualization tools
5. Understand and apply regression models for Predictive Analytics

Course Outcomes :

Student will able to :

1. Install and Explore R environment
2. Apply appropriate data types and operators
3. Apply iterative programming concepts using various R functions
4. Visualize data insights using data visualization
5. Analyze data with Regression Model.

List of Programs:

1. Installation and Environment set up R and Rstudio
2. Experiments on Vector Arithmetic operations
3. Experiments on Matrices operations
4. Experiments on Arrays
5. Experiments on Factors
6. Experiments on Data Frames
7. Experiments on List operations
8. Experiments on Logical operations and Conditional Statements
9. Experiments on looping over lists
10. Experiments on nested functions and function scoping
11. Experiments on mathematical functions
12. Experiments on statistical functions in R
13. Experiments on lapply, sapply and apply functions.
14. Experiments on data visualization using charts and graphs
15. Experiments on Predictive Analytics using regression models