Programming in R and Python

Course Objectives :                  Why this Subject?

1. Learn Fundamentals of R.
2. Learn the Basics of statistical data analysis with examples.
3. Learn the basis of python
4. Compile and visualize data using statistical functions.

Course Outcomes (CO)

• CO1 Impart the basic knowledge of R Fundamentals.
• CO2 How data is analysed and visualized using statistic functions
• CO3 Impart the basic knowledge of python programming
• CO4 Understand the loading, retrieval techniques of data.

UNIT-I

R Basics: Basic operations in R, Math operations in R, working with null values, Import & Export files in R, Data-frame, Joins, One-way and Two way tables, Arrays, Factors, R - Variables: Variable assignment, Data types of Variable, Deleting Variables - R Operators: Arithmetic Operators, Relational Operators, Logical Operator, Assignment Operators, Miscellaneous Operators, R Decision Making: if statement, if – else statement, if – else if statement, switch statement – R Loops: repeat loop, while loop, for loop - Loop control statement: break statement, next statement.

UNIT-II

R-Function : function definition, Built in functions: mean(), paste(), sum(), min(), max(), seq(), user-defined function, calling a function, calling a function without an argument, calling a function with argument values - R-Strings – Manipulating Text in Data: substr(), strsplit(), paste(), grep(), toupper(), tolower() - R Vectors – Sequence vector, rep function, vector access, vector names, vector math, vector recycling, vector element sorting - R List - Creating a List, List Tags and Values, Add/Delete Element to or from a List, Size of List, Merging Lists, Converting List to Vector - R Matrices – Accessing Elements of a Matrix, Matrix Computations: Addition, subtraction, Multiplication and Division- R Arrays: Naming Columns and Rows, Accessing Array Elements, Manipulating Array Elements, Calculation Across Array Elements - R Factors –creating factors, generating factor levels gl().

UNIT-III

Python Basics Objects and Functions, Identifiers, Variables and Datatypes, Operators, Python Flow, Function Arguments, Recursive functions ,Lambda, Exception Handling , Iterators, Generators and Decoders, Numpy and Pandas Numpy: Arrays, Vectorization, Boolean Indexing, Matrix multiplication, Tuple, Join/Merge data, Unicode strings etc. Pandas: Data Structure, Data frame, Reading data, Handling missing data.

UNIT - IV

Mathematics for Data science Probability, Statistics, Linear Algebra, Gradient Descent, Calculus for data science, ANOVA, Hypothesis testing, Data Visualization using GGPLOT2 and Matplotlib, Data Pre-processing, Data Transformation, Data Reduction, Feature Extraction. Univariate and Multi-variate analysis.

Textbook(s):

1. Introduction to Machine Learning with Python, A. C. Muller & S. Guido, O’Reilly
2. Data analytics with R by Dr. Bharti Motwani , wiley publication
3. Sandip Rakshit, R Programming for Beginners, McGraw Hill Education (India), 2017.

References: 

1. Python for R Users: A Data Science Approach by A. Ohri, Wiley India 
2. Python and R for the Modern Data Scientist ,Rick J Scavetta, Boyan Angelov, O’Reilly