Syllabus – R Language & �Big Data Analytics Lab

MIT-WPU TYBBA BA

R Programming

TYBBA BA Prof. Suryaakant Karande

1. Introduction (6 Lectures):

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Introduction to Algorithm and programming concepts. What is R? – Why R? – Advantages of R over Other Programming Languages – R Studio: R command Prompt, R script file, comments – Handling Packages in R:Installing a R Package, Few commands to get started: installed.packages(), package Description(), help(), find.package(), library() - Input and Output – Entering Data from keyboard – Printing fewer digits or more digits – Special Values functions : NA, Inf and –inf. 

2. R Data Types (10 Lectures):

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R Data Types: Vectors, Lists, Matrices, Arrays, Factors, Data Frame – R - Variables: Variable assignment, Data types of Variable, Finding Variable ls(), 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. Solving problems from the Assignment sheet. 

3. Functions in R-Language (9 Lectures):

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 - 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(). String functions : grep(), nchar() , paste(), sprintf(), substr(), strsplit(), regex() gregexpr(), toupper(), tolower(), paste()

4. Creating Data Frames and visualization of Data (10 Lectures):

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Data Frames –Create Data Frame, Data Frame Access, Understanding Data in Data Frames: dim(), nrow(), ncol(), str(), Summary(), names(), head(), tail(), edit() functions - Extract Data from Data Frame, Expand Data Frame: Add Column, Add Row - Joining columns and rows in a Data frame rbind() and cbind() – Merging Data frames merge() – Melting and Casting data melt(), cast(). Loading and handling Data in R: Getting and Setting the Working Directory – getwd(), setwd(), dir() File Handling in R language, -CSV Files - Input as a CSV file, Reading a CSV File, Analyzing the CSV File: summary(), min(), max(), range(), mean(), median(), apply() - Writing into a CSV File – R -Excel File – Reading the Excel file.

5. Descriptive Statistics using R (10 Lectures)

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Descriptive Statistics: Data Range, Frequencies, Mode, Mean and Median: Mean Applying Trim Option, Applying NA Option, Median - Mode - Standard Deviation – Correlation - Spotting Problems in Data with Visualization: visually Checking Distributions for a single Variable - R –Pie Charts: Pie Chart title and Colors – Slice Percentages and Chart Legend, 3D Pie Chart – R Histograms – Density Plot - R –Bar Charts: Bar Chart Labels, Title and Colors. Line Chart, Scatterplot, Developing graphs, Box Plot, Drawing line, circle, rectangle, triangle using R language.

Big Data Analytics Lab

TYBBA BA MIT WPU - Prof. Suryaakant Karande

1. Introduction (9 Lectures):

Introduction to Big Data: Definition and characteristics, Importance in decision-making, IoT, Real Time data streaming.

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2. Big Data Technologies (12 Lectures):

Big Data Technologies: Apache Hadoop, Apache Spark, Apache Cassandra, Apache Flink, SAS Analytics Splunk and Databricks. Distributed file systems.

3. Preprocessing and Analysis in Big data: (9 Lectures):

Data Preprocessing: Cleaning and transforming data, Handling missing values, Feature engineering.Statistical Analysis in Big Data: Descriptive statistics, Inferential statistics, Hypothesis testing.

4. Machine Learning and Real-Time Analytics (10 Lectures):

Machine Learning for Big Data: Regression and classification, Clustering and dimensionality reduction, Model evaluation and validation. Real-time Analytics: Streaming data processing Real-time dashboards. 

5. Industry Trends (5 Lectures)

Big Data in Industry: Case studies across different sectors, Challenges and opportunities.

Thank You

Prof. Suryakant Karande