COURSE DETAILS

The R Programming course offered at NIMACT is having the latest curriculum related to statistical computing, data analysis and data visualization, with the view to preparing data and research professionals for catering to the research, education, healthcare, agriculture, finance and analytics sector in India. The curriculum and the study materials are revised and updated every six months for the inclusion of the latest R packages, analysis techniques and AI assisted learning methods. Accordingly assistance and advice are sought on a continuing basis from the industry for knowing and assessing their requirements.

R is different from other programming languages. Most languages were built for software and later adjusted for data. R was built for data from the first day. This is why a task like reading a survey file, removing missing entries, running a regression and drawing a publication quality chart can be done in R in a few lines, while other languages need much more setup. For anyone whose work is measurement, comparison, testing and reporting, R is the shortest road from raw data to a final result.

R is also the strongest language in the academic and research world. University departments, research institutes, pharma and clinical trial teams, agriculture research centres, survey agencies and government statistical bodies use R heavily. Thesis work, journal papers and research reports across the world carry graphs made in ggplot2. So this course is valuable not only for a job seeker, but also for a student who has to analyse his own research data and defend it.

NIMACT teaches every programming language on the model of AI > Programming Language (Coding). The first stage is AI. Tools like ChatGPT, Claude AI, Gemini and Copilot are used to explain a statistical concept such as p value, regression or standard deviation in simple language, to explain the meaning of an error message, to describe what a package function is doing, and to give extra practice datasets and questions. The second stage is the coding itself. Every script, every chart and every analysis is written, run and understood by the student with his own hands. This rule is followed very strictly in data work, because a wrong analysis produces an output that looks perfectly clean, and only the student who understands his own code can catch that mistake before it reaches a report.

So many opportunities after this course. Research institutes, universities, hospitals, pharma companies, agriculture centres, survey agencies, banks, NGOs and analytics firms recruit people who can handle statistical data, and the job profile will be Data Analyst, Statistical Analyst, Research Assistant, Business Analyst, Survey Data Executive, Reporting Executive, Academic Research Support etc. Freelance and part time work is also available in thesis data analysis, survey processing and report making.

Who Should Attend: Students and scholars of M.Sc., M.A., MBA, Ph.D., Agriculture, Economics, Statistics, Psychology, Sociology and Life Sciences who have to analyse their own research data. People working in survey, MIS, quality control, research and reporting who want to advance their career. Data enthusiasts who want a strong statistical base along with programming. Teachers and academicians who guide research work, as well.

ELIGIBILITY CRITERIA: Open to all. There is no minimum qualification for this course. Basic maths and computer handling is helpful, but the required basics are covered at the beginning of the course.

Mode: Hybrid (Offline + Online + Live Doubt Session)

SYLLABUS:
To get a better idea about the course structure, let us go through a list of important subjects present in this program. Note - Only the important points have been mentioned.

• Data and Statistics Concept, ICT & AI
• Artificial Intelligence (AI) as a Learning Support - ChatGPT, Claude AI, Gemini, Copilot, Grok, DeepSeek, Perplexity
• AI for Concept Explanation, Code Reading, Error Meaning and Practice Datasets
• Introduction to R Language
• History, Features and Scope of R
• R vs Python vs Excel for Data Work
• Installation of R and RStudio
• RStudio Screen - Console, Script, Environment, Plots, Help
• Working Directory, Workspace and Script Saving
• CRAN, Package Installation, Loading and Updating
• R Basics
• Variable, Assignment and Naming Rules
• Data Types - Numeric, Character, Logical, Integer, Complex
• Operators - Arithmetic, Relational, Logical, Assignment
• Type Checking and Type Conversion
• Built in Functions and Help System
• Data Structures in R
• Vector - Creation, Indexing, Slicing and Operations
• Matrix - Creation, Row and Column Operations, Matrix Algebra
• List - Creation, Nested List and Access
• Factor - Levels, Ordered Factor and Categorical Data
• Data Frame - Creation, Indexing, Adding and Removing Columns
• Array and Multi Dimensional Data
• Control Statements
• if, if else, else if, ifelse function
• switch statement
• Looping Statements
• for loop, while loop, repeat loop
• break and next
• Vectorization and Why Loops are Avoided in R
• Functions in R
• Function Creation, Argument and Return Value
• Default Argument and Variable Argument
• Local and Global Variable
• Apply Family - apply, lapply, sapply, tapply, mapply
• Recursive Function
• Project
• Data Import and Export
• Reading CSV, Excel, Text and JSON Files
• Reading Data from Database and Web
• readr, readxl and data.table Packages
• Exporting Results to CSV, Excel and PDF
• Data Cleaning and Preparation
• Missing Value Detection and Treatment
• Duplicate Removal and Data Type Correction
• Outlier Detection and Handling
• Data Filtering, Subsetting and Sampling
• Data Manipulation
• dplyr - select, filter, mutate, arrange, summarise, group_by
• Pipe Operator and Chained Operations
• tidyr - gather, spread, pivot_longer, pivot_wider
• Merging and Joining Data Sets
• String Handling with stringr
• Date and Time Handling with lubridate
• Descriptive Statistics
• Mean, Median, Mode, Range, Quartile
• Variance, Standard Deviation and Coefficient of Variation
• Frequency Table and Cross Tabulation
• Summary Function and Data Profiling
• Probability and Distribution
• Probability Concept and Random Number Generation
• Normal, Binomial and Poisson Distribution
• Sampling and Sampling Distribution
• Inferential Statistics
• Hypothesis Testing Concept, Null and Alternate Hypothesis
• p Value, Confidence Interval and Significance Level
• t Test - One Sample, Two Sample, Paired
• Chi Square Test
• ANOVA - One Way and Two Way
• Correlation Analysis
• Linear Regression and Multiple Regression
• Model Summary, R Square and Residual Analysis
• Data Visualization
• Base Plot System - plot, hist, barplot, boxplot, pie
• ggplot2 Grammar of Graphics
• Bar Chart, Line Chart, Histogram, Boxplot, Scatter Plot
• Density Plot, Heatmap and Facet Grid
• Colour, Theme, Label, Legend and Annotation
• Multi Chart Layout and Chart Export
• Chart Selection and Data Storytelling
• Project
• R Markdown and Reporting
• R Markdown Document Structure
• Code Chunk, Inline Code and Output Control
• Report Export to HTML, PDF and Word
• Automatic Research Report Generation
• Introduction to Machine Learning with R
• Supervised and Unsupervised Learning Concept
• Train Test Split and Model Evaluation
• Logistic Regression, Decision Tree and Clustering Basics
• caret Package Introduction
• Introduction to R Shiny
• Shiny App Structure - UI and Server
• Input Widgets, Output Elements and Reactivity
• Simple Interactive Dashboard Creation
• Debugging and Error Handling
• Common R Errors and Their Meaning
• tryCatch, warning and Error Messages
• Code Reading and Output Prediction Practice
• Programming Coding Practice
• Daily Coding Exercises on Real Data Sets
• Interview Questions and Analytics Case Studies
• Practical Work
• Survey Data Analysis and Report
• Sales and Business Performance Analysis
• Student Result and Academic Data Analysis
• Health and Agriculture Research Data Analysis
• Thesis Data Analysis and Chart Preparation
• Multi Platform Practice
• R on Windows and Linux
• RStudio Cloud and Online R Compilers
• Git and GitHub Basics for R Projects
• Path Ahead - Python, Power BI, Machine Learning and Data Science
• Emerging Technologies - R in Research, Bioinformatics and Business Analytics
• Project Work
• Internship