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Useful for UGC NET

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  1. Welcome
  2. Learning Outcomes
  3. Units in this Course
  4. Recommended Textbooks

Welcome

This is the complete study package for Computational Statistics & R Programming — a hands-on course that takes you from computer fundamentals, through statistical data analysis in MS-Excel, into full R programming for data structures, exploratory data analysis and visualisation. Every concept is defined, explained, illustrated with two examples, and — where useful — backed by a ready-to-run R script.

How to study this course: install R and RStudio early (Unit 4), then type every script yourself rather than copy-pasting. Statistics is learned at the keyboard — run the code, change a number, predict the new output, and check.

Learning Outcomes

  1. Use commercial and open-source tools (Excel, the R language and its libraries) for data analytics and visualisation.
  2. Analyse real-time problems using R.
  3. Use basic R data structures to load, clean and pre-process data.
  4. Carry out exploratory data analysis on real datasets.
  5. Understand and implement linear regression.
  6. Work fluently with lists, vectors, matrices, data frames, factors and tables.

Units in this Course

UNIT 1

Computer Basics

Applications & components of a computer; CPU, input/output, memory & storage; programming languages; files & folders; software types; operating systems (Windows, Linux).

 UNIT 2

Data Processing in Excel

Data entry & editing; copy / paste-special; sort & filter; AutoSum; mean & SD with statistical functions; matrix transpose, multiply, inverse; bar / line / pie charts; export to Word & PowerPoint.

 UNIT 3

Statistical Analysis in Excel

Scatter diagram; fitting straight-line, polynomial & power curves; \(R^2\) and trendline equation; FORECAST & TREND; Data Analysis ToolPak; t-test & one-way ANOVA; the \(p\)-value.

 UNIT 4

R Programming — Basics & Vectors

Introduction & features of R; RStudio; assignment, modes, operators, special numbers, logicals; functions & help; data structures & control structures; vectors — create, index, name, operate, recycle, vectorised if-else, NA / NULL, filtering & subsetting.

 UNIT 5

Matrices, Data Frames & EDA

Matrices & operations; data frames — create, name, access, add/remove, merge; factors & tables; exploratory data analysis (central tendency, variability, summary, missing values, outliers, normalisation); data visualisation — basic, advanced & 3-D plots.

 PRACTICAL

Practical Course (10 Practicals)

Installing R and RStudio, and the working directory; packages; basic operations; vectors; distance matrix; student-marks data frame; price and demand matrices; summary() and dispersion; bar / multiple bar / histogram / box / line / scatter plots. Each set out as Question, Aim, Steps, Programme, and Execution and Results.

 REFERENCE

Official Syllabus

Course outline, textbooks, references and exam blueprint.
Tooling note: R and RStudio are free and open-source. The install.packages() command downloads add-on libraries (such as ggplot2) once; thereafter library() loads them in each session.
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Next course in learning order: Data Handling using R Statistical computing