Skip to the content

Source document. This page reproduces the syllabus this course was written to, as published — its semesters, credits and paper numbers are that document’s, not this site’s. The course itself is studied on its own, in any order.

This page reproduces the prescribed outline so that the teaching pages can be checked against it line by line. It is the syllabus, not a summary of it. Nothing about marks, duration or examination pattern appears on this site.

Course Objectives and Outcomes

Able to carry out the Statistical Analysis and writing statistical Report using R for any dataset.

Data Handling using R

AS PRESCRIBED
  1. Understanding Data set, Data Description, with Data variables, Measurement of scales of data variables, Relations among data variables identifying the dependence and independence among the variables, data pre-processing steps, steps involved in Statistical Data Analysis Report
  2. Data Transformations (Data scale conversions, Standardization, Normalization, usage of Log, Sine, Cosine, Square, Square root, Exponential transformations).
  3. Descriptive Statistics Evaluation based on measurement of scales of variables and relationships among variables.
  4. Data Visualization: Drawing One dimensional diagram (Pictogram, Pie Chart, Bar Chart), two-dimensional diagrams (Histogram, Line plot, frequency curves & polygons, ogive curves, Scatter Plot), other diagrammatical / graphical representations like, Gantt Chart, Heat Map, Box-Whisker Plot, Area Chart, Correlation Matrices.
  5. Mathematical Model Building and its process, over fitting, under fitting, cross validation concepts, (train/test, K fold and leave out one approaches), Cross-tabs
  6. Evaluation of Model Performance for classification techniques for Qualitative and Quantitative data.
  7. Parametric tests (z-, \(\chi^{2}\), t-, F-tests, ANOVA), Correlation & Regression etc.
  8. Non-Parametric tests (Sign test, Median, Wilcoxon sign rank, Mann-Whitney U, Run test).

Notes, as Printed

NOTE 1 — DATA SOURCES

Any free downloadable Data sets from web sources can be used for practice. For example, Iris Dataset; flights.csv Dataset; Sustainable Development Data; Credit Card Fraud Detection; Employee dataset; Heart Attack Analysis & Prediction Dataset; Dataset for Facial recognition; Covid_w/wo_Pneumonia Chest Xray Dataset; Groceries dataset; Financial Fraud and Non-Fraud News Classification; IBM Transactions for Anti Money Laundering.

The practical page uses a twenty-record data set written out in full instead, so that every figure on the page can be reproduced without downloading anything.

NOTE 2 — THE PRACTICAL RECORD

Practical Record should contain all practical's with their implementation and is Mandatory and it carries 5 Marks. The Semester end practical exam contain answer any two out of four questions with their implantations using R.

Pre-requisite, as Covered on this Site

ASSUMED BEFORE THIS PAPER

The R language and the standard analyses are covered by Computational Statistics and R Programming, Units 1 to 5. Nothing from it is repeated on the practical page; each unit is linked at the point where it is needed.

→ The workflow, end to end

← Data Handling using R The Workflow →