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Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Kanpur

Course Brief - A brief description of the course content; Downloading and installing R and R Studio
Exploring R - A description of the basic utilities available in R and R Studio
R preliminaries - Using R as a computational tool; Illustrative use of R for data handling
Using Simple Commands - Some examples of R-commands
Working with R - Importing data into R; Simple applications for data handling

Basic Statistical Concepts - Measurement scales; Null hypothesis statistical testing (NHST)
Practical Significance of Results - Statistical power of a test; Effect size; Statistical and practical significance of results
Univariate Data - Univariate statistical analysis: Z-test, t-test, F-test, correlations
Multivariate Techniques - An overview of multivariate techniques: selecting an appropriate multivariate technique for a given dataset
Data Manipulation - Scaling data: Mean-centering and standardizing data

Data Preparation - Visual (graphical) examination of data: box-plot, stem and leaf plot, histogram; Missing values; Outliers
Examining the Data - Examining the data for univariate and multivariate assumptions
Transformations for Non-normal Data - Transformations for skewed data; Selecting between square-root, inverse, and logarithmic transformations; Interpreting results based on the transformed data

Analytical Models - Descriptive, graphical, and mathematical models
Principal Components Analysis (PCA) - Introduction to different factor analysis techniques; Basic concepts; manually doing PCA; Factor structure and rotation of factor structure
Performing Principal Components Analysis - Using R-packages for computing PCA in R; Interpreting the R-output

Exploratory Factor Analysis (EFA) - Introduction to EFA; Comparison of PCA and EFA; Performing EFA; Higher order factor analysis
Application of Factor Analysis - Issues in PCA and EFA; Illustrative applications of PCA and EFA
Confirmatory Factor Analysis (CFA) - Objectives of CFA; Comparison of CFA and EFA; Performing CFA and interpretation of R-output

Regression Analysis - Bivariate and multiple regression analysis; Assumptions of regression analysis; Multicollinearity and its effect on the regression model
Performing regression analysis - Interpretation of R-Output; Multivariate multiple regression analysis
Cluster analysis - Objectives of cluster analysis; Similarity measures; Hierarchical clustering; the concept of dendrogram

Performing Cluster Analysis - Interpreting R-output
Discriminant Analysis - Objectives of discriminant analysis; comparison with regression analysis and clustering
Performing Discriminant Analysis - Two-group discriminant analysis; Multiple group discriminant analysis

Logistic Regression - Objectives of logistic regression; Logistic regression model; Receiver Operating Characteristic (ROC) Curve
Performing Logistic Regression - Interpretation of R-Output
Analysis of Variance (ANOVA) - Univariate ANOVA; Factorial ANOVA designs; Analysis of covariance (ANCOVA)

Computing ANOVA and ANCOVA - Interpretation of R-output
Multivariate analysis of variance (MANOVA) - Analytical approach to MANOVA; Wilks lambda and other statistics to test the statistical significance of MANOVA results
Computing and interpreting MANOVA - Computing MANOVA for two groups and multiple groups; Interpretation of R-output

Canonical Correlation Analysis (CCA) - Introduction to CCA; Statistical tests for significance testing
Performing Canonical Correlation Analysis - Illustrative example and interpretation of R-output
Multidimensional scaling (MDS) - Spatial models; Similarity and distance; multidimensional map

Performing Multidimensional Scaling - Interpretation of R-output
Correspondence Analysis - Basic concepts; Creating perceptual maps
Conjoint Analysis - The concept of utility (worth); Assessing part worth and whole worth based on the product attributes

Structural Equation Modelling (SEM) - Exogenous and endogenous variables; Reflective and formative constructs; inner model and outer model; Statistics related to SEM
Performing Structural Equation Modeling - Interpreting R-output and reporting the results
Partial Least Squares for SEM (SEM-PLS) - Sample size considerations; SEM-PLS as a technique for data that violates multivariate assumptions; Other approaches to the issue of non-normal data in SEM