Department of Business Intelligence offers short term courses of one year Post Graduate Diploma Programmes. The target of the programmes is those persons who want to increase their employability or improve their professional skills but did not have the opportunity to do so earlier in a formal manner. The focus of the programmes will be to impart the required knowledge and skills. These programmes will be conducted during the evening hours so that employed persons or students pursuing other programmes can also join without affecting their normal pursuits. In other words, even Post Graduate students pursuing other full timeprogrammes can also join any of these programmes
Introduction to Research Methods:-
Introduction to Research and Problem Formulation, Research Process and Research Design,
Sampling Theory, Data Collection, Preparation, Analysis And Reporting
Basics of statistics:-
Sample And Population, Population Parameter And Sample Statistic, Descriptive Statistics And
Inferential Statistics, Ungrouped Data And Grouped Data, Dependent And Independent Variable,
Scale Of Measurement( Nominal, Ordinal, Interval And Ratio)
Introducing Software Interface:-
Data View And Variable View), Measurement Scales, How To Export Data From Excel To
Software, Entering, Saving And Printing Data, Viewing A Few Cases, Merge File With Cases,
Merge File With Variables, Sort Cases, Spilt File, Select Cases, How To Do Serial Number,
Recode Into Same Variable, Recode Into Different Variable, Compute Command, Visual
Binning, Generation Of Shell File.
Descriptive Statistics:-
Tables And Graphs for One Variable, Tables And Graphs for Two Variables, One Variable
Descriptive Statistics, Two Variables Descriptive Statistics, Measures Of Central Tendency And
Variability, Shape Of Distribution, Stem and Leaf Charts, Box And Whisker Plot.
One - Sample Hypothesis Tests, Two- Sample Hypothesis Tests (theory):-
The Logic of Hypothesis testing, A More Realistic Case: We Don’t know µand Sigma, OneSample T-Test, The Logic of Hypothesis Testing, Paired vs. Independent Samples, Testing
Assumptions of Independent Samples, Normal Populations, Randomness of Data and Equal population Variance, Comparing Three or More Means, Testing Assumptions of Independent Samples, Normal Populations and Homogeneity Population Variance, One- Factor Independent Measures ANOVA, Post Hoc Multiple Comparisons
STATISTICAL METHODS/DATA ANALYSIS USING SPSS/ R:-
Univariate, bivariate, Multivariate analysis techniques (SPSS Practical session)
Parametric and Nonparametric Methods:-
Introduction to Parametric and Nonparametric Methods, Mann-Whitney U test, Wilcoxon Signed
Ranks Test, Kruskal- Wallis H Test, Spearman’s Rank Order Correlation, Sign Test, Runs Test,
One Sample Chi Square Test, Fridman One-Way Anova, Kolmogorov- Smirnov One Sample
Test.
Factor analysis, cluster and discriminate analysis :-
What is FA, Hypothetical example of FA, Assumptions of FA, Deriving factors and assessing
overall fit, interpreting the factors, Validation of factors analysis. What is cluster analysis, how
does it work, Cluster analysis decision process, what is MDS, Hypothetical example of it,
Assumption of it interpretation & Validation
Correlation and regression analysis:-
What is Multiple Correlation Analysis, assumption of it, Hypothetical example of it,
interpretation & validation. Introduction, Regression with a binary dependent variable,
estimating Logistic Regression Model, Hypothetical example of it, interpretation & Validation
Introduction R- Software and Qualitative Analysis
Introductory session for each one mentioned above
Department of Business Intelligence offers short term courses of one year Post Graduate Diploma Programmes. The target of the programmes is those persons who want to increase their employability or improve their professional skills but did not have the opportunity to do so earlier in a formal manner. The focus of the programmes will be to impart the required knowledge and skills. These programmes will be conducted during the evening hours so that employed persons or students pursuing other programmes can also join without affecting their normal pursuits. In other words, even Post Graduate students pursuing other full timeprogrammes can also join any of these programmes
Introduction to Multivariate Statistical Techniques: -
Regression Analysis Overview of multiple linear regression analysis and its applications. Both ordinary regression
analysis and partial regression analysis, methods of testing significance of various regression
models
Principal Components Analysis: -
Overview of principal components analysis (PCA).
Relationship to multivariate normal distribution and to eigen structure of covariance or
correlation matrix and to SVD of original (mean centered or standardized) matrix of observations
by variables.
Exploratory Factor Analysis:-
Principal axis form of exploratory factor analysis (FA) and its relationship to PCA. computation
of factor scores as well as factor loadings,estimation of “communalities”.
Multidimensional ScalinG:
Metric and nonmetric models and methods of “two-way” multidimensional scaling (MDS).
Three-way MDS; Methods of multidimensional analysis of preferential choice (or other
“dominance”) data.
Cluster Analysis. hierarchical clustering, specifically single, complete and average linkage, Ward's method, and Kth nearest neighbor clustering, based on direct or derived measures of similarity or dissimilarity, and on K-means clustering for partitioning based on a standard objects by variables multivariate data matrix. .
Canonical Correlation:-
Canonical correlation analysis (CCA), methods of computing a set of ordered canonical
variates, the Kth set of variates constrained to be orthogonal to the first K-1 (in a certain sense
that will be defined)
Analysis of Variance, and generalizations. A review of standard analysis of variance (ANOVA), plus an introduction of ANCOVA (analysis of covariance), MANOVA (Multivariate ANOVA) and MANCOVA (Multivariate ANCOVA) multiple linear regression analysis, with “dummy” independent variables, while ANCOVA is a special case of partial regression analysis. Both MANOVA and MANCOVA as special cases of canonical correlation analysis.
Department of Business Intelligence offers short term courses of one year Post Graduate Diploma Programmes. The target of the programmes is those persons who want to increase their employability or improve their professional skills but did not have the opportunity to do so earlier in a formal manner. The focus of the programmes will be to impart the required knowledge and skills. These programmes will be conducted during the evening hours so that employed persons or students pursuing other programmes can also join without affecting their normal pursuits. In other words, even Post Graduate students pursuing other full timeprogrammes can also join any of these programmes
Introduction to Multivariate Statistical Techniques: -
Regression Analysis Overview of multiple linear regression analysis and its applications. Both ordinary regression
analysis and partial regression analysis, methods of testing significance of various regression
models
Principal Components Analysis: -
Overview of principal components analysis (PCA).
Relationship to multivariate normal distribution and to eigen structure of covariance or
correlation matrix and to SVD of original (mean centered or standardized) matrix of observations
by variables.
Exploratory Factor Analysis:-
Principal axis form of exploratory factor analysis (FA) and its relationship to PCA. computation
of factor scores as well as factor loadings,estimation of “communalities”.
Multidimensional ScalinG:
Metric and nonmetric models and methods of “two-way” multidimensional scaling (MDS).
Three-way MDS; Methods of multidimensional analysis of preferential choice (or other
“dominance”) data.
Cluster Analysis. hierarchical clustering, specifically single, complete and average linkage, Ward's method, and Kth nearest neighbor clustering, based on direct or derived measures of similarity or dissimilarity, and on K-means clustering for partitioning based on a standard objects by variables multivariate data matrix. .
Canonical Correlation:-
Canonical correlation analysis (CCA), methods of computing a set of ordered canonical
variates, the Kth set of variates constrained to be orthogonal to the first K-1 (in a certain sense
that will be defined)
Analysis of Variance, and generalizations. A review of standard analysis of variance (ANOVA), plus an introduction of ANCOVA (analysis of covariance), MANOVA (Multivariate ANOVA) and MANCOVA (Multivariate ANCOVA) multiple linear regression analysis, with “dummy” independent variables, while ANCOVA is a special case of partial regression analysis. Both MANOVA and MANCOVA as special cases of canonical correlation analysis.
