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Social Research Insights



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Training on Sectoral Programs

The following programs are offered through Workshops, Seminars and faculty development programs. Please call +919177573730 or write to or


PROGRAM - 1: Basic statistical analysis through Calc & PSPP.

Duration; 5 hours.

    • Introduction to data science.

    • Descriptive statistics (Univariate & Bivariate): summary statistics like measures of central tendency, measures of dispersion, measures of shape. Cross tabulation.

    • Inferential statistics (Univariate & Bivariate): Statistical tests (chi-square test, T test, Z test, F test, other parametric and non-parametric tests)

    • Exploratory statistics (Bivariate & Multivariate): Correspondence analysis (including MCA, MJCA), Principal components analysis, Factor analysis, Cluster analysis.

PROGRAM - 2: Advanced Visualization techniques through R language.

Duration: 5 hours

    • Creating statistical graphs - 2D & 3D: bar charts, histograms, pie charts, box plots, line charts etc.; Scatter diagrams,

    • linear plots, QQplots, regression plots, contour plots, perspective plots; 3D plots; Animations; Creating flow charts and block diagrams; Image manipulation and editing.

PROGRAM - 3: Advanced analytics through R language.

Duration: 5 hours

    • Structural Equation Modelling (SEM)

    • (Social) Network Analysis (SNA)

    • Artificial Neural Networks (ANN)

    • Text Analytics (Text Mining/TM)

    • Natural Language Processing (NLP)

    • Data mining and Web scraping


PROGRAM 1: Data analytics for better health care decisions

Duration: 5 hours

Tools: Excel, Calc, PSPP, SPSS, R and SAS.

  • Summary statistics: central tendency, dispersion and shape.

  • Statistical tests or diagnosis: parametric & non-parametric and hybrid tests.

  • Association and linear modelling: bivariate ) canonical correlation, simple regression, logistic, predictive modelling, Cross tabulation and other association techniques.

  • Effective visualization techniques

  • Multivariate analytics: Correspondence analysis, PCA, MCA. MJCA, HCPCA, Exploratory and Confirmatory Factor Analysis, Cluster analysis and MDS

  • Advanced analytics: Artificial neural networks, Text analytics for medical transcription

  • Structural equation modelling

  • Social network analysis

  • Big data analytics

PROGRAM 2: Text Mining for Clinical Data Analysis through R

Duration: 4 hours

  • Data Import & Export

  • Inspecting Corpora

  • Data Transformations

  • Filtering

  • Meta data Management

  • Creating Term-Document Matrices

  • Operations on Term-Document Matrices

  • Dictionary operations

PROGRAM 3: Medical Image Analysis through R language

Duration: 5 hours

  • Magnetic resonance imaging (MRI): diffusion tensor imaging (DTI), Dynamic contrast - enhanced MRI (DCE - MRI), functional connectivity, functional MRI, structural MRI, visualization, simulation of time series and 4D data.

  • General image processing.

  • Positron Emission Tomog

  • Digital imaging and communications in medicine (DIACOM)

  • raphy (PET)

  • Electroencephalography (EEG).


PROGRAM 3: Open source tools for bioinformatics

Duration: 5 hours

Tools: Calc, PSPP and R.


  • Data Display & Discriptive statistics

  • Probability Distributions

  • Estimation and Inference

  • Linear Models

  • Cluster Analysis & Trees

  • Classification Methods

  • DNA Sequence Statistics

  • Micro Array Analysis



PROGRAM 1: Risk Modeling Through R Language

Duration: 5 hours

  • Probability distributions for returns

  • Methods and models of valuation

  • Modeling volatility

  • Modeling dependence

  • Robust portfolio optimization techniques

  • Tactical asset allocation techniques

  • Testing and reporting portfolio strategies

PROGRAM 2: Fraud Analytics for effective risk modeling

Duration: 5 hours

Tools: R language

  • Binning

  • ROC Curves

  • KS statistics

  • Logistic Regression & predictive analytics

  • Tree algorithms

  • Plotting trees

  • Assessing complexity of trees

  • Trees to rules

  • Conditional inferences

  • Estimation of trees with Logistic regression

  • Baysian Networks

  • Graphical & numerical analysis



PROGRAM 1: High Performance and Parallel Computing" (Big data analytics through R Language)

Duration: 5 Hours

  • Explicit parallelism

  • Implicit parallelism

  • Grid computing

  • Hadoop

  • Random numbers

  • GPU computing

  • Resource (memory) management

For other programs or special training through R language please visit