Question 1

Core Courses

Accepted Answer

Course nameDetailsBasic StatisticsDevelop the necessary knowledge and skills in basic statistics that are needed for statistical analysis of data. Get familiar with data, basic descriptive statistics, and the LibreOffice software.Basic ProbabilityApply basic concepts of probability theory for statistical understanding of data, construct statistical models for data using probability distributions, and compute probabilities of uncertain events for prediction and inference.Statistical MethodsLearn the concepts of population, sample and sampling distribution, perform t-tests, ANOVA tests, and chi-square tests, and estimate mean, proportions, and dispersion.Census and Sample surveysLearn to apply methods of survey sampling for inferring about a population, select an appropriate sample for a given situation, and use R to compute estimates for population parameters and associated standard errors.

Question 2

Specialisation One: Data Analytics

Accepted Answer

Course NameDetailsIntroduction to R and PythonStart the data analytics track by learning the fundamentals of R and Python.Multiple Regression with RLearn method and interpretation, multiple correlation and R-square, prediction, estimation versus prediction errors, subset selection, residual and leverage, outliers, and more.Advanced Regression with RLearn to handle nonlinearity, heteroscedasticity, serial correlation, and non-normality in multiple linear regressions.Time Series Analysis and Forecasting with RLearn how to forecast using R - Key topics include trends, seasonality, stationarity, smoothing and differencing, ACF and PACF, SARIMA models, forecasting, ARCH/GARCH models, multivariate time series, and VAR models.Multivariate Statistical Methods with RLearn to perform principal component analysis, factor analysis, multidimensional scaling, and correspondence analysis.Introduction to Statistical LearningLearn the basic of training and test data, validation, discriminant analysis, classification, tree-based methods, clustering, SVM, and neural networks.Advanced statistical methodsLearn general methods of estimation and model fitting (eg; method of Moments, Maximum Likelihood Estimation and assessing methods through simulation), model fitting (QQ plot, Goodness of Fit chi-square, Kolmogorov-Smirnov), basics of Bayesian statistics and nonparametric inference. (Conjugate prior, Metropolis Hastings algorithm, MCMC, R demonstrations)Specialised Models and MethodsLearn bivariate and multivariate data modelling, time to event data (Eg life tables, KM, two sample test, Cox regression), categorical data, multiple event occurrence data (Eg Markov chain Poisson Process), text data (eg Parsing, text mining, sentiment analysis, Natural Language Processing) and image data (Representations, feature extraction, statistical models).

Question 3

Specialisation Two : Official Statistics

Accepted Answer

Course NameDetailsManagement of DataGet introduced to fundamental concepts in database management systems, database models, SQL, and spatial data analysis.Survey Design and ConceptsLearn to plan and design large-scale sample surveys.Population and Social StatisticsDive deeper into vital statistics and learn about major large-scale surveys like consumer expenditure surveys, labour and employment statistics, social consumption, health and education surveys, and time-use surveys.Introduction to Official Statistical SystemsLearn about Official Statistics and its classification and sources, while navigating through important censuses and surveys.Statistics and EconomyStudy the basics of microeconomics, macroeconomics, and national accounts.Economic Statistics ILearn about National Accounts Statistics,  including the compilation of production-side, income-side, and use-side estimates, the sectoral sequence of accounts, price indices, and the compilation of the Consumer Price Index.Economic Statistics IILearn about agriculture and allied sector statistics, including area and yield estimation, surveys for estimating output of livestock products, and surveys for estimating marine fish catch and inland fish production. The course also covers industrial statistics, including  ASI and unorganised sector surveys. Finally, the course covers service sector statistics, and other sectoral statistics.Economic Statistics IIIThis course covers Government Financial Statistics (GFS), Banking and Financial Statistics (BFS), Foreign Trade Statistics, and Balance of Payment (BoP) Statistics.

Post Graduate Diploma in Applied Statistics

Post Graduate Diploma in Applied Statistics

Indian Statistical Institute

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Curriculum

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Course name	Details
Basic Statistics	Develop the necessary knowledge and skills in basic statistics that are needed for statistical analysis of data. Get familiar with data, basic descriptive statistics, and the LibreOffice software.
Basic Probability	Apply basic concepts of probability theory for statistical understanding of data, construct statistical models for data using probability distributions, and compute probabilities of uncertain events for prediction and inference.
Statistical Methods	Learn the concepts of population, sample and sampling distribution, perform t-tests, ANOVA tests, and chi-square tests, and estimate mean, proportions, and dispersion.
Census and Sample surveys	Learn to apply methods of survey sampling for inferring about a population, select an appropriate sample for a given situation, and use R to compute estimates for population parameters and associated standard errors.

Course Name	Details
Introduction to R and Python	Start the data analytics track by learning the fundamentals of R and Python.
Multiple Regression with R	Learn method and interpretation, multiple correlation and R-square, prediction, estimation versus prediction errors, subset selection, residual and leverage, outliers, and more.
Advanced Regression with R	Learn to handle nonlinearity, heteroscedasticity, serial correlation, and non-normality in multiple linear regressions.
Time Series Analysis and Forecasting with R	Learn how to forecast using R - Key topics include trends, seasonality, stationarity, smoothing and differencing, ACF and PACF, SARIMA models, forecasting, ARCH/GARCH models, multivariate time series, and VAR models.
Multivariate Statistical Methods with R	Learn to perform principal component analysis, factor analysis, multidimensional scaling, and correspondence analysis.
Introduction to Statistical Learning	Learn the basic of training and test data, validation, discriminant analysis, classification, tree-based methods, clustering, SVM, and neural networks.
Advanced statistical methods	Learn general methods of estimation and model fitting (eg; method of Moments, Maximum Likelihood Estimation and assessing methods through simulation), model fitting (QQ plot, Goodness of Fit chi-square, Kolmogorov-Smirnov), basics of Bayesian statistics and nonparametric inference. (Conjugate prior, Metropolis Hastings algorithm, MCMC, R demonstrations)
Specialised Models and Methods	Learn bivariate and multivariate data modelling, time to event data (Eg life tables, KM, two sample test, Cox regression), categorical data, multiple event occurrence data (Eg Markov chain Poisson Process), text data (eg Parsing, text mining, sentiment analysis, Natural Language Processing) and image data (Representations, feature extraction, statistical models).

Course Name	Details
Management of Data	Get introduced to fundamental concepts in database management systems, database models, SQL, and spatial data analysis.
Survey Design and Concepts	Learn to plan and design large-scale sample surveys.
Population and Social Statistics	Dive deeper into vital statistics and learn about major large-scale surveys like consumer expenditure surveys, labour and employment statistics, social consumption, health and education surveys, and time-use surveys.
Introduction to Official Statistical Systems	Learn about Official Statistics and its classification and sources, while navigating through important censuses and surveys.
Statistics and Economy	Study the basics of microeconomics, macroeconomics, and national accounts.
Economic Statistics I	Learn about National Accounts Statistics, including the compilation of production-side, income-side, and use-side estimates, the sectoral sequence of accounts, price indices, and the compilation of the Consumer Price Index.
Economic Statistics II	Learn about agriculture and allied sector statistics, including area and yield estimation, surveys for estimating output of livestock products, and surveys for estimating marine fish catch and inland fish production. The course also covers industrial statistics, including ASI and unorganised sector surveys. Finally, the course covers service sector statistics, and other sectoral statistics.
Economic Statistics III	This course covers Government Financial Statistics (GFS), Banking and Financial Statistics (BFS), Foreign Trade Statistics, and Balance of Payment (BoP) Statistics.