Postgraduate Diploma in Applied Statistics
Indian Statistical Institute
The curriculum of the Postgraduate Diploma in Applied Statistics features two levels: basic and advanced. Both levels cover six subjects, and the advanced level features two specialized tracks: Official Statistics and Data Analytics.
Each subject includes pre-recorded instructor videos, reading material, and practice questions. These are accompanied by weekly assessments, hands-on assignments, and project work—which often takes place in groups.
- Basic statistics: Learn and 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 and 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.
- 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.
There are two advanced level tracks after the basic level.
Official Statistics Specialization
- Data storage and retrieval: Get introduced to data integration concepts, database management systems, SQ, and methods of data validation.
- 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.
- Economic Statistics I: This course focuses on 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: This course covers key economic areas like 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.
Data Analytics Specialization
- Introduction to R and Python: Start the data analytics track by learning the fundamentals of R and Python in this course.
- Multiple Regression with R: In this course, you’ll cover 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: in this course, you will 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.
The programme is designed so you can enroll from anywhere in the world and complete courses at your own pace. The programme duration is 12 months.
Coursera on Mobile
Access all course materials anywhere with the mobile app, used by over 80 percent of degree students on Coursera. Available on iOS and Android. Using the mobile app, learners can:
- Save a week’s worth of content for offline access with one click
- Save and submit quizzes offline
- View text transcripts of lecture videos
- Take notes directly in the app
- Set reminder alerts to help you make progress
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We encourage you to investigate whether this diploma meets your academic and/or professional needs before applying.