The isi academy training

June 16, 2026

The isi academy training

The ISI Academy Training Team website: https://isi-web.org/training Any ISI or ISI association member can join and learn for free.

 

 

Explore current and upcoming courses below.

Applied Time Series Analysis Using R

MWF June 29 – July 10 2026, 13:30 CET - 15:30 CET (6:00-8:00 pm IST

Course description
Using R, this course will describe various modeling and forecasting approaches for time series data including time series regression methods, smoothing methods, Nonseasonal and seasonal autoregressive moving average models, models for vector-valued time series, and deep learning models. The recommended preparation includes an introductory knowledge of statistical methods and a familiarity with R.

Recommended Books
- Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). *Time series analysis: Forecasting and control* (5th ed.). Wiley.
- Peña, D., & Tsay, R. S. (2021). *Statistical learning for big dependent data.* John Wiley & Sons.
- Shumway, R. H., & Stoffer, D. S. (2025). *Time series analysis and its applications: With R examples* (5th ed.). Springer.
- Shumway, R. H., & Stoffer, D. S. (2019). *Time series: A data analysis approach using R*. Chapman and Hall/CRC.

Getting started with R.

Instructor

Nalini Ravishanker is Professor in the Department of Statistics at the University of Connecticut (UConn), Storrs. She has a PhD in Statistics and Operations Research from the Stern School of Business, New York University, and a B.Sc. in Statistics from Presidency College, Madras. Her primary area of research is time series analysis. She has over 150 peer-reviewed publications, two books, and one edited volume. She is a Fellow of ASA, AAAS, CASE, and elected member of ISI. She served as President of ISBIS (2017-2019) and is President-elect of ISI (2025-2027). She has served as EIC of the International Statistical Review and is currently EIC of the Wiley journal Applied Stochastic Models in Business and Industry. 
 

Applied Spatial Data Analysis Using R

MWF July 13 – July 24 2026, 13:30 CET - 15:30 CET (6:00-8:00 pm IST)

Course description
This course will introduce spatial data and R ecosystem, and describe spatial data visualization and mapping, spatial dependence and autocorrelation, point referenced spatial data and variograms, spatial interpolation and kriging, and spatial regression models. The recommended preparation includes an introductory knowledge of statistical methods and a familiarity with R.

Recommended Books
- Bivand, R. S., Pebesma, E., Gómez-Rubio, V. (2013). Applied Spatial Data Analysis with R. Springer.
- Cressie, N. (2015). Statistics for spatial data. John Wiley & Sons. 
- Stein, M. L. (1999). Interpolation of spatial data: some theory for kriging. Springer Science & Business Media.
- Wikle, C. K., Zammit-Mangion, A., & Cressie, N. (2019). Spatio-temporal statistics with R. Chapman and Hall/CRC.
- Schabenberger, O., & Gotway, C. A. (2017). Statistical methods for spatial data analysis. Chapman and Hall/CRC.

Instructors
Suman Majumder is an Assistant Professor in the Interdisciplinary Statistical Research Unit in the Indian Statistical Institute. He has a PhD in Statistics from the North Carolina State University and an M.Stat. degree from Indian Statistical Institute. His primary research area is spatial and spatiotemporal modeling with miscellaneous applications. He is a member of the ISI and the IISA. He serves currently as the technical editor for Sankhya.

Arnab Hazra is an Assistant Professor in the Department of Mathematics and Statistics in IIT Kanpur. He has a PhD in Statistics from the North Carolina State University and an M.Stat. degree from ISI. His primary research area is spatial and spatiotemporal modeling and spatial extremes. He is a member of the ASA, IISA, and ISI. He serves currently on the editorial boards of Statistics and Computing, Journal of Agricultural, Biological, and Environmental Statistics, Sankhya, and Journal of Applied Statistics: Environmental Statistics and Data Science.
 

 

Nalini Ravishanker

Professor, University of Connecticut

President-elect ISI, https://isi-web.org/

Editor-in-Chief, ASMBI, https://onlinelibrary.wiley.com/journal/15264025

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