On-line courses

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A vast array of courses in statistics and computational methods are available online. Some have fixed schedules and charge tuition, others are flexible and free. We list a selection of these courses here. Listing does not imply any assurance or approval by ASAIP.
Coursera: Data-driven Astronomy
Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills – all the activities will be done in Python 3. Course content includes: discovering pulsars in radio images, managing data and algorithms, SQL queries, regression, machine learning classification. The course is created by The University of Sydney AU.
Employment advice from the AAS
Advice from the AAS The American Astronomical Society Committee on Employment encourages astronomers who seek positions in industry to learn more statistics and informatics.
Starting in 2003, the Institute for Statistics Education now offers over a hundred courses through the Web site Statistics.com. A typical course is 4-5 weeks in duration, costs around $400, and is taught by an author of a major textbook or monograph. We list here courses related to astrostatistics in the areas of: training in the R software environment; Bayesian methodology; and general statistical modeling.
Courses from Massachusetts Institute of Technology (free)
Elementary courses: Introduction to Probability and Statistics; Statistics for Applications. Advanced courses: Introduction to Algorithms; Time Series Analysis; Statistical Learning – Theory and Applications; Pattern Recognition for Machine Vision; Biomedical Signal and Image Processing; Parallel programming for Multicore Machines Using OpenMP and MPI
Courses from Stanford University
Elementary courses: Introduction to Statistical Inference; Statistical Methods in Engineering and the Physical Sciences; Data Mining and Analysis. Advanced courses: Modern Applied Statistics – Learning; Modern Applied Statistics – Data Mining; Paradigms for Computing with Data
Courses from Johns Hopkins University
As of spring 2014, JHU is offering 9 courses on Coursera as part of the Johns Hopkins Data Science Specialization.
Courses from Texas A&M University
Texas A&M provides online about 20 courses in statistics including: sampling, regression analysis, time series analysis, mathematical statistics, multivariate analysis, statistical bioinformatics, categorical data analysis, and applied Bayesian methods.
Courses from Pennsylvania State University
Penn State University offers a 12-credit online graduate Certificate in Applied Statistics. Course offerings include: Applied Statistics, Regression Methods, Intro to Probability Theory, Intro to Mathematical Statistics, Topics in R statistical language, Analysis of Discrete Data, Applied Multivariate Statistical Analysis, Applied Time Series Analysis, and Data Mining.
Other introductory courses in statistics
Several universities including Rice University (free), University of California Berkeley, Carnegie-Mellon University
Other courses and workshops
statsTeachR is an open-access, online repository of modular lesson plans, a.k.a. “modules”, for teaching statistics using R at the undergraduate and graduate level. statsTeachR provides a central location for interactive and modern lesson plans in statistics and statistical computing. All modules are made available via a Creative Commons license.
MIT course: Tackling the Challenges of Big Data *******
This Digital Programs course will survey state-of-the-art topics in Big Data, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), visualization, and a range of applications. Tools include SQL, NoSQL, NewSQL, MapReduce, data compression, machine learning.