Courses
Current teaching/learning materials developed under Unlicense, Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License or Apache 2.0 License are listed below.
- Scalable Data Science and Distributed Machine Learning, 2021.
- Introduction to Data Science: A Computational, Mathematical and Statistical Approach, Summer 2019.
- Scalable Data Engineering Science with Apache Spark 2.x, Spring/Summer 2019.
- 360-in-525-2019:: Introduction to Privacy-Aware Decisions - A Full-Day Workshop in Data Sciences, Spring 2019.
- as-2019:: 1MS926: Applied Statistics
- glm-2018:: 1MS369: Generalised Linear Models, Uppsala Maths Postgraduate Course, Fall 2018.
- infty-2018-01:: 1MS035: Inference Theory 1, Uppsala Maths Undergraduate Course, Fall 2018.
- 360-in-525 Minutes CIM Course Set in Data Sciences for Uppsala’s PhD/MSc Students, Spring 2018.
- Scalable Data Science for Uppsala’s PhD/MSc Students, Fall 2017 (under progress).
- Scalable Data Science - 1.6 from Middle Earth, Raazesh Sainudiin and Sivanand Sivaram, Published by GitBook, 791 pages, 24th July 2017. Available as:
- forkable open source in GitHub
- YouTube-playlist.
- Computational Statistical Experiments in MATLAB, Raazesh Sainudiin and Dominic Lee, 360 pp, 2017. Available as:
- Mathematical and Statistical Elements of Data Science in SageMath/Python: A programmatic introduction for data scientists, Raazesh Sainudiin, 2017 (in progress).
- See an earlier version of the course with an emphasis on simulations that is available as:
- forkable open source in GitHub
- YouTube-playlist
- See an earlier version of the course with an emphasis on simulations that is available as:
- See courses taught from 2007-2016 at the Course Archive.