### 07-21, 14:00–17:00 (UTC), Red

This workshop provides an introduction to the Julia language for data-scientists and statisticians. No prior experience with Julia is assumed. The workshop starts with a few Julia basics and then progresses through basic probability and statistics examples, usage of dataframes, elementary statistical inference, regression, and more advanced methods. At the end of this workshop, attendees will have solid entry point for using Julia as their preferred data analysis tool.

This workshop accommodates data-scientists and statisticians that have experience with a language like R, but have not used Julia previously. In learning to use Julia, a contemporary "stats based" approach is taken focusing on short scripts that achieve concrete goals. The primary focus is on statistical applications and packages. The Julia language is covered as a by-product of the applications. Thus, this workshop is much more of a *how to use Julia for stats* course than a *how to program in Julia* course. This approach may be suitable for statisticians and data-scientists that tend to do their day-to-day scripting with a data and model based approach - as opposed to a software development approach.

The topics covered include:

- Basic probability and Monte Carlo.
- Basics from the in-built Statistics package and the StatsBase package.
- Basic plotting and statistical plotting with StatsPlots.
- Using the Distributions package.
- (Basic) usage of the Dataframes package.
- Using the GLM package.
- Other useful resources and packages.

(Note that Julia has hundreds of statistical packages and we can not cover them all in 3 hours).

Code snippets from Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence will be used in conjunction with smaller live constructed examples.

An extensive Jupyter notebook for the workshop together with data files is here. You can install it to follow along.

If you don't have Julia with IJulia (Jupyter) installed, you can follow the instructions in this video.

Associate Professor Yoni Nazarathy from the University of Queensland Australia, specializes in data science, probability and statistics. His specific research interests include scheduling, control, queueing theory, and machine learning. He has been at The University of Queensland for nearly a decade, teaching courses in the Masters of Data Science program and working on research. Prior to his previous academic positions in Melbourne and the Netherlands, he worked in the aerospace industry in Israel. In recent years, he has also been heavily involved with primary and secondary mathematics education and is the co-founder of an EdTech mathematics organization called One on Epsilon. He is also the co-author of a data science book, "Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence". Recently his research has also focused on epidemics and he leads the Safe Blues program dealing with finding efficient and ethical methods to track social mobility with a goal of prediction and control of epidemics.