Software For Data Science Workshop: Julia

March 20, 2017, The Fields Institute

As acquisition of data continues to accelerate, businesses in every sector are requiring that their employees are equipped to analyze and understand all of the information available to them. As part of continuing mission of Fields to promote mathematics education, Fields is happy to present an ongoing series of workshops aimed at providing students access to, and training for, some of the skills that employers want most. By participating in these events, students can add valuable skills to their CV and learn some of the most cutting edge techniques used in industry. The events are designed specifically for both undergraduate and graduate students, but also open to faculty and postdocs. All workshops are $10 each for students and faculty and registration is required.

Workshop Series Schedule:

SAS: February 4 - 5, 2017
Maple: February 23 - 24, 2017
Julia: March 20, 22, 2017
Biosymetrics Augusta: April 22 - 23 , 2017

Participants who attend all workshops will receive a Certificate Of Completion from the Fields Institute.

Overview

Objectives:

This course is an introduction to programming in Julia and it constructed around hands-on examples of its usage. Topics include solving predictive, optimization and simulation problems. The material is selected in order to help the participants learn when Julia can be a language of choice for solving practical problems.

The course assumes no previous knowledge of Julia and is for users who want to learn how to write Julia programs. During the course we will discuss similarities and differences to other open-source data science languages (R and Python) but their in-depth knowledge is not required (although some experience with other scientific computing language would be helpful).

Why Julia:

Julia is a high-level, high-performance dynamic programming language for technical computing that recently rapidly gains popularity. Scientific computing has traditionally required the highest performance, yet domain experts have largely moved to slower dynamic languages for daily work. The Julia programming language tries to solve this problem: it is a flexible dynamic language, appropriate for scientific and numerical computing, with performance comparable to traditional statically-typed languages. Julia features optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation. During the course we will explain how these features of Julia work and how they make Julia different form R or Python.

Participation Requirements:

The workshop will be hands-on and participants should bring their laptops and have Julia environment installed. This is a free download and is available at http://julialang.org/downloads/.

You can use your favorite text editor to develop in Julia. You can find extensions for major text editors at https://github.com/JuliaEditorSupport (installing them is optional). In particular Juno project (http://junolab.org/), is a popular and flexible IDE for Julia that is built on top of Atom.

If you want a packaged installation of Julia, Juno editor and most popular extension packages you can consider downloading JuliaPro at http://juliacomputing.com/products/. During the workshop we will use Julia version 0.5.

If you encounter any problems with installation of Julia please contact Bogumił Kamiński - e-mail:

Instructor:

Dr. Bogumił Kamiński

Bogumił Kamiński (http://bogumilkaminski.pl/about/) is Head of Decision Analysis and Support Unit at Warsaw School of Economics and is a member of Management Committee of European Social Simulation Association (ESSA) and Membership and Member Services Committee of Institute for Operations Research and Management Sciences (INFORMS). His field of expertise is operations research, with special focus on industrial applications of forecasting, optimization and simulation. He has 15 years of experience in teaching data science related topics at undergraduate, graduate and MBA courses.

Schedule

Monday, March 20th, 2017: Getting started with Julia*
08:45 to 09:00	Check In
09:00 to 10:30	Introduction to Julia. Basic Julia workflow
10:30 to 10:45	Coffee Break
10:45 to 12:00	Building blocks of Julia: variables, control flow, methods, types
12:00 to 13:00	Lunch Break
13:00 to 15:00	Julia code analysis and performance evaluation; graphics
15:00 to 15:30	Coffee Break
15:30 to 17:00	Basics of working with text, graphics and code parallelization

Wednesday, March 22th, 2017: Basic data science with Julia examples**
09:00 to 10:30	Prediction: estimation of piecewise-linear model; Tikchonov regularization
10:30 to 10:45	Coffee Break
10:45 to 12:00	Simulation: simple queuing system analysis; simulation of cellular-automata; comparison of A/B testing policies
12:00 to 13:00	Lunch Break
13:00 to 15:00	Optimization: exact solution of k-medoids clustering; depot location problem
15:00 to 15:30	Coffee Break
15:30 to 17:00	Wrap-up: overview of more advanced topics in Julia programming