Online tutorials

Upcoming

Intro to Julia, by Jane Herriman on July 20, 2018 at 10AM PDT/1PM EDT/19:00CET
Intro to Julia for data science, by Huda Nassar, date TBD

Past

Intro to Julia, by Jane Herriman (December 19, 2017)
Intro to Julia by Jane Herriman (January 25, 2018)
Intro to solving differential equations in Julia, by Chris Rackauckas (February 6, 2018)
Intro to Julia, by Jane Herriman (February 15, 2018)
Intro to JuliaDB, a package for working with large persistent data sets, by Josh Day and Shashi Gowda (February 28, 2018)
Intro to Julia, by Jane Herriman (April 6, 2018)
Introducción a Julia en español, by Miguel Raz Guzmán (April 7, 2018)
Intro to Julia, by Jane Herriman (May 4, 2018)
Intro to dynamical systems in Julia, by George Datseris (May 10, 2018)
Intro to Julia Data Frames, by Bogumił Kamiński (May 24, 2018)
Intro to Julia, by Jane Herriman (June 8, 2018)
Intro to the Queryverse, a Julia data science stack, by David Anthoff (June 14, 2018)

Get jupyter notebooks for all past tutorials here or run them directly on JuliaBox.

Books

Julia: High Performance Programming by Ivo Balbaert, Avik Sengupta, Malcolm Sherrington (697 pages; published: November 2016; ISBN: 9781787125704) In this learning path, you will learn to use an interesting and dynamic programming language—Julia! This book is a combination and curation of the three separate books by the three authors.
Julia Programming for Operations Research: A Primer on Computing by Changhyun Kwon (246 pages; published: 2016-05; ISBN: 978-1533328793) This book aims to teach how one can solve various optimization problems arising in operations research and management science.
Julia High Performance by Avik Sengupta (120 pages; published: 2016-05; ISBN: 9781785880919) This is a book about performance optimisation of Julia programs, showing how to design and write Julia code that fully realises the potential speed of the language and its libraries.
Mastering Julia by Malcolm Sherrington - published by Packt Publishing (410 pages; published: 2015-07; ISBN: 9781783553310)
Getting Started with Julia Programming by Ivo Balbaert - published by Packt Publishing (214 pages; published: 2015-02-28; ISBN: 9781783284795)
Seven More Languages in Seven Weeks by Bruce Tate, Fred Daoud, Jack Moffit and Ian Dees - published by The Pragmatic Programmers (350 pages; published: 2014-11-15; ISBN: 978-1-94122-215-7) This book contains a Julia tutorial chapter (written by Jack Moffitt and Bruce Tate) for programmers new to Julia, which is very good, with nice examples and exercises.
Julia for Data Science by Anshul Joshi (348 pages; published: 2016-09; ISBN: 9781785289699) Explore the world of data science from scratch with Julia by your side
Julia for Data Science by Zacharias Voulgaris PhD (415 pages; published: 2016-09-01; ISBN: 9781634621304). Master the essentials of data science through the Julia programming ecosystem (no prior knowledge of the language is required), accompanied by a variety of interesting examples and exercises.
Julia Cookbook by Jalem Raj Rohit - published by Packt Publishing (172 pages; published: 2016-09; ISBN: 9781785882012)
Julia Solutions by Jalem Raj Rohit - A comprehensive guide to learn data science for a Julia programmer - Produced by Packt Publishing (2 hours and 52 minutes long; published: January 31, 2017; ISBN: 9781787283299)
Getting Started with Julia by Erik Engheim - Learn the new language Julia for high performance technical computing - Produced by Packt Publishing (9 hours and 50 minutes long; published: March 31, 2017; ISBN: 9781786462978)
Learning Julia by Anshul Joshi, Rahul Lakhanpal (316 pages; published: November 2017; ISBN: 9781785883279). This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set.

Tutorials

A Deep Introduction to Julia for Data Science and Scientific Computing by Chris Rackauckas
Programming in Julia (Quantitative Economics) - by Thomas J. Sargent and John Stachurski. Along with being a complete textbook with Julia code for macroeconomics, this also is a very good introduction to Julia.
The Julia Express (featuring Julia 1.0) by Bogumił Kamiński
Introducing Julia wikibook

Resources

Julia in the classroom

Julia is ready for the classroom. We encourage instructors to participate in the Julia community resources for questions about Julia or specific packages. This page puts together various resources that instructors may find useful. Tutorials and other learning materials are in the learning section of the website.

MOOCs teaching Julia

Coursera, University of Cape Town
- Julia Scientific Programming online course, (Dr. Juan H Klopper, Dr. Henri Laurie) a
edX MITx
- 15.053x, Optimization Methods in Business Analytics MOOC (massive online open course), (Prof. James Orlin)

Classes using Julia for teaching

Julia is now being used in several universities and online courses. If you know of other classes using Julia for teaching, please consider updating this list.

AGH University of Science and Technology, Poland
- Signal processing in medical diagnostic systems (Tomasz Pieciak), Spring 2015
Arizona State University
- MAT 423, Numerical Analysis (Prof. Clemens Heitzinger), Fall 2014
Azad University, Science and Research Branch
- CE 3820, Modeling and Evaluation (Dr. Arman Shokrollahi), Fall 2014
Brown University
- CSCI 1810, Computational Molecular Biology (Prof. Benjamin J. Raphael), Fall 2014
Budapest University of Technology and Economics
- [Applications of Differential Equations and Vector Analysis for Engineers II.] (Brigitta Szilágyi)
City University of New York
- MTH 229, Calculus Computer Laboratory (Prof. John Verzani), Spring 2014. Also see the MTH 229 Projects page.
Cornell University
- CS 5220, Applications of Parallel Computers (Prof. David Bindel), Spring 2014
École Polytechnique Fédérale de Lausanne [CIVIL 557] Decision-aid methodologies in transportation (Mor Kaspi, Virginie Lurkin), Spring 2017
Einaudi Institute for Economics and Finance, Rome
- Econometrics of DSGE Models (Giuseppe Ragusa)
Emory University
- MATH 346, Introduction to Optimization Theory (Prof. Lars Ruthotto), Spring 2015
- MATH 516, Numerical Analysis II (Prof. Lars Ruthotto), Spring 2015
Federal Rural University of Rio de Janeiro (UFRRJ)
- TM429, Introduction to Recommender Systems (Prof. Filipe Braida), Fall 2016, Spring 2017
Federal University of Alagoas (Universidade Federal de Alagoas, UFAL)
- COMP272, Distributed Systems (Prof. André Lage-Freitas): 2015, 2016, and 2017
Federal University of Paraná (Universidade Federal do Paraná, UFPR)
- CM103, Mathematics Laboratory (Prof. Abel Soares Siqueira): 2016, 2017, and 2018
- CM106, Nonlinear Optimization (Prof. Abel Soares Siqueira): 2018
Federal University of Uberlândia, Institute of Physics
- GFM050, Física Computacional (Prof. Gerson J. Ferreira), Fall 2016
IIT Indore
- ApplNLA, Modern Applications of Numerical Linear Algebra (Prof. Ivan Slapnicar), June 2016
Iowa State University
- STAT 590F, Topics in Statistical Computing: Julia Seminar (Prof. Heike Hofmann), Fall 2014
Luiss University Rome, Department of Economics and Finance
- Econometric Theory (Giuseppe Ragusa)
Massachusetts Institute of Technology (MIT)
- 6.251/15.081, Introduction to Mathematical Programming (Prof. Dimitris J. Bertsimas), Fall 2015
- 18.06, Linear Algebra: Fall 2015, Dr. Alex Townsend; Fall 2014, Prof. Alexander Postnikov; Fall 2013, Prof. Alan Edelman
- 18.303, Linear Partial Differential Equations: Analysis and Numerics (Prof. Steven G. Johnson), Fall 2013–2016.
- 18.337/6.338, Numerical Computing with Julia (Prof. Alan Edelman). Fall 2015 (IJulia notebooks). Fall 2013–
- 18.085/0851, Computational Science And Engineering I (Prof. Pedro J. Sáenz)
- 18.330, Introduction to Numerical Analysis (Dr. Homer Reid), Spring 2013–2015
- 18.335, Introduction to Numerical Methods (Prof. Steven G. Johnson), Fall 2013, Spring 2015
- 18.338, Eigenvalues Of Random Matrices (Prof. Alan Edelman), Spring 2015
- 18.S096, Performance Computing in a High Level Language (Steven G. Johnson, Alan Edelman, David Sanders, Jeff Bezanson), January 2017.
- 15.093/6.255, Optimization Methods (Prof. Dimitris Bertsimas and Dr. Phebe Vayanos), Fall 2014
- 15.S60, Software Tools for Operations Research (Iain Dunning), Spring 2014
- 15.083, Integer Programming and Combinatorial Optimization (Prof. Juan Pablo Vielma), Spring 2014
Northeastern University, Fall 2016
- MTH3300: Applied Probability & Statistics
Óbuda University, John von Neumann Faculty of Informatics, Institute of Applied Mathematics
- [Intelligent Development Tools (Hungarian)]
- [Intelligent Development Tools (English)]
- [Fundamental Mathematical Methods (English)]
Pennsylvania State University
- ASTRO 585, High-Performance Scientific Computing for Astrophysics (Prof. Eric B. Ford), Spring 2014 - github repo
- ASTRO 585, High-Performance Scientific Computing for Astrophysics (Prof. Eric B. Ford), Fall 2015 - github repo
Pontifical Catholic University of Rio de Janeiro (PUC-Rio)
- Programming in Julia (Prof. Thuener Silva), Summer 2017
- Linear Optimization (Prof. Alexandre Street), Spring 2017
- Decision and Risk Analysis (Prof. Davi Valladão), Fall 2015
Purdue University
- CS51400, Numerical Analysis (Prof. David Gleich), Spring 2016
Royal Military Academy (Brussels
- ES123, Computer Algorithms and Programming Project (Prof. Ben Lauwens), Spring 2018
- ES313, Mathematical modelling and Computer Simulation (Prof. Ben Lauwens), Fall 2018
“Sapienza” University of Rome, Italy
- Operations Research (Giampaolo Liuzzi), Spring 2015
- Optimization for Complex Systems (Giampaolo Liuzzi), Spring 2016
Sciences Po Paris, Department of Economics, Spring 2016.
- Computational Economics for PhDs (Florian Oswald)
SGH Warsaw School of Economics, Poland
- 223490-0286, Statistical Learning Methods (Bogumił Kamiński): Fall 2017, Spring 2018
- 234900-0286, Agent-Based Modeling (Bogumił Kamiński): Fall 2017, Spring 2018
- 239420-0553, Introduction to Deep Learning module (Bogumił Kamiński): Spring 2018
Southcentral Kentucky Community and Technical College
- CIT 120 Computational Thinking (Inst. Bryan Knowles), Online, Fall 2017
Stanford University
- AA222, Introduction to Multidisciplinary Design Optimization (Prof. Mykel J. Kochenderfer), Spring 2014
- AA228/CS238, Decision Making under Uncertainty (Prof. Mykel J. Kochenderfer), Fall 2014
- EE103, Introduction to Matrix Methods (Prof. Stephen Boyd), Fall 2014, Fall 2015
- CME 257, Advanced Topics in Scientific Computing with Julia (Mr. Brad Nelson), Fall 2015
- EE266, Stochastic Control (Prof. Sanjay Lall), Spring 2016
TU Dortmund / SFB 823, Germany
- One week introductory course into Julia with applications in statistics and economics (Tileman Conring): Spring 2018
Universidad Nacional Autónoma de México
- Física computacional (Prof. David P. Sanders), Fall 2014
- Métodos numéricos para sistemas dinámicos (Prof. Luis Benet), Fall 2014
- Métodos numéricos avanzados (Prof. David P. Sanders and Prof. Luis Benet), Spring 2015
- Métodos computacionales para la física estadística (Prof. David P. Sanders), Spring 2015
Universidad Nacional Pedro Ruiz Gallo, Lambayeque, Perú
- Julia: el lenguaje del futuro, Semana de Integración de Ingeniería Electrónica, (Oscar William Neciosup Vera), Spring 2015
University at Buffalo
- IE 572 Linear Programming (Prof. Changhyun Kwon), Fall 2014
University of Antwerp, Faculty of Pharmaceutical, Biomedical, Veterinary Sciences, October 2016
- Computational Neuroscience (2070FBDBMW), Master of Biomedical Sciences, of Biochemistry, of Physics (Michele Giugliano)
University of California, Los Angeles (UCLA)
- Stat M230/Biomath 280/Biostat M280, Statistical Computing, Spring 2017 (Prof. Hua Zhou)
University of Cologne, Institute for Theoretical Physics
- Computational Physics (Prof. Simon Trebst), Summer 2016
- Computational Physics (Prof. Ralf Bulla), Summer 2017
- Statistical Physics (Prof. Simon Trebst), Winter 2017
- Computational Many-Body Physics (Prof. Simon Trebst), Summer 2018
University of Connecticut, Storrs
- CHEG 5395, Metaheuristic and Heuristic Methods in Chemical Engineering (Prof. Ranjan Srivastava), Spring 2018
University of Edinburgh
- Spring 2017, MATH11146, Modern optimization methods for big data problems (Prof. Peter Richtarik)
- Spring 2016, MATH11146, Modern optimization methods for big data problems (Prof. Peter Richtarik)
University of Glasgow, School of Mathematics and Statistics
- An Introduction to Julia, course of Online Master of Science (MSc) in Data Analytics (Theodore Papamarkou), September 2017
University of South Florida
- ESI 6491, Linear Programming and Network Optimization (Prof. Changhyun Kwon), Fall 2015
- EIN 6945, Nonlinear Optimization and Game Theory (Prof. Changhyun Kwon), Spring 2016
University of Sydney
- MATH3076/3976, Mathematical Computing (Assoc. Prof. Sheehan Olver), Fall 2016
Université Paul Sabatier, Toulouse
- Optimization in Machine Learning, (Prof. Peter Richtarik), Fall 2015
Université de Liège
- MATH0462, Discrete Optimization (Prof. Quentin Louveaux), Fall 2016
- MATH0461, Introduction to Numerical Optimization (Prof. Quentin Louveaux), Fall 2016
- MATH0462, Discrete Optimization (Prof. Quentin Louveaux), Fall 2015
Université de Montréal
- IFT1575, Modèles de recherche opérationnelle (Prof. Bernard Gendron), Fall 2017
- IFT3245, Simulation et modèles (Prof. Fabian Bastin), Fall 2017
- IFT3515, Optimisation non linéaire (Prof. Fabian Bastin), Winter 2017-2018
- IFT6512, Programmation stochastique (Prof. Fabian Bastin), Winter 2018
Western University Canada
- CS 2101A, Foundations of Programming for High Performance Computing. (Prof. Marc Moreno Maza), Fall 2013