This course introduces you to Julia as a first programming language. Julia is a high-level, high-performance dynamic programming language developed specifically for scientific computing. This language will be particularly useful for applications in physics, chemistry, astronomy, engineering, data science, bioinformatics, and many more. You can start programming with Julia within Coursera and it can also be used from the command line, program files, or a Jupyter notebook.
Julia Scientific Programming
Gain insight into a topic and learn the fundamentals.
4.4
(428 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 19 hours
Learn at your own pace
94%
Most learners liked this course
What you'll learn
How to start coding in the Julia programming language.
The advantages and capacities of Julia as a computing language.
Skills you'll gain
Details to know
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Assessments
15 assignments
Taught in English
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There are 4 modules in this course
A warm welcome to Julia Scientific Programming. Over the next four weeks, we will provide you with an introduction to what Julia can offer. This will allow you to learn the basics of the language, and stimulate your imagination about how you can use Julia in your own context. This is all about you exploring Julia - we can only demonstrate some of the capacity and encourage you to take the first steps. For those of you with a programming background, the course is intended to offer a jumpstart into using this language. If you are a novice or beginner programmer, you should follow along the simple coding but recognising that working through the material will not be sufficient to make you a proficient programmer in four weeks. You could see this as the ‘first date’ at the beginning of a long and beautiful new relationship. There is so much you will need to learn and discover. Good luck and we hope you enjoy the course! Best wishes, Henri and Juan
What's included
16 videos6 readings7 assignments1 discussion prompt
In our case study we use Julia to store, plot, select and slice data from the Ebola epidemic. Taking real data, we explain how to work in Julia using arrays, and for loops to work with the structures. By the end of this module, you will be able to: create an array from data; learn to use the logical structures IF and FOR ; conduct basic array slicing, getting the incidence data and generating total number of cases; use Plots to generate graphs and plot data; and combine the Ebola data outputs to show a plot of disease incidence in several countries.
What's included
9 videos1 reading3 assignments1 peer review
in this week, we demonstrate how it is possible to use Julia in the notebook environment to interpret a model and its fit to the data from the Ebola outbreak. For this, we apply the well-known SIR compartmental model in epidemiology. The SIR model labels three compartments, namely S = number susceptible, I =number infectious, and R =number recovered. By the end of this module, you will be able to: understand the SIR models; describe the basic parameters of an SIR model; plot the model-predicted curve and the data on the same diagram; adjust the parameters of the model so the model-predicted curve is close (or rather as close as you can make it) to the data.
What's included
9 videos1 reading2 assignments1 peer review
As a scientific computing language, Julia has many applications and is particularly well suited to the task of working with data. In this last module, we will use descriptive statistics as our topic to explore the power of Julia. You should see this week as offering you a chance to further explore concepts introduced in week one and two. You will also be introduced to more efficient ways of managing and visualizing your data. We have also included additional, honors material for those who want to explore further with Julia around functions and collections. By the end of this module, you will be able to: 1. Practice basic functions in Julia 2.Creating random variables from data point values 3. Build your own Dataframes 4. Create a variety of data visualisations 5. Conduct statistical tests 6. Learn how to export your data.
What's included
9 videos4 readings3 assignments1 peer review1 ungraded lab
Instructors
Instructor ratings
4.5 (82 ratings)
Offered by
Recommended if you're interested in Data Analysis
Rice University
University of Michigan
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
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Learner since 2021
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"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
4.4
428 reviews
- 5 stars
62.32%
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25.58%
- 3 stars
7.44%
- 2 stars
1.62%
- 1 star
3.02%
Showing 3 of 428
MH
4
Reviewed on Feb 1, 2017
This course is more like a lesson for data science, most of them are organized for plotting curve and making diagrams. This is good. But I was expected a more detailed lesson toward Julia itself.
AS
4
Reviewed on May 19, 2021
Overall I learned a lot, but the pacing was strange and some of the things in the quizzes were not taught well or were taught in the chapter after the quiz.
GD
5
Reviewed on Dec 14, 2020
Really great pacing, practical examples and quizzes without being overwhelming. Great for both beginners in programming and statistics, and for those with some experience. Awesome lesson, thank you!
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