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 is designed to address the requirements of high-performance numerical and scientific computing while being effective for general-purpose programming. You will be able to access all the available processors and memory, scrape data from anywhere on the web, and have it always accessible through any device you care to use as long as it has a browser. Join us to discover new computing possibilities. Let's get started on learning Julia. By the end of the course you will be able to: - Programme using the Julia language by practicing through assignments - Write your own simple Julia programs from scratch - Understand the advantages and capacities of Julia as a computing language - Work in Jupyter notebooks using the Julia language - Use various Julia packages such as Plots, DataFrames and Stats The course is delivered through video lectures, on-screen demonstrations, quizzes, and practical peer-reviewed projects designed to give you an opportunity to work with the packages.
I thoroughly enjoyed this course. The material is very clear and the exercises were enjoyable. Keen to learn more! It would be great to see an advanced Julia course here!
Good and enthusiastic teachers. I found the difficulty a bit too easy but it was a good introductory course and I was new to Julia, so it served its purpose.
Amazing Explanation, in-Depth and concise, Well taught course, Loved the Julia Programming and it's inbuilt functions.
Good introductory course - but assignments can be made more interesting with actual problems in scientific areas or basic algorithms instead of command knowledge testing.
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.
Excellent, engaging teaching that makes me want to use Julia language (and Jupyter notebooks) all the time. As the language evolves, you need to adjust to newer Julia versions - just a part of fun.
It is very simple to get hands on with examples and practice quizzes. However, it will be so much better if updated with Julia v. 1.0+.
The course is cool but the content could be better organized. The peer-review assignment is so hard to get reviews.
The course is awesome! I could learn much more than I expected about the Julia Language! I liked also the different contexts and applications shown in the course!
A very good Introductory Course on Julia, and they deal bit of epidemiology which even makes it better. It's a very interesting topic.
The course was excellent. Thank you for all the work that has gone into this. I recommend it for anyone who wishes to start using Julia or is simply curious about Julia.
I learned many thinks in Julia scientific programming. they want to add many thinks link simulation.It could help many people
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Julia is now at V1.3. The course was based on Julia 0.6 and done around the time Julia 1.0 was close to release. The material sorely needs to be updated.
Content is out of date and no one responds to questions on discussion board. For example, the last quiz had several bugs that make it impossible to answer correctly, but people have been pointing this out for months on the discussion board with no response from course moderators.
Lectures are using different version of Julia from the notebooks, so packages don't work or you need to use a different package. Quizzes often refer to things that were not included in any of the lectures or notebooks. Note that I passed the course, but it was super frustrating.
Amazing course! I learned a lot during this course. The assignments made me improve my coding skills and the questions were challenging enough. The instructors are engaging and innovative!
A clear and well presented course.
Suitable for newcomers and relatively new users of Julia. The course notes (Jupyter notebooks) are a useful reference after the course.
Great course, well taught and thorough. I would only add that it needs to be updated from the version in the course (0.4.6) to the latest, full version of Julia, e.g. (1.x). I would have given 5 stars but for that fact.
Honestly, I was a bit underwhelmed at the content of the course. There is a fairly large amount of overlap in the content covered by the two instructors which makes Weeks 1-3 and Week 4 somewhat redundant, and the amount of ground covered in terms of pure Julia code is very basic and sometimes methods shown are suboptimal or outdated.
Excellent, engaging teaching that makes me want to use Julia language (and Jupyter notebooks) all the time. As the language evolves, you need to adjust to newer Julia versions - just a part of fun.
It was very exciting. I widen further, my computing horizons
Hope, that the university will launch in the near future, a more advanced course of "Julia Scientific Programming"
I general, I can recommend this course.
I was able to learn the basics of Julia language.
The three things that stand out for me are:
Data structures, Plotting, Data Frames.
Parallel computing is not part of this course.
There are a few weaknesses in this course, but it seems that the course is being reviewed frequently, in order to adress issues, and enhance the content.
Weaknesses I found were:
Some typos and dead links in tests
In some tests, correct answers (when the code works in the computer language) are graded as incorrect, because the grading software only accepts one correct answer, rather than a variety of correct answers.
Some tests are testing your ability to pay attention in small details, rather than your ability to understand the computer language
There are not enough participants in the course for mutual grading to work properly. Instead instructors have to do grading, which often occurs with a delay of a few days.
I suggest that the course material should give more references to side information (e.g. cheat sheet for markup language, definition of SIR model, links to collections of open datasets)
I found this a great introductory course on Julia for scientists. I specially loved the classes about data frames, user-defined types, and the Gadfly package.
The course needs to be updated, though, videos, exercises and Julia notebooks. Nothing too serious, yet, but some of the contents need to be reviewed in face of the current development status of Julia.
I am looking forward to see a course on data science with Julia.
Julia is now in 1.5, there is barely any difference with what is done in the course. But it might be good to update the course to engage better with the language. Besides that, I would suggest more visual/animated elements to explain concepts instead of just Jupyter Notebook text (which is not bad at all, but a more dynamic lecture could make it even more interesting).
Solid overall course for both introduction to Julia as well as programming as a whole. A few aspects could be updated. Juliabox is about to be sunset(discontinued) so it would be nice for them to walk users through using JupyterLabs in Anaconda for loading the Julia notebooks needed for the course assignment. Atom and Juno are briefly mentioned but I would recommend using these to anyone looking to take this course. Some of the code used in the course is now deprecated, but using the error messages it is not that difficult to debug and update - though it does slow down the overall speed with which the course can be completed. Overall, I would highly recommend this to anyone involved in data science or interested in programming languages in general
This course was a good introduction to Julia, and it has helped me start to use Julia regularly for data analysis. I think the course would benefit from being longer and covering a greater number of topics, as it really just scratches the surface of using a coding language for scientific analysis, but in its present form it is definitely worth the money. One specific thing that the course taught me which I may not have learned had I purely taught myself, is the importance of Jupyter-style notebook environments for Julia, which given the import times of some modules, are more useful for Julia than Python. Though of course, Julia is also used in conventional IDEs for different types of work, as the course mentions.
This is a great introductory course for getting acquainted with the Julia programming language and its many data science applications. However, the curriculum is too easy for someone with a prior DS background or knowledge of other language such as Python. It's interesting to see how similar tasks such as array manipulation and data visualization are done in Julia, but for a better command of the language, I recommend that one examine other available literature in addition to this course.
Not a bad course as an introduction to Julia. It gives a good feel of how Julia defines functions, uses for loops, arrays, dataframes, and statistics. It would be of interest to those who have prior experience with other statistical programming languages such as R and Python.
The course is awesome! I could learn much more than I expected about the Julia Language! I liked also the different contexts and applications shown in the course!
This course needs updating. Some of the commands are deprecated.
But overall it is an excellent introductory course to Julia.
Teaches you lost of things in the process of making you understand the power of Julia data visualization.
I think the videos and jupyter notebooks should be updated to reflects the Julia 1.4 version.
Strong introduction to Julia.
Updating some materials would deprecated functions would be good