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Content is out of date and no one responds to questions on discussion board.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.

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the amount of ground covered in terms of pure Julia code is very basic and sometimes methods shown are suboptimal or outdated.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.

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.

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!

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

It is an excellent course to gain an understanding and the central insights of Julia.

Un excelente curso para iniciarse en la programación cientifica.

Good course and excellent explanation by the professors

Nice introduction into Julia. The content is well structured and provides enough details so you can write your own (basic) data science routines in Julia. I also liked the Ebola use-case and to learn about modelling such an outbreak.

Personally I would prefer more videos about some of the more advanced language features and less about plotting libraries and the likes. But I guess that really depends what you are looking for.

Overall a highly recommended course.

Nice introduction to Julia based on some examples and exercices. Julia is a fast changing-language and some of the course would require little updating to match the latest releases. Also since juliabox.com does not exist anymore and no other alternative is indicated, it currently requires the ability to run Jupyter notebooks for the assignements. Last note, maybe one of the teacher shouldn't read the slides that much :)

The course is great for beginners to get in touch with the application of a new programming language.

For someone with at least intermediate knowledge on the concepts, it is quite easy to crack the assignments. But still, this course gives you an insight into how easy it has made coding and visualization for people in the field of data science!