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Avis et commentaires pour l'étudiant pour Big Data Analysis with Scala and Spark par École polytechnique fédérale de Lausanne

4.7
2,074 notes
418 avis

À propos du cours

Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance. Learning Outcomes. By the end of this course you will be able to: - read data from persistent storage and load it into Apache Spark, - manipulate data with Spark and Scala, - express algorithms for data analysis in a functional style, - recognize how to avoid shuffles and recomputation in Spark, Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Parallel Programming: https://www.coursera.org/learn/parprog1....

Meilleurs avis

CC

Jun 08, 2017

The sessions where clearly explained and focused. Some of the exercises contained slightly confusing hints and information, but I'm sure those mistakes will be ironed out in future iterations. Thanks!

CR

Apr 10, 2017

Great introduction to spark. Fun assignments. Since it was the first ever session, there were quite a few kinks with the assignments. But the discussion forums rescued me any time I was stuck.

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376 - 400 sur 402 Examens pour Big Data Analysis with Scala and Spark

par Aleksei M

Apr 19, 2017

The selection of content is very good. Yet, the course requires solid amount of polishing. I had a feeling that the real teaching started only in the 4th week, with lots of walking around the subject in the beginning. Worth to mention that in contrast to the first 3 courses in Scala specialisation this one is very heavy on visual aspect. You actually have to see the picture, as the voice channel just references it.

I can certainly recommend this course to people curious about data analysis. If you have had some experience and would like to try Spark or get better at it, then probably a good book can save your time.

par Jeff B

Jul 05, 2017

The course content is fine, I did learn about Spark and how to work with it in Scala. The only disappointing thing is the homework assignments all use Spark in local mode, so it runs only on my laptop. I really wanted to see an assignment run in a real cluster, for real. So having some kind of cookbook recipe to follow for putting an assignment into Databricks would caus

par Korbinian K

Oct 10, 2017

I really liked the lectures and the good and fun explanations by the instructor. However, I found the assignments over complicated with unnecessary machine learning concepts involved. I think a course about Spark should be about core Spark ONLY and applications to machine learning should happen in a separate course.

par Jim N

Apr 12, 2017

I understand that creating such a course is a tremendous amount of work. Let me congratulation the developers for all their hard work. I learned a lot. I'm glad I good the course, and I'm speechless that I finally finished it with a passing albeit imperfect score. This was the maiden voyage and it has some problems that should probably be corrected. That's my opinion.

There were generally two sets of challenges with each assignment exercise (1) undemanding the scala/big-data component, and (2) understand what's being asked. Sometimes the comment in the code are wrong or misleading. In particular in the 2nd set of exercises. The functions are very poorly specified, and there are no unit tests. there should be one or the other. Without a specification the user cannot write his own tests. In addition the 2nd exercise is intended to be do-able, but not efficiently until the user views week 3's videos. Because of this the text output is EXTREMELY verbose, making it impossible to read the error messages and understand them. When the user finally submits the assignment it fails and the grader gives misleading error messages. A failing unit test should at the very least explain what the test was, what the measured value was, and what was expected. This is especially true when the user is forbidden from looking at the actual testing code. An additional problem with exercise 2, is that the student is asked to calculate a particular percentage, but it is not explained what this is a percentage OF and there are several ways to interpret it. I didn't realize until wasting 2 weeks that there was an alternate interpretation which I could try.

In the 3rd exercise some of the comments in the code are wrong/misleading. Particularly with regard to classifiedColumns. comment #3 should read:* 3. other activities (leisure). These are the columns starting with “t02”, “t04”, “t06”, “t07”, “t08”, “t09”, “t10”, “t12”, “t13”, “t14”, “t15”, “t16” and “t18” BUT EXCLUDING those which are not part of the previous groups only. Otherwise the sentence is at best grammatically incorrect, and at worst misleading.

Another significant issue with this "Bit Data" course is that it assumes a user understands data base and can construct sql queries. Well, such background is not a prerequisite, but the student needs such information to successfully finish the exercises. I can understand the temptation by the course developers to include a section on SQLspark, as this is a very powerful set of spark libraries, but I believe it is beyond the prerequisites. I could not have finished the course if I hadn't had an SQL/data-base expert in the same office, who I could pose questions to.

I hope you find my comments useful.

par Benjamin S

Sep 12, 2017

The lectures are really great with vivid and easy-to-follow explanations on complex topics.

However, the exercises don't seem to match the lectures very well and may confuse you. I would prefer to apply the things I learned in the lectures.

par Ioannis A

Sep 25, 2018

course needs to be updated

par Rob S

Oct 15, 2018

Lots of good knowledge in this course. Took a star away for the second exercise, which taught me more about how to pass the second exercise than anything useful. Took a second star away for not having test coverage that helps the students. You wind up submitting and waiting for the grader to run.

Yes - I know we can write our own tests, but in a graded course like this with sometimes unclear problems the tests can make all the difference in the world.

par Aaron S

Jun 04, 2017

Very average. Lectures could be a fraction of their current length, too much time spent rephrasing the same point (sometimes 3+ times!). It was driving me nuts, my mind would wonder if I didn't focus. It would be nice to have local tests that incrementally check progress similar to Andrew Ng's Machine Learning coursera.

par Aaron H

Jan 23, 2018

Instructor was good and knew what she was talking about. The assignments were also good, but the grading was weird. Spent a lot of time try to figure out the unwritten requirements that would make Coursera's tests pass.

par Gian U L

May 07, 2017

In the assignments, I had the feeling that the goal was more "guess what they want" than "write it correctly using what you have learnt". Stating more clearly the requirements and improving error messages from the grader may help.

par Vesa P

Jul 03, 2017

I learned a lot and the lectures were good.

The feedback from the automatic grader was sometimes absolutely awful. Cache an RDD in the main -> grader does not execute main -> OOM exception with no stack trace. I guess it prepares you for real life since EMR Spark also has absolutely useless log output if you go OOM.

Thank goodness for forums.

par Luis V

Oct 01, 2017

Good course but with many outdated concepts (mostly valid for Spark 1.x) and some pitfalls. Need many improvements, actualization and some reshaping in the distribution of the topics and sessions of the course. The topics left for the last week are some of the most important and central in current Spark 2.x and they include at least as many fundamental concepts as the rest of the course.

par Harold O

Apr 16, 2017

This is a really excellent course. It deals with difficult content clearly, thoroughly and at a good pace. I would now be happy to use Spark in my work and feel I have a strong base to go on to further study. The exercises are well conceived covering fairly realistic use cases and are of about the right complexity. BUT, the feedback from the automatic grading really lets it down.

The auto grading tests are at too high a granularity, each test tests too many things. They give cryptic errors such as '<some random element> is empty'. You have no idea why the element is empty as the students are given a different dataset to the one tested on. Moreover, the failed tests are invariably nothing to do with learnt course content, but trivial rounding/formatting errors. The forums are a lifesaver, but I still spent a good two weekends working on bugs once I'd got the assignments 70% right. I would not recommend to a completist!

All the same, thanks to Heather for the wonderful work she's done putting it together and the excellent lectures she gives.

par Sam Z

May 03, 2017

good: the concepts and on-hands skills taught by the course are good.

bad: the assignments. you cannot complete them just by following the course material, forcing you to waste quite a lot of time either: (1) learning from other sources; (2) looking for answers on the forum; or (3) brute forcing an answer till rage quitting :)

another bad point: the course is supposed to be focused on spark & big data analysis but it has 1-2 lectures (around 40-60 mins) pretty much devoted to showing some SQL. << this could be summarized to around 10-20 mins and/or give a link to quickly learn/try to some other source.

par Rafael G

Mar 31, 2017

The material in this course is very interesting. However, there were a few important issues:

Lots of typos in the slides

Lots of problems with the assignments

At the end, I feel like a beta-tester (it would be OK if it was clearly stated and if we had a discount).

It could also be nice to add 1 or 2 weeks to this course.

par Yann L M

Mar 19, 2017

Lectures are great. Explanation are very clear. Assignment was having issue like incorrect and/or vague reporting which made them needlessly painfull. I'm quite sure that the next iteration of this course can get a 5 star rating, but for now, it's only 3.

par Alexandre V

Nov 25, 2017

Explanations are OK and it's a good investment. However, I'm mixed about the courses: the teacher is speaking really fast with slides full of text. It's sometime hard to keep my concentration (compared to previous courses of the specialization). Still, I would recommend this course.

par Lance F

Mar 27, 2017

This course took a lot of work to create. I would have like more quizzes during the lectures and the assignments to have walk through the steps more. The best course I have seen online is the Machine learning course by Andrew Ng. https://www.coursera.org/learn/machine-learning/home/welcome.

I did really enjoy the course. Thank you.

par Kyle J

May 22, 2019

Pretty good, but one of the assignments was poorly set up. Some of the provided code was broken and it was very hard to debug.

par Owen N

Apr 09, 2017

Course material was pretty good, but the lectures were hard to watch. Lots of editing problems, and blurring on the text (gave me a headache several times). Would rate higher if the videos were improved.

par Mikołaj J

Jun 05, 2017

So many mistakes in the slides. Coding exercises are so hard to comprehend, it's tough to know what you are trying to achieve. I have already done a course in spark, this was supposed to be just refresher, but now I'm just confused...

par José F

Apr 18, 2017

a let down and not up to par with other courses in the series.

Huge amount of time is wasted bsically repeating that the API is close to scala collections'. The huge amout of time is wasted again on very simple dataframe APIs including several slides presenting the show() function. Allow me to repeat : several slides presenting the show() function.

Finally the assignments are unimaginative and mechanical. The desciption is really confuse and most of the time is spend trying to chase small differences away to please the grader.

The sole part of the course that seemed interesting was the shuffling one, that was unfourtunately ignored on the assignments

Not related to the course but to spark: What utter mess are dataframes and datasets filled with boilerplate type conversions and runtime erros. I shy away in disgust form this untyped IDE-unfriendly monstruosity.

par Vladyslav S

May 06, 2017

Relatively decent video lectures, if not that blurry which makes text hard to read. Accompanied with awful practice lessons: - code templates are written with little to no style, even file reading is done in 3 different ways in all 3 lessons; - grader output is very confusing and almost useless; - unit tests, very useful to avoid some common caveats, were present in the first lesson, disappear completely in the last one.

Probably following spark's programming guide is better time investment, even if it misses some "humanity" of video lectures

par Марко И

Apr 10, 2017

I don't know what happened but it seems they had technical or some other problem while preparing this course. Some assignments were more oriented to solving marginal problems then using Spark and distributed and parallel computing. And that is really annoying. Previous 3 courses were great, maybe this one will improve.

par Dan O

Mar 25, 2017

Slow videos repeating several times the same thing (not a pedagogical / "good to fix an idea" kind of repetition), which makes them hard to follow.

However the worst are the exercises: the first time after 3-4 other Coursera Scala related courses where I have to actively check the forums for minute details about what is expected / implied for the solutions to pass the grading.

Things like what to do when updating the kmeans and you have duplicates, subtle differences between average and mean, etc. ...

In all other courses the expectation of the exercises were sufficiently clear and straight forward that I never had to check the forums to solve them.

Also, the code style of the exercises is literally an anti-pattern in idiomatic Scala, against everything learnt in the previous Scala courses: "var" all over the place, low level loops like in C or Java, etc. ...