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Отзывы учащихся о курсе Big Data Analysis with Scala and Spark от партнера Федеральная политехническая школа Лозанны

4.7
звезд
Оценки: 2,431
Рецензии: 504

О курсе

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....

Лучшие рецензии

BP
28 нояб. 2019 г.

Excellent overview of Spark, including exercises that solidify what you learn during the lectures. The development environment setup tutorials were also very helpful, as I had not yet worked with sbt.

CC
7 июня 2017 г.

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!

Фильтр по:

451–475 из 487 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Waqas A

15 нояб. 2020 г.

Overall the course is a highlevel one and suitable for beginners. Specially the assignment of Stackoverflow requires the need of other course in the specialization. I think the instructor need to make assignments reated to the concepts discussed in the video. In the current scenario assignments require a through look at other resources.

автор: Lance F

27 мар. 2017 г.

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.

автор: Aaron S

4 июня 2017 г.

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.

автор: Rafael G

31 мар. 2017 г.

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.

автор: Korbinian K

10 окт. 2017 г.

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.

автор: Andre H

5 авг. 2017 г.

The material of the fourth week is quite dense, this could be split over two weeks (including splitting it into two exercises). The exercise of the fourth week is quite a dissatisfying experience, there is too little detail in the error messages about what failed for students to improve their solution.

автор: Jeni

29 нояб. 2019 г.

It felt like the course material skipped over a great deal of syntax and how-to. It was useful for concepts; but I found that I had to dig a great deal to be able to complete the assignments and that there is a lot of folklore in stackOverflow that potentially send you in a wrong direction.

автор: Alexandre V

25 нояб. 2017 г.

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.

автор: Daniel Z

14 мар. 2020 г.

The assignments are not really well prepared - there is tests provided which is really needed for big amount of data - sometimes it really hard to find a bug. If you tell me that this is my problem to right tests - I'll tell you that I've paid money for that.

автор: Yann L M

19 мар. 2017 г.

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.

автор: Benjamin S

12 сент. 2017 г.

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.

автор: Gian U L

7 мая 2017 г.

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.

автор: Aaron H

22 янв. 2018 г.

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.

автор: Virapat K

2 февр. 2021 г.

The course is good overall. The assignment is OK. The tests should and grading should be more transparent. There are a lot of errors that are difficult to decipher.

Also week 2 assignments have bugs in the codes.

автор: Serg D

14 апр. 2020 г.

It was a good course, much more useful than the first one in the series. I would say too much focus was on the SQL side and not enough on the big data side, which is what i was hope to get from the course.

автор: Aitor S G

24 февр. 2021 г.

The course is okay, but the presentations are blurry, which is annoying. I would like to go more in-depth, like optimizations, stages, partition pruning...

автор: Nikita V

11 мая 2017 г.

I guess it's a goo introduction course into Apache Spark. Meantime I would expect deeper dive into optimizations and algorithms.

автор: Moiseenko A

6 мая 2020 г.

There is not any course support at all.

Provided drafts of program assignments does not satisfy SOLID and Clean-code principles

автор: Kyle J

21 мая 2019 г.

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.

автор: Virginija D

7 авг. 2017 г.

Too entry-level after the first two much more challenging courses by M. Odersky.

автор: Hans R

25 окт. 2020 г.

Explains the core concepts very well, but lacks depth.

автор: Mortatha K H

8 июля 2020 г.

it's hard for beginner understanding this course

автор: Ioannis A

25 сент. 2018 г.

course needs to be updated

автор: Cherniaev A

7 февр. 2021 г.

Good, but outdated course

автор: José F

17 апр. 2017 г.

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.