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

Оценки: 2,411
Рецензии: 498

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:

Лучшие рецензии

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.

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!

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101–125 из 481 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Samuel L

23 мар. 2017 г.

Very well taught and insightful! Especially the combination of slides, and additionally drawn notes on that slides, I found very good.

автор: Zhenduo D

19 нояб. 2017 г.

Interesting and useful course. Could be better if the videos were not as blurred, which made it sometimes hard to read the slides.

автор: Venkat

1 июня 2017 г.

Awesome video lectures by the instructor. The content is very good and i fee i gained good knowledge. I really loved the series.

автор: Iris M

20 дек. 2020 г.

Great lesson, the instructor is doing a great job explaining all concepts and using intuitive examples when needed. Thank you!

автор: Lewis

29 мар. 2017 г.

It was short and sweet. However, I wish the assignments had more unit tests to fill gaps where the instructions weren't clear.

автор: Imre K

22 мар. 2017 г.

Challenging but very enlightening. Requires you to read the docs and figure out a lot of stuff on your own. My kind of course!

автор: Jonathan R

22 авг. 2020 г.

Instructor does a great job of explaining the material and keeping students' interest. Good explanations and use of examples.

автор: Prachi C

14 сент. 2017 г.

An excellent explanation of basic concepts of Spark using Scala. We covered the most important topics and ready to deep dive!

автор: Vladislav B

8 апр. 2017 г.

Very good and interesting videos. Really clear explanations. Practice part worse, but Forum helps with all misunderstandings.

автор: Max Z

3 мар. 2018 г.

Excellent. I learned a lot from this course, even though I did not know much about scala. Strongly recommended this course

автор: VincentWMChan

25 апр. 2017 г.

Pretty nice course. But with one comment on the grading system, sometime the comment & message is not that intuitive enough.

автор: Thierry M

1 февр. 2018 г.

Very complet and accurate about Spark with Scala. Maybe an assignement with MongoDB or the like would be a plus

Thank you

автор: Aleksander S

5 мая 2017 г.

Amazing lectures, and challenging tasks to do on the way. I really enjoyed going through the course, and I learned a lot.

автор: Ubaldo P

10 апр. 2017 г.

It is a course well organized, full of useful notions e with good assignments to assess your progress in learning stuff.

автор: Marazzi F

31 дек. 2020 г.

I loved this course! One of my preferred courses so far. Very engaging and greatly explained! 5 stars totally deserved.

автор: Manuel F B L

21 июля 2018 г.

Muy bueno, Excelente. Todos los conocimientos obtenidos me serán de mucha ayuda en mi camino hacia el mundo de Big Data

автор: joao d s

9 апр. 2017 г.

Really helpful, it's a really good way to get to know how spark works as well as a good round up on basic data mining.

автор: srinivasa k

1 янв. 2018 г.

I really liked the course, it gave me head start on spark definitely there is much more to learn, nice intro course.

автор: OUMOUSS E M

18 июня 2017 г.

Very good course, it is a must for anyone who is starting in spark using scala, thanks a lot, it did really help me

автор: German A S G

22 апр. 2018 г.

Good course, it goes beyond functional algorithms and teaches also about how to improve performance of the cluster

автор: Gregory E

10 мар. 2018 г.

Good course, shows a lot of useful and unobvious things about Spark. But not always has well described assignments

автор: Adrian D

22 дек. 2020 г.

The course is explained very well but the assignments are a bit ambiguous. The requirements can be a bit clearer.

автор: Vlad F

14 мар. 2018 г.

I learned a great deal about the Spark API. I recommend it to anybody eager to self educate after working hours.

автор: Aleksander K

2 апр. 2017 г.

This class is great! Highly recomended! It helped me to understand and perform better at my daily working tasks!

автор: Jevelson S

17 мая 2017 г.

This course is awesome. I got a pretty good idea of spark. In fact this course helped me understand scala well.