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學生對 洛桑联邦理工学院 提供的 Big Data Analysis with Scala and Spark 的評價和反饋

4.7
2,178 個評分
434 條評論

課程概述

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....

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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!

BP

Nov 29, 2019

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.

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76 - Big Data Analysis with Scala and Spark 的 100 個評論(共 417 個)

創建者 Konstantin K

Apr 12, 2017

good stuff, compared to the other similar course in PySpark this one gave me a lot more understanding of how things work in Spark on the low level

創建者 Liqun Y

Jun 30, 2017

Very useful. Dr. Miller apparently did a very good job. I strongly suggest beginners to read the "Learning Spark" book and then take this class.

創建者 Shane

May 10, 2018

Very well-organized courses about Spark ! Really learned some good practical tips. Hope there could be more explanations about the assignments.

創建者 John V M

Apr 25, 2017

Yet another excellent Scala class. Good lectures, good assignments. Clear and understandable, while presenting a whole bunch of new concepts.

創建者 Bennie K

Oct 15, 2017

Really clear and direct. Would love to see another course on the Advanced Spark topics such as Spark Streaming and Spark custom libraries

創建者 Álvaro L L

Jun 11, 2017

A great introduction to Spark !!! An esential course to anyone interested in the field! Thank you very much Heather and the EPFL team !!

創建者 Yacine G

Dec 23, 2018

FANTASTIC!!! I don't even know which was better: the course material quality, the instructor's approach or the assignments. FANTASTIC!!!

創建者 Buddhika L S

Feb 17, 2018

This is a good course if you already know the Scala language and looking to improve your theoretical understanding of Spark programming.

創建者 Neha B

May 19, 2017

It gave me very good understanding on Spark Architecture, functioning along with hands on over Scala. Good examples used during lessons.

創建者 Oleg m

Apr 10, 2017

First weeks were overcomplicated with k-means and stuff not related to Spark itself.

In general - GREAT JOB !!! Thx for such kind courses

創建者 CLAUDIO A

Jun 10, 2019

Excellent explanations by Heather Miller. She really knows how to explain a topic, and also makes the lectures a lot of fun to listen !

創建者 Alvin H

Apr 04, 2017

Awesome Course . Detail and Depth of RDD vs Dataframe vs Dataset.

Latency vs Network/IO vs Shuffling.

Learnt a lot .

Thank you Heather.

創建者 Samuel L

Mar 23, 2017

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

創建者 Zhenduo D

Nov 19, 2017

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

創建者 Venkat

Jun 01, 2017

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

創建者 Lewis

Mar 29, 2017

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

創建者 Imre K

Mar 22, 2017

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

創建者 Prachi C

Sep 14, 2017

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

創建者 Vladislav B

Apr 08, 2017

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

創建者 Max Z

Mar 04, 2018

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

創建者 VincentWMChan

Apr 25, 2017

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

創建者 Thierry M

Feb 01, 2018

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

Thank you

創建者 Aleksander S

May 05, 2017

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

創建者 Ubaldo P

Apr 10, 2017

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

創建者 manuel B L

Jul 22, 2018

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