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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

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2,413 個評分
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498 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
2017年6月7日

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
2019年11月28日

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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476 - Big Data Analysis with Scala and Spark 的 481 個評論（共 481 個）

創建者 Dan O

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2017年3月25日

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. ...

創建者 Марко И

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2017年4月10日

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.

創建者 Deleted A

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2020年7月9日

good lectures another case of lectures not matching the assignment in terms of what you should pick up. inadequate resources and not enough depth on actual transformations and methods.

創建者 Sergio R P

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2019年9月30日

The assignments are very confusing and unexplained. They do not take long to reply to the forum.

創建者 Krzysztof S

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2020年3月3日

Assignments don't work properly

創建者 crow f

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2018年5月19日

The course is too basic.

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