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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
2,521 個評分
517 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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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.

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!

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426 - Big Data Analysis with Scala and Spark 的 450 個評論(共 500 個)

創建者 Gongqi L

2017年4月9日

Very good course, but it needs more details and examples.

創建者 kaushik

2017年4月9日

Good course ! But does need more programming assignments

創建者 Mohammad T

2019年8月24日

such a beautiful course design for a bigData devlopers

創建者 Kota M

2018年4月5日

It is a good course, but the lecturer speaks too fast.

創建者 Anuj A

2020年10月22日

Needs more detailing for datasets and dataframe apis

創建者 Wolfgang G

2017年8月30日

Very well-lead introductory, a bit lengthy at times.

創建者 Manuel W

2017年4月18日

Would be better to have more and shorter exercises.

創建者 Ruslan A

2017年8月23日

lectures don't correlate to practical assigment :(

創建者 David G

2017年8月25日

Great course, but can be great idea have the ppts

創建者 Yuan R

2018年1月20日

Great course that is very practical for the job.

創建者 Guillermo G H

2017年6月30日

Great approach to learn about Spark in practice

創建者 Michaël M P

2019年2月5日

Talk about how to set Scala version in Eclipse

創建者 林鼎棋

2017年5月29日

Great! But I want to know more about dataset!

創建者 VeeraVenkataSatyanarayana M

2017年6月4日

Basics are covered in an effective way.

創建者 Pavel O

2017年8月12日

Good final course for Scala learners.

創建者 Lucas F

2017年5月15日

Great lectures and great content!

創建者 Роман В

2018年6月24日

I would like to learn some more.

創建者 Hoon P

2017年4月18日

Learned Spark APIs, internals.

創建者 Alberto P d P

2017年5月12日

Very good and concise course.

創建者 Javier L B

2021年12月7日

Good course.

創建者 Stéphane L

2017年10月13日

Very useful

創建者 Srinivasu N

2020年5月15日

good

創建者 Devaraja K R

2018年11月14日

Good

創建者 Jim N

2017年4月12日

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.