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

Оценки: 2,508
Рецензии: 516

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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51–75 из 499 отзывов о курсе Big Data Analysis with Scala and Spark

автор: Bora K

1 сент. 2018 г.

Teacher has excellent teaching skills. She takes enough time to go through to concepts. Before introducing a new technique she teaches why the new technique is needed and explains how it solves existing problems. Absolutely great course.

автор: 許致軒

16 апр. 2017 г.

Very Very Interesting and helpful!

The slides' layout is very clear and step by step for each important topic.

The motivation of why we need dataframe and dataset and what's their difference is explained with a logical and reasonable way!

автор: Raduś N

23 мая 2017 г.

Awesome teacher - very engaging. This is probably first time when I am watching lectures with pleasure. Also you can easily feel that course is fresh and specially made for this unlike previous ones from Scala speciality.

автор: Heyang W

18 авг. 2017 г.

A walk through from the oldest RDD to newest Dataset API of spark, together with brief introduction on how spark work. Home work set up several scenario to use the different kinds of spark API to do basic data analysis.

автор: Mike D

6 апр. 2017 г.

Great introduction course to Spark with excellent materials and hand-on programming assignments. Thanks for taking effort to get this class online. I have enjoyed it very much.

Kudos to Professor Miller, we love you :-)

автор: Jayaprakash

9 апр. 2017 г.

Great Material and lecture videos. It covered important concepts we need to know about Spark and helped me to learn further. Assignments were on real datasets and helped me to explore different APIs if Spark and Scala.

автор: Heitor M G

20 февр. 2018 г.

I really enjoyed the course, specially the first 3 weeks. Week 4 videos could be split in more videos, it felt as if they were not reviewed as much as the other weeks videos. Anyway, the whole course is really good.

автор: Peter T

7 апр. 2017 г.

Enjoyed the course and learnt lots. Stimulating material. Though I feel that the week 4 material could be made more concise. On the whole would recommend to other interested and it will definitely help in my career.

автор: Eric L

9 апр. 2017 г.

Demystified the subject for me. I felt like the lecturer covered a considerable amount of material in relatively short time. The assignments helped to cement the knowledge acquired over the duration of the course.

автор: Angel A

7 мая 2017 г.

love this topic!!!! New and frontier technology in NLP. More and more concentrations and analysis on this big data research.

I wish more courses about Parallel clustering using Spark available to the many.

автор: PatrickEifler

21 февр. 2020 г.

I really enjoyed the course. It is very comprehensive and exhaustive. It covers a lot of details on Spark and all its APIs and looks under the hood. I recommend to anyone who really wants to learn Spark.

автор: Alexandr M

24 июня 2017 г.

This course gives basics of distributed computing by practical examples, several alternative approaches to the same problem are considered, this gives more insight and flexibility. Very interesting one!

автор: Darcio L

4 апр. 2017 г.

Excellent and broad view of Spark and its design foundations. Besides the formal knowledge of Spark, it could change my perspective about how Scala and Akka had a crucial role in the Spark architecture.

автор: Bill P

29 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.

автор: Jose R

15 нояб. 2020 г.

I’m really happy with this 4-weeks experience. I’ve learnt a lot of Spark’s internals and different ways of resolving problems taking in account performance and optimisation details.

Great course!

автор: Natalia G

28 мар. 2017 г.

goot as introduction about spark and big data.

Small notice: it is incorrect to compare performance hadoop and spark. As I understand, spark was expected to be compacred with MapReduce.

автор: William D

2 дек. 2017 г.

Excellent course! It's clear the instructor put a ton of thought and hard work into this. I learned a lot that I wouldn't have learned without taking this class. Thank you, Heather!

автор: pratik

10 авг. 2017 г.

The course starts from the basic concepts and moves towards the complex concepts. The most important thing is that minute details are taken into consideration and explained properly.

автор: Enrique M B

23 мая 2017 г.

I had previous experience with Scala and Spark and I really enjoyed the course.

I learned some very interesting things, specially y de last 2 weeks about partitioning and Spark SQL.

автор: Nikola M

3 апр. 2017 г.

Good overview of the subject, covering all important aspects. Assignments were well prepared, with a couple of unclear points that were quickly discovered and explained on the forums.

автор: Santiago A

23 сент. 2019 г.

Awesome course and awesome teacher! Nevertheless, to grasp the most of this course, you should do the previous 3 courses of the "Functional Programming in Scala" specialization.

автор: Yuriy P

27 окт. 2017 г.

Dear Heather,

your course on big data with scala is the very first online course I participate in.

I enjoy the way you explain the material and receive a real aesthetic pleasure.

автор: Ignacio G S

5 мая 2021 г.

The contents are well explained and the material is of good quality. The exercises are of correct level of complexity, in order to learn what is viewed in the theory lectures.

автор: Luca D S

1 дек. 2017 г.

It surely opens your mind, even on unrelated topics, I found myself able to apply some of the distributed computing logics even to imperative sequential programming. Good job.

автор: Mani P

9 апр. 2017 г.

Excellent material. Very good flow. Heather has an amazing way of walking through the flow and simplifying the concepts. Great assignments -- takes a bit longer than 3 hours.