Nov 22, 2018
Everything in this course is new to me, but it provides me with many practice so I can gradually get familiar with all these new stuff. I find it a bit challenging, but overall it's quite good.
May 10, 2019
The course takes you from basic level , step level .But It is quite fast for beginners , you may need pause video in between and try to understand the concept.
교육 기관: Mayur T•
Mar 08, 2019
Tools provided in the course to submit the assignment doesn't work and there is no response from the team on how to resolve this issue. All the users in this course are facing the same issue.
교육 기관: Scott S•
Apr 05, 2019
I audited this course, because I was interested to complete the specialization. I finished the course and all of the assignments. After finishing this course, I will not continue the specialization.
For me, the biggest problem was the lectures regarding MapReduce. In my mind, there was a disconnect between the lecture materials and the assignments. The assignments also tended to be poorly worded; it was rarely clear what needed to be done to finish an assignment. I needed to use a lot of external resources here. I still do not understand map-side and reduce-side joins, and I do not feel comfortable writing a MapReduce job without a lot of time.
The lectures over Hadoop were ok, but strange. A lot of details are presented about how Hadoop works internally, and the speed at which the lectures move makes the discussion very dense and difficult to follow. However, the material is not used in the assignments or required further in the course, and the instructors are quite clear that this is the case. To me, this seems like a missed opportunity. There could be an entire week dedicated to the internals of Hadoop (or maybe even an entire course). After this course, I do feel comfortable getting around in an HDFS, and I feel I have a basic understanding of how it works.
The best part of the course was the lectures about Spark. The material was clearly presented, and the assignments were all relevant. The course gives a good introduction of Spark. I feel comfortable using basic SPARK operations to manipulate data.
If you wish to take this course, I recommend that you are knowledgeable about Linux Bash commands. There is a review section, but if you are seeing these ideas for the first time, I suspect you will suffer a lot.
The instructors provide Docker images so the assignments can be completed on a local computer. If you are not knowledgeable about Docker, I recommend learning through this course. It's not necessary but it's quite simple.
I do agree with others that the accents of some instructors can be difficult to understand. There are options for English subtitles which help a lot here.
Because I only audited the course, I could not submit any assignments for review. Thus I cannot comment on the automated grader. However many people in the forums complained about the grader.
I am interested to continue with Big Data topics, but this course was an inefficient way to learn. I fear the remaining courses in the specialization will be similar. I have completed several courses on Coursera, and this was by far the worst. I recommend the MapReduce section be improved and clarified.
교육 기관: Suman K S•
Sep 26, 2018
I am unable to understand what the tutors have been talking. I am scared after seeing them talking in the very first video.
교육 기관: Vinokurov A•
Sep 18, 2018
Difficult to understand, poorly cut and buggy
교육 기관: Ferran G P•
Mar 08, 2019
The tools provide to complete the course doesn't work.
교육 기관: Sock, H•
Mar 25, 2019
I appreciate that practical assignments exist and were definitely helpful for really understanding how to use MapReduce and Spark.
My complaints come from various issues that shouldn't be issues. A link to a Jupyter notebook file for the statistics part on week 6 wouldn't be downloaded when clicked on, and instead opened it on a new page (and the notebook file did not work unless you copied and pasted the page AFTER VIEWING THE PAGE's SOURCE).
The URLs for the New York taxi data are completely broken, the auto marker gives unhelpful error messages (for example, for week 6 td*idf when the issue was that I redirected my first map reduce job's stderr to a file, the error message from the marker was to "use only 0 or >1 reducers". I was using 0 reducers already, so this error message confused me for hours until I found a random post on Slack that said that stderr is needed to be output to terminal for the first mapreduce).
The course does teach quite a bit, however the lack of support from instructors, poor error messages on auto testers, and other issues that you will naturally encounter taking the course make it difficult for me to recommend this course to others.
교육 기관: Ehsan F•
Apr 02, 2019
This is the most awful course I have ever had in Coursera
교육 기관: Leonid M•
Dec 12, 2018
The course is advertised as a practical one. But the majority of time is spent on outdated technologies like Map/Reduce. It would be more productive to go deeper into Spark. Assignments are not difficult but it takes a lot of time and attempts to figure out what exactly the authors wanted. The worst part is the grader and how it organized. Nevertheless you can learn a few things even if you are working in this industry.
교육 기관: Evgeny F•
Jul 18, 2018
Too few learning materials. Unreliable grader. Also unclear yarn mapReduce assignments. For example in the final assignment after submition one test said "... this task must be done in less than 3 jobs" Ok, why don't write it in the assignment text? And so on..
It wasn't a good learning expirience
교육 기관: Mikhail M•
Oct 17, 2018
The course is not elaborated. Actually it is a FAIL. Bad accent, inappropriate jokes (Hello, Alexey!), not so good topics (for introductory course), paranoid grading system.
Only lectures from Ivan Puzyrevskiy are decent. Thank you, Ivan!
교육 기관: Maryna D•
Oct 19, 2018
Accent is horrible, it is hard to listen, a lot of mistakes in the words pronunciation. But the idea of course is good.
교육 기관: Ejaj A•
Sep 03, 2018
Not very happy with the course. Submissions had lot of issues.I could not figure out and left the course in the middle(even the demo assignment was not working).The instructors were great but somehow I thought they were not very involved.Too much information (stated fast) out of which you may not be caring a lot. Lot of slides/presentation but somewhere they were too fast or were not able to connect the exact points.
교육 기관: pete_ch•
Jan 25, 2018
Non existent instructor support
Buggy inconsistent development environment.
A perfect example of how not to implement the principle of least surprise
교육 기관: Pieter M•
Jan 28, 2018
The topics and the content of the course were good but that is the only thing that was good. The presentation is done in English with a heavy Russian accent with wrongly translated subtitles. The assignments are described minimalistically, passing the automatic checking of the assignments cost more time than actually getting the right answer for the assignment and often the external assignment environment is down or not functioning correctly. When this happens the staff support on the discussion board is really slow (think in days or weeks) or non-existing.
교육 기관: Bryce C F•
Oct 18, 2017
The lectures are good, but the assignments seem to rely on a Jupyter setup that is different than the one usually provided through Coursera. So far, I have spent more time dealing with these troubleshooting issues than actually focusing on the content.
교육 기관: navneet k•
Nov 28, 2018
Awesome content...great learning ...:)
교육 기관: Viacheslav I•
Oct 24, 2017
There were no such other course online as far as I know. You can find many courses on machine learning or other topics of data analysis, but on the side of engineering you were along so far. With this course you will learn basics of Hadoop, HDFS, MapReduce and Spark. You will work on many practical assignments that will let you understand those technologies. If you are willing to move into direction of data engineering being software developer, or if you want to learn some engineering topics being a data analyst, this course is for you!
교육 기관: STEVEN V D•
Jun 28, 2018
Absolutely essential for everyone who wants a proper introduction to HDFS, MapReduce and Spark. Brought to you by a great team of geniuses of their time ;)
교육 기관: Benjamin R•
Aug 09, 2018
Quite challenging course for me (I was starting almost from scratch). Lots of information, lots of practice, additional practices for those eager to do more, Slack channels to communicate, you name it. Kudos to the Yandex team, and to the mentors community!
교육 기관: Sanjay M•
Feb 26, 2018
Excellent class to learn the basics of MapReduce, Hadoop, and PySpark. The lectures are very informative. They move at a strong pace, making this class more like a graduate level class in lecture style.
The programming problems are well designed to learn these languages.
The only downside is that code submission can be a bit of an adventure. It's not always clear exactly what the auto-grader is looking for. Aside from this issue, I would recommend this class for people interested in the material.
교육 기관: Raja H•
Jul 21, 2018
This course has been well structured and I liked the assignments a lot. One area of improvement is to make the grading environment stable. This will save a lot of time!
교육 기관: Sergejs P•
May 13, 2018
The course fills an important gap between software engineering and data engineering. The course content is good and the presentation is quite good as well. There are occasional issues with submitting assignments. It seems like the grading infrastructure has not been perfected just yet. This is definitely not a course for beginners. There's just too many things that can go wrong that are hard to understand unless you're already a somewhat experienced programmer and comfortable with the CLI.
교육 기관: Kassymzhomart K•
Mar 22, 2019
Difficult for newbies, but good for intermediate
교육 기관: Alexander P•
Oct 29, 2017
Home Assignments and their checker could be more transparent :)
교육 기관: Bingnan L•
Nov 15, 2018
There are a lot of unclear things about the homeworks. So even when you can run your homework successfully in the docker image, you still can't pass the online tests. Besides, the error msgs shown by online test system (not logs) are also unclear. It can't tell you the real reason of failure.