Jan 13, 2021
The contents of this course are really practical and to the point. The examples and notebooks are also up to date and are very useful. i really recommend this course if you want to start with Spark.
Jul 21, 2021
Nice course. Learned the basics of a lot of different topics. Nice to do a large Data Science project in the last part. So you can apply all learned theory
By Mike D•
Nov 29, 2018
Currently, it is not advisable to take this course.
I have finished the excellent IBM Data Science Professional Certificate series on Coursera and wanted to improve my knowledge of scalable Data Science with this series. Unfortunately, the videos and advice are extensively outdated. Python 2 is used through this course and the instructions of how to set up Node-RED and Cloudant do not work. I have been trying to work myself around that but then again, I wouldn't need this course in the first place and it only leads to confusion. Also, instead of the Cloudant application UI, Kibana seems to be used now, which there is no introduction to. I have noticed that Romeo Kienzler, the course lead, is very active and dedicated in the discussion forums. I am afraid, I have to give this course 0 stars for content (for now) and 5 stars for course lead dedication.
By Vincenzo M•
Apr 13, 2018
I'm really disapointed with the "Fundamentals of Scalable Data Science" course from IBM. The videos are referring to an outdated software releases, with really different screenshots from the ones existing on IBM platform. The discussions refers to code samples different from the ones available for download (flow2.js) and to applications present on the IBM Bluemix platform, without explaining where to found or how to create them. All these discrepancies make really difficult to follow the course. LBNL, the speaker is one of the most boring ever encountered in a webinar, barely able to read in english language. Being a course organized by IBM for such an important topic as Watson, I expected a much higher level of quality. I hope that the IBM staff will be able to perform a deep review of this series of course.
By Marco D•
Aug 17, 2019
Assignments are too easy. The English pronunciation of the teacher is quite bad, sometimes I cannot understand and the subtitles are often wrong. I would be interested in an explanation that helps to setup spark in the local computer, instead of using IBM products.
By David-Leigh B•
Dec 18, 2018
IBM cloud environment is buggy and inconsistent with lectures.
When deploying services it sometimes fails and you are unable to remove them, rendering the account inoperable (as you have limits on free tier)
By Qian L•
Jul 6, 2019
If there was negative score, I wouldn't even hesitate to give that to this course. I understand coursera is actually trying to do good to the society and change the way education works in the past. But bad UI won't get you any further. About this course, I don't know why this Romeo guy is allowed to teach here. Extremely unusable tutorials, extremely bad organization of the the materials, extremely bad accent, extremely unusable IBM cloud service, extremely outdated tutorial for environment setup, and you name it. This guy can't even articulate himself. Please stop doing this to the user since now coursera started charging subscription fees. If there is even no quality control and this kinda of bad actor is allowed here, what is the difference between coursera and traditional college? Just go to the forum and discussion session of this course and look at the complaints over there. You don't even need to search for complaints since they are there on the surface. Shame on you.
By Mohanad A N•
Mar 10, 2019
Fuzzy, outdated, low quality course, according to the instructor; he is very busy "TRAVELLING" so that he cannot improve the quality of this course.
My problem is that the programming assignment of week 2 has no meaningful doc-string inside the functions that require coding, I do not know how I can deduce that my implementation is right before submitting to the auto grader.
By Dr S K•
Jan 18, 2019
The concepts taught in this course are very current. I would appreciate some more in depth practical/technical information about IoT, also about apache spark and the overall mechanism of action in the real world. The assignments I did not enjoy much, I found them rather uninteresting, although I appreciate the concept behind them and I can see why they were chosen. I really appreciate Romeo - I find him very interesting and I can tell he has a lot of experience and passion for what he has been doing. This particular course could provide a lot more information and education on scalable data science. Overall I found it ok, but I do appreciate that this is a new subject area and people have trouble collating material together. If Romeo would team up with people who have experience in teaching I think this course would have been outstanding. In any case I really appreciated his very hard work and I am very grateful.
By Ryan S•
Dec 23, 2018
Introduces Scalable Data Science and setting up the proper environments.
Areas for Improvement:
Not fully compatible with Python 3.5 (frustrating)
Could use some pre-requisite course material such as basic SQL and walk-through of pyspark SQL DataFrames.
Structure and presentation could be improved and reorganized.
Instructions need proof-reading.
By Joe Z•
Jan 4, 2019
I couldn't find most relevant IBM web pages only with the instructions; I spotted typos and bugs in exercises; the most disappointing part is the autograder which malfunctions OFTEN on unclear grounds, I had to submit my assignment multiple times and to test the autograder.
I could have spent less time learning more.
Aug 22, 2018
All of the course is telling you how to use their product
By Tyler G•
Apr 11, 2020
As an advanced course, the concepts here are pretty basic. I wish there was a bit more focus on Spark and not on explaining what "mean" is and how it differs from "median". I also found some of the videos to be verbose and repetitive, in particular when providing instructions like how to submit assignments. All that in consideration, the outline and content on Spark are great! It could just use some polishing to be a great course!
By Denys v K•
Dec 27, 2018
Very steep learning curve! Getting everything set up correctly is not very user friendly at this stage. Once everything is setup the material presented is very interesting. However, I prefer the tests (assignments) to actually test what was being taught and not something quite distinct (teach python; test spark SQL). The many comments in the forum attest to the issues for this course. Good start but needs improvement. Thank you.
By Gabor K•
Nov 10, 2018
I will un-enroll after 7 days in the course as the basic cloud environment setup did not work as written in the course handouts and videos. Which could be okay, however noone replied to my questions for 4 days on that, and the reply tips did not work either. Updating with screenshots, explanations no answer till today so what would have been a 15 minute job takes 7+ days here. At other coursera and competition platforms all my questions got answered in hours vs weeks as in this course.
By Octavio A T N•
Oct 26, 2019
This is a very good Data Science course. It helped me a lot to think in realistic application of Data Analysis. Impressive !!!
By Harshit S•
Sep 10, 2017
A perfect course to pace off with exploration towards sensor-data analytics using Apache Spark and python libraries.
By Matthew T•
Feb 8, 2019
Good course content, however, some of the material especially the IBM cloud environment setup sometimes confusing
By Arseniy T•
Jan 12, 2020
I want to put things into perspective: I recently completed a one-year data science course at Flatiron School which covered all aspects of data science: Python, SQL, data mining, statistics, probability, linear regression, classification, decision trees, deep neural networks and everything in between. You name it, I've studied it. If you want to learn data science - don't take this course. Few videos about central limit theorem + several graphs in matplotlib wouldn't leave you confident enough about how to actually do analysis. Also, assignments for this course were mostly about how to extract data with SQL, pretty easy if you know the basics. The entire course took me less than a day to complete and I'm still confused about how actually spark works under the hood. Some people complain about old videos and the thick accent of the teacher. For me it wasn't the problem, the code was running smoothly and I understood everything the teacher said. My suggestion would be to give a more detailed explanation of the cloud/parallel computing, how it's structured, how to set up servers, etc.
By Robert M•
Jun 12, 2019
Training Videos are pretty good for a beginner but to consider this an advanced course is incorrect. Intermediate at most. In addition the application of lessons to assignments were minimal as the answers needed for the assignment were not related to the content that was discussed, just very simple fill in the blank.
By Marcin S•
Apr 14, 2019
not enough additional materials
By Vuong B A•
Nov 30, 2018
Setup process is tedious
By Marius J•
Jul 6, 2018
This course does cover the needed fundamentals but needs a serious audit and edit of content, transcription, testing and grading for accuracy, conflicting instruction, and actual relevant instructional feedback.
By Jérémie B•
Jan 20, 2020
Nice introduction, not too difficult without being so easy that you learn nothing.
Sometimes outdated contents, but I always find solutions quickly to make everything work. In fact it is better to have realistic examples and to use up-to-date technologies, even it is of course harder to maintain. Therefore my remark is not a complaint. Actually Mr Kienzler does a good job to keep things working and the learners informed.
By Chirag S•
Jul 9, 2018
A Great Course to get an understanding regarding some basics concepts of data analytics through implementation
By Jose L R•
Sep 30, 2018
Great way to understand and learn open source tools and latest IBM data science offerings.
By Dmitry B•
Jan 11, 2019
This course is teaching how to work with data in a distributed environment. While getting used to IBM Cloud takes time, it is definitely a friendlier environment for data scientists and it removes the burden of setting up the infrastructure.