par Tatiana P•
10 janv. 2022
This course seemed very detached from the rest of the Data Engineering courses.
Very advanced info on a very advanced topic presented in a superficial and rushed manner.
Final project with many technical issues in the necessary Jupyter Labs, which I don't see reseaonably debugged by the person taking the course (also, why should they?).
Very happy with the rest of the Data Engineering offering so far (I completed 11 out of 13).
Very disappointed with this one.
par James N•
8 nov. 2021
Assignments remain offline for more than a week. No refunds offered, no staff responses
par Rorisang S•
2 juil. 2022
The instructions in the lab could be clearer.
par Minh Q N•
22 sept. 2021
par Zahid H•
13 mars 2022
par dumebi j•
19 nov. 2021
par CHAUVEL S•
25 janv. 2022
Very interesting session. Topic was well covered. I would have, perhaps put, a specific exercise on the implementation, the parameter setting and the execution of a pipeline with Elyra. For example: reading csv file+putting in parquet format+condensing parquet file.
par David S S•
15 nov. 2021
I can't rate higher this course due to the problems with the final project... I hope all the errors could be fixed for future students because the course is excellent and the exercise is great to practice all the knowledge acquire but it has a lot of bugs.
par Natale F•
25 nov. 2021
The Data Engineer part is too fast. The final assessment focuses on the implementation of Machine Learning algorithms with Spark, there is no Data Engineer code production required.
par Sheraz M•
18 sept. 2021
The final assignmnet instructions are not very clear and also there are some coding msiatkes that lead you to unexpected results.
par Pawel D•
14 janv. 2022
This course is misunderstanding. The lab environment is not working since months. Running lab notebooks locally require a lot of hacking to make it work. The course is assuming knowledge re/ Machine Learning and data wrangling, The spark is explained superficially and not much use. Free online tutorials are better and clearer.
par Dmitry K•
14 sept. 2021
Peer project has tasks which has never been though or referenced. Part of the labs are failng with lack of resources and git has some obsolete code.
par Katarzyna G•
28 févr. 2022
It's really not for someone that is not familiar with ML.
par A. C•
6 avr. 2022
Pretty horrible experience. While working on the assignment I got banned for "improper conduct" (no further explanation given) by the IBM Skills Network (the provider of the hands-on environment). I opened a support ticket there (31st of March) which remains unanswered until today (6th of April). In essence I paid for 1 month access to the course, and as it stands, i could not work on the content for more than a quarter of the time.
Interestingly, I had a very similar experience (hands-on labs not working for days at a time) when i did an IBM (Data Analytics) course a few years back at edX. So given my current experience, I would strongly discourage doing any of the IBM courses that involve the IBM Skills Network.
par Cristina M M•
9 nov. 2021
The theory and practice of this course are not at the same level. Yo need to learn some statistics and ML theorical concepts previously.
Labs cannot be do it only with the explanations of the videos.... The final project shouldn't be the place where you see a decision tree.
Also, there is a some commands that work in a bad way in the labs. I think the course need a complete revision, keeping in mind that a lot of learners do the course as part of a certification and had no experience with ML and a only a little with spark.
par Omar H•
5 déc. 2021
It offers very little information, The labs are not well explained, this course doesn't add any value for the specialization.
par Tatsuya T•
27 mars 2022
This course is waste of time. I should've taken another course.