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Opiniones y comentarios de aprendices correspondientes a Data Engineering and Machine Learning using Spark por parte de Habilidades en redes de IBM

79 calificaciones

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Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others. In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering. The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case. NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one....

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1 - 23 de 23 revisiones para Data Engineering and Machine Learning using Spark

por Santiago Z A

3 de oct. de 2022

This is the worst online course that I've taken in my entire life. Labs are not debugged.

Python API for Spark has a great potential for learning but in this course it is covered in a superficial way and the explanation is not clear.

Labs are not debugged, I would put a minus five star.

I don't understand how a company like IBM, which has provided so many technological advances to the world, is delivering such a bad course.

And the most impressive thing for me is that the course was made by more than 10 persons according to Team & Acknowledgements.

If you really want to learn about Spark, I'll recommend you the course Spark and Python for Big Data (Jose Portilla).


por Tatiana P

10 de ene. de 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.

por Dmitry K

14 de sep. de 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.

por James N

8 de nov. de 2021

Assignments remain offline for more than a week. No refunds offered, no staff responses

por David S S

15 de nov. de 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.

por A. C

6 de abr. de 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.

por Cristina M M

9 de nov. de 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.

por Natale F

25 de nov. de 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.

por Sheraz M

18 de sep. de 2021

The final assignmnet instructions are not very clear and also there are some coding msiatkes that lead you to unexpected results.

por Pawel D

14 de ene. de 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.

por Arunava B

15 de sep. de 2022

IThis one particularly was very hard to follow. Content is hard to follow and understand. I wish the course designer put more effort into the subject matter so that sudents undeerstand the basics rather than chasing to finish the course somehow.

por Omar H

5 de dic. de 2021

It offers very little information, The labs are not well explained, this course doesn't add any value for the specialization.

por Yevgenia C

28 de nov. de 2022

This course is terrible and plagued with technical issues. The third lab is next to impossible to complete for this reason.

por Tatsuya T

27 de mar. de 2022

This course is waste of time. I should've taken another course.

por Katarzyna G

28 de feb. de 2022

It's really not for someone that is not familiar with ML.

por Colin S

10 de nov. de 2022

Unclear and confusing labs and assignment.


25 de ene. de 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.

por Sean B

25 de feb. de 2023

The course content does not prepare you well for the final project, it can be completed but with a lot of extra outside research, I don't think this is fair as the rest of the courses in the IBM Data Engineering certificate don't really require this

por Rorisang S

2 de jul. de 2022

Fantastic delivery.

The instructions in the lab could be clearer.

por Raihan N H

30 de ene. de 2023

easy to understand the course

por Minh Q N

22 de sep. de 2021

Great Course!!!

por Zahid H

13 de mar. de 2022


por dumebi j

19 de nov. de 2021