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Data Engineering and Machine Learning using Spark に戻る

IBM による Data Engineering and Machine Learning using Spark の受講者のレビューおよびフィードバック



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....

Data Engineering and Machine Learning using Spark: 1 - 16 / 16 レビュー

by Minh Q N


Great Course!!!

by Zahid H








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.

by Tatiana P


T​his course seemed very detached from the rest of the Data Engineering courses.

V​ery advanced info on a very advanced topic presented in a superficial and rushed manner.

F​inal 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?).

V​ery happy with the rest of the Data Engineering offering so far (I completed 11 out of 13).

V​ery disappointed with this one.

by David S S


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.

by Natale F


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.

by Sheraz M


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

by Pawel D


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.

by Dmitry K


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.

by Katarzyna G


I​t's really not for someone that is not familiar with ML.

by A. C


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.

by Cristina M M


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.

by Omar H


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

by James N


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

by Tatsuya T


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