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The first course "Fundamentals of Scalable Data Science" on the specialization "Advanced Data Science with IBM" provides a good overview on theory, methods and tools you need for larg-scale data analysis. It requires basic to intermediate knowledge of Python and math. But it helps if you have experience beyond that to understand some ideas quicker and get the broader context.

Potential learners should know - as it is the normal thing with teaching/learning something - the teachers can't teach you something; you have to learn it. Means: spent some time beyond the time you need to consume the material from coursera. For example, I wrote five pages on the basics on statistics. It really helps! Again, the teachers organize a well well structured journey through the course material, but the just point to things that might be interesting.

On recommendation/request for improving quality of the provided videos: the are quite outdated. Date back to 2016/2017 and use Python 2 (which is not longer maintaned since 2020). Using the old python isn't too much of a problem, but it certainly does not help to learn effectively. The bigger problem is that the shown code is massively annotated with corrections and updates. These are all correct and helpful. But simply creating an updated video is way easier to consume. Just image a studend would submit his/her thesis as a draft plus a chain of 3 patches that have to applied on the thesis draft version. Not too handy, uhhm!?

My 4-star review is based on the many errors the course has. The material s great and the instructor is very knowledgable and seems to be on top of the class, however, I did not get a single reply of the notes I posted in the forum.

Besides the structure, the class requires revision due to inconsistencies and errors. It is surprising that topics have not been updated after many comments in the discussion forum.

Overall for me, it was a great experience and great learning experience

The content is great and applicable to industry. My only critique is that the coding assignments had been too simple. I would have preferred less hand-holding and more examples to work through to ensure the learner truly conceptualizes the process. With that said, it is easy enough for a learner to apply the process to other applications and understand how the pieces fit together for more real-world application.

Overall i had a good experience with the course. The course touches a number of components of IBM Cloud platform, that includes IBM Watson Studio (online software development platform) and Node-RED (a flow based programming language for defining data flows). I am happy that this course gave me my first practical experience with Apache Spark. It took me around 10 days to complete this course.

I've gained plenty of interesting information and valuable hands-on experience. I had to work for it a little more than I should have, however. The lecturer has a strong accent, speaks very fast and the subtitles are mostly useless as they are wrong more often than not. If you take this course, be prepared to take plenty of notes and watch the videos several times.

The course encompasses difficult and simple topics. For difficult ones, I have to follow "https://cognitiveclass.ai/learn/spark" as suggested by the instructor, and the course are really useful for understanding this course. Nevertheless, I found the programing exercise quite simple but good to recap what I have learned in the course. Thank you.

I would like to thank the authors of this course. It gives great introduction into Apache Spark and its applications in real problems. The only thing I would like to notice is that assignments could be a bit more complicated. Writing any code from scratch is much better for a future Data Scientist than just 'fill in' gaps in the existing code.

the material in this course was interesting and I learned a lot in a short time. I now understand how to deal with big data using Spark which is exactly what I wanted. One thing I wish was different was the code in the submission notebooks. I wish the functions we wrote had parameters for example instead of basically just running a script.

I liked the course because it introduced me to new topics but it did not really go further as expected from an advanced specialization. Maybe when I finished other courses, I find out that it well prepared me for the rest. However, please provide more sample datasets, similar questions, and generally more practice.

A very slow beginning although that is to be expected so that the course can draw in people from a wide range of backgrounds. Still a little tedious for someone with a little more of a background. Very well organized and it seems like a great introduction to Spark / Pyspark for those just beginning with this tool.

The course is quite good. However, its not meant for absolute beginners. One needs to have a decent understanding of Python and SQL in order to follow the course and complete the programming assignments. However, the extra effort put towards learning how to program is well worth it

Nice Course. Going straight forward to the manipulations using spark, and giving a great overview on how to deal with IoT data in the Cloudant NOSQL platform. Would hope to see a new course where we can use MLLIB with massive IoT data to showcase the power of parallel programming!

I wish this was more extensive /detailed course and assignments little bit more complex. The moderator timely response was greatly lacking. If the course instructor is asking the students to try out RDD while the auto-grader depends on SQL, it should have been clarified.

Nice course. Inform the basic concepts of statistics.

Some of the code is not consistent (E.g. the week 4 assignment I have to remove the parameter of getListForHistogramAndBoxPlot() and getListsForRunChart() in submit cell in order to successfully submit).

It's a good course, good practices of IBM Watson Studio, Apache Spark and Python programming skills. Although would like to see more specific content about data science like methods and linear algebra libraries and techniques. But it was satisfatory, btw.

The course was designed some years ago and now it needs some update considering the technology has changed a bit. Even after all of that, the teachers are really good and they provide high-quality education. Really glad I could be part of this course.

Good course. However, there are a lot of silly mistakes in the videos, which is solved by "cloud text boxes". Does not look professional, rather re-record the videos. The videos are sometimes also pixelated. This could be improved significantly

This course gives you nice experience with Apache Spark. There is lot of update going on interface which creates few problem but discussion forum helps you out. Good for beginners in Data Science who have basic knowledge of python and SQL.

It's an excellent course for anybody who wants to learn the basic of Spark, Watson Studio, and data analysis. It's also a good reminder for anybody well acquainted to the subject and want to know how to deal with it in Watson Studio

there were two errors i noticed if you could correct them - check the last assignment in the grading system it has parameters given which are not required and the last quiz there is a ques about PCA pls correct the options

I was just wondering, is the content a bit short? Are there any more details on practising writing functions and text rather than an hour videoing and quiz? I believe intense programming skills practise is more efficient

Nice for a basic introduction. I really got to know a lot about the basics of 'data' and spark applications. However, the exercises and assignments seemed a bit too simple. Also could do with a few more extra readings.

El curso es una introducción muy básica, lo más interesante son los ejercicios opcionales como el de node-red. Me esperaba que se aplicaran buenas prácticas en los ejercicios, pero como introducción está bien.

As an introductory course on data science and manipulation of large data sets, the course proved to be quite comprehensive and technically capable of leading the student to an understanding of all content.