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Learner Reviews & Feedback for Fundamentals of Scalable Data Science by IBM

4.3
stars
1,970 ratings
441 reviews

About the Course

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies. This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science. Please have a look at the full specialization curriculum: https://www.coursera.org/specializations/advanced-data-science-ibm If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging. After completing this course, you will be able to: • Describe how basic statistical measures, are used to reveal patterns within the data • Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. • Identify useful techniques for working with big data such as dimension reduction and feature selection methods • Use advanced tools and charting libraries to: o improve efficiency of analysis of big-data with partitioning and parallel analysis o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling) For successful completion of the course, the following prerequisites are recommended: • Basic programming skills in python • Basic math • Basic SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed) In order to complete this course, the following technologies will be used: (These technologies are introduced in the course as necessary so no previous knowledge is required.) • Jupyter notebooks (brought to you by IBM Watson Studio for free) • ApacheSpark (brought to you by IBM Watson Studio for free) • Python We've been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we've been reported that this really helps. Of course, you can give this course a try first and then in case you need, take the following courses / materials. It's free... https://cognitiveclass.ai/learn/spark https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68 This course takes four weeks, 4-6h per week...

Top reviews

ZS

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.

EH

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

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401 - 425 of 443 Reviews for Fundamentals of Scalable Data Science

By Robert H

Mar 26, 2020

Nice subjects notebooks could be more in-dept

By IVAN I

Apr 16, 2021

too easy, programming part too stupid

By 吴芃

Mar 14, 2020

seems it is not well prepared

By Feng L

Dec 26, 2019

too simple

not advanced

By Anderson E A G

Dec 25, 2020

it's not enough clear

By Arushi G

May 10, 2022

Extremely advanced.

By Leyre

Dec 7, 2019

Low level

By Parker K

Sep 23, 2021

The concepts behind the use of Spark are not explained very well. Otherwise, the content is very simple. I thought the value of this course would be in learning Spark by applying it to concepts that I already know well, but the course didn't do a very good job of thoroughly teaching Spark concepts. I don't really think this course is appropriate as part of an "Advanced" data science series. The material is extremely basic. I didn't get much out of this course. I hope the others in the series are better.

By Lei Z

Jun 15, 2021

N​ot very good. There is no logic in the lectures and the exercises. I have been a reputable pure mathematician for many years. Taught several linear algebra courses. But when I hear the "linear algebra" taught by Romeo Kienzler I am deeply confused, completely don't know what he wanted to say. The exercises on "linear algebra" are equally bad, confusing, with no logic.

By Ian H

Jul 10, 2020

A disappointing amount of the material presented is out of date (e.g. what environments to use, Watson vs 'Data science experience' )--while fine for some cases, it too frequently borders on intrusive at best to desperately opaque at worst. Clarity of presentations could also be greatly helped. Perhaps focusing more on the why? and so what? aspects would be helpful

By Aner W

Feb 11, 2021

The explanations are not clear enough- the rational for using spark is not clear enough (missing context explanation, etc)

missing written lecture notes (in bullets- summariezed)

It would be helpful if the lecture text would be integrated in the video itself and not below the video presentation- harder to follow

By Stavros T

Sep 23, 2021

Interesting material, but i don"t feel i"ve learned how to actually use spark on my own. Most of the code was already in place during the assignments and i just had to add some minor easy parts. Kept some nice noted though and will revisit this in the future.

By Tiago S

Oct 12, 2021

Not recommended course for an intermediate level of Statistics and ML. I had the impression it will be focus on pyspark and apache, but it turned out to be on ML topics.

By Erik A

Aug 31, 2020

The videos are fuzzy, extremely outdated, and don't match up with the actual projects. I couldn't pay much attention to them. Projects were good though.

By Nancy P

Apr 16, 2022

The material was outdated, and the programming assignments were more appropriate for novice learners, rather than professionals is the field.

By Markus W

Sep 22, 2019

Romeo does a very good job of explaining things!

However, the programming assignments are too easy to learn anything from.

By Brendan A

Dec 27, 2021

Meh. Not the best one. Videos are hard to watch and not very clean instructions. I'm going to try a different course.

By Zhao Q D

Mar 9, 2020

Both exercises and programming tests are too easy. It should be real programming instead of filling in the blanks.

By Jason M

May 14, 2020

very simple. homework not challenging enough - just repeating the demos almost exactly.

By Brian A P

May 1, 2020

The course content is not well structured and at times very confusing.

By Paulo R C D S

May 4, 2020

Very basic and Spark exercises are too easy to learn useful skills

By Yew C L

Oct 15, 2020

Not really fundamental. Beginner will have difficulty to learn.

By Nima

Jun 4, 2020

Big data materials are less discussed specially coding sections

By RAHUL C

Aug 21, 2020

The course feels old now. Not much interactive.

By Hossein A

Jun 17, 2020

Very good topics very not very good instructors