Chevron Left
Back to Fundamentals of Scalable Data Science

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

4.3
stars
1,975 ratings
442 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

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

MA

Jun 19, 2021

Great Course but this would have been even a better course if more concepts and details were covered in it. Anyways, still a great course for beginners

Filter by:

276 - 300 of 444 Reviews for Fundamentals of Scalable Data Science

By Amy P

Aug 28, 2019

I learned a lot from this introduction and appreciated the amount of coding that the lecturer did during many of the videos. Would have liked more involved programming challenges at the end of each week.

By William Y

May 5, 2021

I wish the course offers more information on SparkContext and SparkConf, setMaster, wget, etc; In other word, how data is being passed around, and what component is responsible for what tasks, etc.

By Jan D

Mar 19, 2017

Good course with a good Instructor. It's a real basic course and good for beginners, though you need to have to dive into Python and Spark on your own to follow the course and the assignments. :)

By ADEJOKUN A

Jun 24, 2020

Great Introductory course for Big Data Analytics. The exercises and the assignments had the appropriate level of difficulty considering this was an advanced course. Thank you IBM and Coursera.

By Pranav N

Aug 27, 2019

Deserves 5 Star if the contents are updated such as removing redundant codes in Video lectures, upgrading Python and Spark to latest version etc. Overall a great place to start Scalable DS.

By Daniel D S P

Jun 7, 2020

La semana 2 es un ladrillo, se explican los temas de ingeniería para el procesamiento masivo de datos, pero la explicación no es muy pedagógica que digamos. Por lo demás estuvo muy bien.

By Bruno N

Sep 3, 2018

Very good course for a hands on overview introduction to the topic, and the associated tools (particularly Apache PiSpark).

Some issues with the auto grader encountered sometimes.

By Luca P

Sep 13, 2021

Very clear explanations. Tests not too difficult. Sometime too easy for an "advance" course. I liked it and I am looking forward to learn in the next sections of the program.

By Quazi M T M

Jul 5, 2020

There should be some links that are helpful towards this course, as it is an intermediate course, what courses are available in Coursera prior to this as a beginner lesson.

By Nora I

Nov 19, 2020

The difference between rdd, dataframe and sql.spark could be more clear in the practical sense. But all in all excellent course. A boost in my Data Scientist profile!

By Gouri K

Nov 12, 2019

Good overall,instructor was very good,but I feel more examples could be used especially when explaining multidimensional vector space and such basics of graphs

By Ivan J M

Nov 2, 2019

There are a lot of not updated sections, sometimes it confuses me because in some videos he talks about how we will use Node RED but then we don't use it.

By Hassna E

Apr 17, 2022

The course is good and informative, but needs more frequent updates as data science world evolves quickly and some of the guidlines provided are outdated

By Gerardo E G G

Jun 26, 2020

Great Course!

I would like to suggest to update the videos in order to reflect the operations in Python 3.x rather than 2.x but everything else was great!

By Muzamal A

May 10, 2020

Romeo is a great instructor and I love his lectures, however some of the quiz questions are very trivial and aren't explained on his video tutorials...

By Lucas M B

Dec 2, 2019

Seria ótimo se atualizassem o conteúdo do vídeo para reproduzir a versão atual do sistema e do Python, porém em teoria o conteúdo não deixou a desejar.

By Parth G

Oct 4, 2020

A bit on the easy side especially if you are proficient with SQL. But otherwise a decent into to spark and nice flavour of data analysis with python.

By Eric J

Feb 9, 2017

Really good course to provide an overview of working within IBM's cloud platform offerings. This course provides the basics of ApacheSpark as well.

By Thomas M

Sep 12, 2020

Pretty fun introduction, assignments were moslty copy-paste from instruction videos, so you don't get to 'learn' the right way in my opinion

By Kevin A H L

Jul 30, 2020

I taught the course would be more advanced. Terminology is confusing at first, but besides that, the assignments aren't so challenging.

By Umer A B

Mar 18, 2017

The Grader template in the beginning is very confusing when doing first assignment. The response from Instructor should be quick.

By Mortaja A

Jan 4, 2019

structure and instruction to setup of ibm clound and ibm watson needs improvement. overall good instructions and flow.

By Tamer M

Sep 24, 2019

Most of the video's subtitles need to be synced, it was hard to fully understand the Indian accent without subtitles.

By Jaydeep K R

Jun 23, 2020

It was a good overview of the large scale data but I would be more interesting if they had provided more Practice.

By Norman F

Jan 13, 2019

Some errors like lambdas are not working anymore with Python, some typos like in Assignment 4.1 and missing steps.