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

4.3
stars
1,942 ratings
431 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.

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

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426 - 433 of 433 Reviews for Fundamentals of Scalable Data Science

By GARG M

Oct 27, 2021

Well I have taken several courses on coursera and their explaination was pretty good but in this course explaination was not up to the mark , very disappointed.

By Ahmet Y

Mar 17, 2020

After the IBM Data Science Proffesional Specialization this course was very inadequate. Lambda calculus is not explained well.

By Mike H

Jan 1, 2020

Not well structured in my opinion. Difficulty of content not well balanced. Outdated presentations and content...

By Goce Z

May 19, 2020

easier to just make it labs and some reading as all the videos are just watching the instructor type code

By Kaustav S

May 14, 2020

Not a course relevant to data science, what needed in the market perspective

By W L

Sep 20, 2020

course material is inconsistent and not well prepared.

By Sergei B

Aug 26, 2020

To easy to be advanced ML course.

By jack g

Apr 29, 2021

content needs updating.