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Opiniones y comentarios de aprendices correspondientes a Fundamentals of Scalable Data Science por parte de IBM

1,922 calificaciones
424 reseña

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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: 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 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 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... This course takes four weeks, 4-6h per week...

Principales reseñas

19 de jun. de 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

6 de ene. de 2020

A very nice introduction to Apache Spark and it's environment. As a bonus, it's also a very nice refresher to your basic statistics!!! Great course!

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301 - 325 de 427 revisiones para Fundamentals of Scalable Data Science

por ANAND G 1

14 de jun. de 2020

A good introduction to the steps to be taken to handle huge data sets. Surely would recommend to others.

por Braian G

24 de dic. de 2020

I liked the course but it has some errors in the code, related to Python2 -> Python3. Good material!

por Zeynep İ

19 de may. de 2020

The course is perfect for beginners but some videos are old. They should be revised. Thank you :)

por Jeet K P

5 de ago. de 2021

Maybe the course video should be changed properly. It will help student to understand properly

por Jeffrey G D

7 de ene. de 2020

Some of the courses have out of date instructions, or the methods recommended are deprecated.

por Prithvi M

15 de mar. de 2018

Good! Would have liked it even more if there was more data analysis involved using IOT data.

por Irfan H

14 de may. de 2020

The course lesson is easy enough to be learned, but I expect to learn more from this course

por Tim B H

2 de ene. de 2021

Nice course on PySpark and Data Science. I rate it 4 Stars as some details were missing.

por Cosme B M R

1 de may. de 2020

The topics are difficult but the course is very good and the teacher is well qualified.

por Mark B

17 de abr. de 2020

Hard to follow at times... was able to get a lot of assistance in discussion forums

por shubham b

5 de ene. de 2021

Nice introduction to the differences between "normal and scaleable" data science


16 de ago. de 2019

At first, I'm not sure what to do and it is hard for me to set up environment.

por deepshikhar

26 de sep. de 2018

The last quiz needs to be reviewed, otherwise awesome start to specialization

por Eugene N

12 de may. de 2020

I actually loved this course because it helped augment my spark basic skills

por Revalino J C S

4 de ene. de 2019

The environment setup is a little cumbersome due to constant changes in UI.

por Kaiqi Z

5 de abr. de 2020

The assignment is a little bit simple, but the knowledge is quite helpful!

por Suyash

23 de sep. de 2019

There are a lot of glitch with the assignments, hope it gets fixed soon

por Pablo R L

22 de may. de 2020

Too advanced material for introductory course. Excellent exercises.

por Matthijs K

6 de feb. de 2019

Sets you up well for working with Spark within the IBM Environment.

por Vinita S

20 de sep. de 2020

Harder assignments would been nice and maybe a little more reading

por Tushar J

14 de jul. de 2020

Good course. The pace was good and the material was enough for me.

por Zheng Y

10 de abr. de 2020

Assignments are too simple -- too similar to the course material.

por Harsh D

3 de feb. de 2019

Quite Good. But sometimes i had trouble following instructions.

por Raj N

13 de may. de 2017

Great introduction to Data Science, IoT and scalable computing!

por Abhay B K M

3 de jul. de 2020

It is hard to follow as it is very advanced and unevenly paced