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

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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: 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...

Principales reseñas

EH

21 de jul. de 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

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

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401 - 425 de 442 revisiones para Fundamentals of Scalable Data Science

por Robert H

26 de mar. de 2020

Nice subjects notebooks could be more in-dept

por IVAN I

16 de abr. de 2021

too easy, programming part too stupid

por 吴芃

14 de mar. de 2020

seems it is not well prepared

por Feng L

26 de dic. de 2019

too simple

not advanced

por Anderson E A G

25 de dic. de 2020

it's not enough clear

por Arushi G

10 de may. de 2022

Extremely advanced.

por Leyre

7 de dic. de 2019

Low level

por Parker K

23 de sep. de 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.

por Lei Z

15 de jun. de 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.

por Ian H

10 de jul. de 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

por Aner W

11 de feb. de 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

por Stavros T

23 de sep. de 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.

por Tiago S

12 de oct. de 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.

por Erik A

31 de ago. de 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.

por Nancy P

16 de abr. de 2022

The material was outdated, and the programming assignments were more appropriate for novice learners, rather than professionals is the field.

por Markus W

22 de sep. de 2019

Romeo does a very good job of explaining things!

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

por Brendan A

27 de dic. de 2021

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

por Zhao Q D

9 de mar. de 2020

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

por Jason M

14 de may. de 2020

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

por Brian A P

1 de may. de 2020

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

por Paulo R C D S

4 de may. de 2020

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

por Yew C L

15 de oct. de 2020

Not really fundamental. Beginner will have difficulty to learn.

por Nima

4 de jun. de 2020

Big data materials are less discussed specially coding sections

por RAHUL C

21 de ago. de 2020

The course feels old now. Not much interactive.

por Hossein A

17 de jun. de 2020

Very good topics very not very good instructors