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Opiniones y comentarios de aprendices correspondientes a Fundamentals of Scalable Data Science por parte de Habilidades en redes 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: 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


13 de ene. de 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.


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

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226 - 250 de 450 revisiones para Fundamentals of Scalable Data Science

por Prabakaran C

8 de feb. de 2020

Great course

por Zeghraoui M

26 de mar. de 2019

I loved it !

por Vishwanath b

27 de may. de 2020

best course

por Farrukh

24 de abr. de 2020

Good Course

por Ranjith K M

30 de nov. de 2020

Very good


27 de sep. de 2020


por Anand M

23 de jun. de 2020

very nice


8 de jun. de 2020

very nice

por Lahcene O M

4 de abr. de 2020

Great job

por Charlie d T

10 de oct. de 2019

very good

por Javier C

7 de may. de 2019

Great Job

por Uzwal G

26 de abr. de 2019

Thank you

por Alessandro R M

5 de ene. de 2019


por Ahmad e D

12 de nov. de 2020


por Thiago P

27 de abr. de 2019


por ARUL N J

29 de sep. de 2020


por Jeff D

23 de ene. de 2021


por Dhaou B

1 de ene. de 2021


por raunak p

3 de ene. de 2022



5 de sep. de 2021


por Sivanta

25 de jul. de 2021


por Venkadesh

27 de nov. de 2020


por Yash V

8 de sep. de 2020


por Sakshi U

24 de jul. de 2020


por Rifat R

14 de jul. de 2020