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Voltar para Fundamentals of Scalable Data Science

Comentários e feedback de alunos de Fundamentals of Scalable Data Science da instituição IBM

1,837 classificações
399 avaliações

Sobre o curso

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...

Melhores avaliações

13 de Jan 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.

6 de Jan 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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201 — 225 de 398 Avaliações para o Fundamentals of Scalable Data Science

por Mr.D.Premkumar A P

29 de Set de 2020

super tq to all

por Paulo R R

26 de Abr de 2019

Awesome course!

por Bruno M A A

20 de Dez de 2018

very practical.

por Kevin R L

4 de Abr de 2020

awesome course

por Manjula

21 de Jul de 2020

good platform

por alexander n

16 de Mai de 2020

Great course!

por Felipe P B

9 de Ago de 2019

Great course!

por Alejandro S M

25 de Mar de 2019

Just awesome!

por Aymen R

30 de Jun de 2020

good course!

por Prabakaran C

8 de Fev de 2020

Great course

por Zeghraoui M

26 de Mar de 2019

I loved it !

por Vishwanath b

27 de Mai de 2020

best course

por Farrukh N A

24 de Abr de 2020

Good Course

por Ranjith K M

30 de Nov de 2020

Very good

por bhargav d

27 de Set 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 Charles-Antoine d T

10 de Out de 2019

very good

por Javier C

7 de Mai de 2019

Great Job

por Uzwal G

26 de Abr de 2019

Thank you

por Alessandro R M

5 de Jan 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 Set de 2020