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

4.3
estrelas
922 classificações
191 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: 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 This course takes four weeks, 4-6h per week...

Melhores avaliações

AA

Jan 07, 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!

HS

Sep 10, 2017

A perfect course to pace off with exploration towards sensor-data analytics using Apache Spark and python libraries.\n\nKudos man.

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101 — 125 de {totalReviews} Avaliações para o Fundamentals of Scalable Data Science

por Rahul M

Jul 06, 2017

Very good learning exposure.

por Lingjun K

Aug 07, 2019

good learning experience!

por Danyang

Oct 27, 2017

Thanks this is exciting!

por Gautham N

Nov 24, 2019

It's a very good course

por Yue B

Jun 28, 2019

comprehensive and basic

por PV R K

Oct 01, 2019

excellent experience

por Reetu

Jun 12, 2018

very well explained!

por mahmut k

Jul 04, 2018

Useful information!

por YOUSSEF M

Aug 13, 2019

amazing content !!

por George K

Dec 24, 2018

Very good teacher.

por Bikash R

Nov 21, 2019

PCA part was fun!

por ujjwal k g

Jul 16, 2019

good one to start

por Madan K

Oct 31, 2019

Excellent course

por Saman S

Oct 26, 2019

that's wonderful

por ENRIQUE A C A

May 21, 2019

Excellent course

por alamelumuralidaran

Feb 18, 2019

Wonderful course

por Paulo R R

Apr 26, 2019

Awesome course!

por Bruno M A A

Dec 20, 2018

very practical.

por Felipe D P B

Aug 09, 2019

Great course!

por Alejandro S M

Mar 25, 2019

Just awesome!

por Zeghraoui M

Mar 26, 2019

I loved it !

por Charles-Antoine d T

Oct 10, 2019

very good

por Javier A C B

May 07, 2019

Great Job

por Uzwal G

Apr 26, 2019

Thank you

por Alessandro R M

Jan 05, 2019

excellent