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Fundamentals of Scalable Data Science(으)로 돌아가기

Fundamentals of Scalable Data Science, IBM

4.3
491개의 평가
103개의 리뷰

About this Course

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 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...

최상위 리뷰

대학: 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.

대학: MT

Feb 08, 2019

Good course content, however, some of the material especially the IBM cloud environment setup sometimes confusing

필터링 기준:

105개의 리뷰

대학: Savan Rangegowda

May 23, 2019

Covers exactly what is required for data science using spark in case IoT data applications and the fundamentals required for the advanced data science topics . I am happy with the course and the topics that I have learned so far!

대학: mohamed aly

May 23, 2019

Assignment 2 need more clarficaiton

대학: ENRIQUE ALFONSO CARRERO ACOSTA

May 21, 2019

Excellent course

대학: Javier Adolfo Cruz Blanco

May 07, 2019

Great Job

대학: hamza jamshaid

May 01, 2019

Best course for People who have basic understanding about Python programming, Machine learning and statistics. The assignments are flexible and easy to complete. The course includes both theoratical and technical aspects of data science

대학: Paulo Thiago Palaia

Apr 27, 2019

awesome!

대학: Paulo Renato Rodrigues

Apr 26, 2019

Awesome course!

대학: Jamiil Touré ALI

Apr 26, 2019

Excellent. I highly recommend it, jump in and enjoy learning the foundations.

대학: Uzwal Gutta

Apr 26, 2019

Thank you

대학: Pawel Piela

Apr 23, 2019

Too easy to be called advanced. I look forward to seeing what's next.