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Fundamentals of Scalable Data Science, IBM

4.4
Оценки: 175
Рецензии: 42

Об этом курсе

The value of IoT can be found within the analysis of data gathered from the system under observation, where insights gained can have direct impact on business and operational transformation. Through analysis data correlation, patterns, trends, and other insight are discovered. Insight leads to better communication between stakeholders, or actionable insights, which can be used to raise alerts or send commands, back to IoT devices. With a focus on the topic of Exploratory Data Analysis, the course provides an in-depth look at mathematical foundations of basic statistical measures, and how they can be used in conjunction with advanced charting libraries to make use of the world’s best pattern recognition system – the human brain. Learn how to work with the data, and depict it in ways that support visual inspections, and derive to inferences about the data. Identify interesting characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. The goal is that you are able to implement end-to-end analytic workflows at scale, from data acquisition to actionable insights. Through a series of lectures and exercises students get the needed skills to perform such analysis on any data, although we clearly focus on IoT Sensor Event Data. 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 Automatically store data from IoT device(s) 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 any programming language (python preferred) • A good grasp of basic algebra and algebraic equations • (optional) “A developer's guide to the Internet of Things (IoT)” - a Coursera course • Basic SQL is a plus 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.) • IBM Watson IoT Platform (MQTT Message Broker as a Service, Device Management and Operational Rule Engine) • IBM Bluemix (Open Standard Platform Cloud) • Node-Red • Cloudant NoSQL (Apache CouchDB) • ApacheSpark • Languages: R, Scala and Python (focus on Python) This course takes four weeks, 4-6h per week...

Лучшие рецензии

автор: 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.

автор: CS

Jul 09, 2018

A Great Course to get an understanding regarding some basics concepts of data analytics through implementation

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Рецензии: 42

автор: Vuong Binh An

Nov 30, 2018

Setup process is tedious

автор: Mike Doepking

Nov 29, 2018

Currently, it is not advisable to take this course.

I have finished the excellent IBM Data Science Professional Certificate series on Coursera and wanted to improve my knowledge of scalable Data Science with this series. Unfortunately, the videos and advice are extensively outdated. Python 2 is used through this course and the instructions of how to set up Node-RED and Cloudant do not work. I have been trying to work myself around that but then again, I wouldn't need this course in the first place and it only leads to confusion. Also, instead of the Cloudant application UI, Kibana seems to be used now, which there is no introduction to. I have noticed that Romeo Kienzler, the course lead, is very active and dedicated in the discussion forums. I am afraid, I have to give this course 0 stars for content (for now) and 5 stars for course lead dedication.

автор: Satyam Kumar

Nov 20, 2018

This course gives you nice experience with Apache Spark. There is lot of update going on interface which creates few problem but discussion forum helps you out. Good for beginners in Data Science who have basic knowledge of python and SQL.

автор: Miguel Angel Barberan Galan

Nov 12, 2018

Eeverything related with the graded assesment in this course fails, it's outdated and the practicla exercises are not well explain, if you are looking for a hard struggle to get very simple things done, this is your course

автор: Gabor Koltai

Nov 10, 2018

I will un-enroll after 7 days in the course as the basic cloud environment setup did not work as written in the course handouts and videos. Which could be okay, however noone replied to my questions for 4 days on that, and the reply tips did not work either. Updating with screenshots, explanations no answer till today so what would have been a 15 minute job takes 7+ days here. At other coursera and competition platforms all my questions got answered in hours vs weeks as in this course.

автор: Matthew Tsoi

Nov 08, 2018

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

автор: Azeezur Rahman

Oct 17, 2018

Excellent Course with very interesting assignment and informative video course

автор: Sven

Oct 05, 2018

Very good data science specialization covering many interesting advanced technologies!

автор: Tinguaro Barreno

Oct 04, 2018

Great introduction to Data Science on IBM Cloud.

автор: Jose Luis Rodriguez

Sep 30, 2018

Great way to understand and learn open source tools and latest IBM data science offerings.