Об этом курсе
4.4
Оценки: 145
Рецензии: 36
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...
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Предполагаемая нагрузка: 10 hours/week

Прибл. 14 ч. на завершение
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Субтитры: English, Vietnamese

Приобретаемые навыки

StatisticsData ScienceInternet Of Things (IOT)Apache Spark
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Только онлайн-курсы

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Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Beginner Level

Начальный уровень

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Предполагаемая нагрузка: 10 hours/week

Прибл. 14 ч. на завершение
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English

Субтитры: English, Vietnamese

Программа курса: что вы изучите

1

Раздел
Clock
4 ч. на завершение

Introduction to exploratory analysis

Analysis of data starts with a hypothesis and through exploration, those hypothesis are tested. Exploratory analysis in IoT considers large amounts of data, past or current, from multiple sources and summarizes its main characteristics. Data is strategically inspected, cleaned, and models are created with the purpose of gaining insight, predicting future data, and supporting decision making. This learning module introduces methods for turning raw IoT data into insight ...
Reading
2 видео (всего 3 мин.), 1 материал для самостоятельного изучения, 3 тестов
Video2 видео
Overview of technology used within the course1мин
Reading1 материал для самостоятельного изучения
*new* *new* *new* Latest Video summary on environment setup10мин
Quiz1 практическое упражнение
Challenges, terminology, methods and technology2мин

2

Раздел
Clock
6 ч. на завершение

Tools that support IoT solutions

Data analysis for IoT indicates that you have to build a solution for performing scalable analytics, on a large amount of data that arrives in great volumes and velocity. Such a solution needs to be supported by a number of tools. This module introduces common and popular tools, and highlights how they help data analyst produce viable end-to-end solutions. ...
Reading
8 видео (всего 52 мин.), 2 материалов для самостоятельного изучения, 5 тестов
Video8 видео
ApacheSpark and how it supports the data scientist7мин
Programming language options on ApacheSpark10мин
Functional programming basics6мин
Introduction of Cloudant2мин
ApacheSparkSQL6мин
Overview of end-to-end scenario8мин
IBM Watson Studio (formerly Data Science Experience)3мин
Reading2 материала для самостоятельного изучения
Download the “IoT Data storage cost calculator”10мин
Exercise 1 (Mandatory)10мин
Quiz3 практического упражнения
Data storage solutions, and ApacheSpark12мин
Programming language options and functional programming12мин
ApacheSparkSQL, Cloudant, and the End to End Scenario12мин

3

Раздел
Clock
4 ч. на завершение

Mathematical Foundations on Exploratory Data Analysis

This learning module explores mathematical foundations supporting Exploratory Data Analysis (EDA) techniques. ...
Reading
7 видео (всего 35 мин.), 1 материал для самостоятельного изучения, 4 тестов
Video7 видео
Averages5мин
Standard deviation3мин
Skewness3мин
Kurtosis2мин
Covariance, Covariance matrices, correlation13мин
Multidimensional vector spaces5мин
Reading1 материал для самостоятельного изучения
Exercise 210мин
Quiz3 практического упражнения
Averages and standard deviation10мин
Skewness and kurtosis10мин
Covariance, correlation and multidimensional Vector Spaces16мин

4

Раздел
Clock
4 ч. на завершение

Data Visualization

This learning module details a variety of methods for plotting IoT time series sensor data using different methods in order to gain insights of hidden patterns in your data...
Reading
4 видео (всего 24 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video4 видео
Plotting with ApacheSpark and python's matplotlib12мин
Dimensionality reduction4мин
PCA5мин
Reading2 материала для самостоятельного изучения
Exercise 3.110мин
Exercise 3.210мин
Quiz1 практическое упражнение
Visualization and dimension reduction10мин
4.4
Briefcase

83%

получил значимые преимущества в карьере благодаря этому курсу

Лучшие рецензии

автор: HSSep 10th 2017

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

автор: CSJul 9th 2018

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

Преподаватель

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

О IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

О специализации ''Advanced Data Science with IBM'

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....
Advanced Data Science with IBM

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