课程信息
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第 1 门课程(共 1 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

初级

完成时间大约为13 小时

建议:14 hours/week...

英语(English)

字幕:英语(English), 越南语

您将获得的技能

StatisticsData ScienceInternet Of Things (IOT)Apache Spark

第 1 门课程(共 1 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

初级

完成时间大约为13 小时

建议:14 hours/week...

英语(English)

字幕:英语(English), 越南语

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 4 小时

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

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2 个视频 (总计 3 分钟), 1 个阅读材料, 3 个测验
1 个阅读材料
Latest Video summary on environment setup10分钟
1 个练习
Challenges, terminology, methods and technology2分钟
2
完成时间为 5 小时

Tools that support BigData 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.

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8 个视频 (总计 52 分钟), 2 个阅读材料, 4 个测验
8 个视频
Functional programming basics6分钟
Introduction of Cloudant2分钟
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6分钟
Overview of how the test data has been generated (optional)8分钟
IBM Watson Studio (formerly Data Science Experience)3分钟
2 个阅读材料
Apache Parquet (optional)10分钟
Create the data on your own (optional)10分钟
3 个练习
Data storage solutions, and ApacheSpark12分钟
Programming language options and functional programming12分钟
ApacheSparkSQL, Cloudant, and the End to End Scenario12分钟
3
完成时间为 4 小时

Scaling Math for Statistics on Apache Spark

This learning module explores mathematical foundations supporting Exploratory Data Analysis (EDA) techniques.

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7 个视频 (总计 35 分钟), 1 个阅读材料, 4 个测验
7 个视频
Averages5分钟
Skewness3分钟
Kurtosis2分钟
Covariance, Covariance matrices, correlation13分钟
Multidimensional vector spaces5分钟
1 个阅读材料
Exercise 210分钟
3 个练习
Averages and standard deviation10分钟
Skewness and kurtosis10分钟
Covariance, correlation and multidimensional Vector Spaces16分钟
4
完成时间为 4 小时

Data Visualization of Big Data

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

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4 个视频 (总计 24 分钟), 2 个阅读材料, 2 个测验
2 个阅读材料
Exercise 3.110分钟
Exercise 3.210分钟
1 个练习
Visualization and dimension reduction10分钟
4.3
110 个审阅Chevron Right

62%

完成这些课程后已开始新的职业生涯

50%

通过此课程获得实实在在的工作福利

来自Fundamentals of Scalable Data Science的热门评论

创建者 HSSep 10th 2017

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

创建者 MTFeb 8th 2019

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

讲师

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

关于 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....

关于 Advanced Data Science with IBM 专项课程

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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