About this Course
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Beginner Level

Approx. 13 hours to complete

Suggested: 14 hours/week...


Subtitles: English, Vietnamese

Skills you will gain

StatisticsData ScienceInternet Of Things (IOT)Apache Spark

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Approx. 13 hours to complete

Suggested: 14 hours/week...


Subtitles: English, Vietnamese

Syllabus - What you will learn from this course

4 hours to complete

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

2 videos (Total 3 min), 1 reading, 3 quizzes
2 videos
Overview of technology used within the course1m
1 reading
Latest Video summary on environment setup10m
1 practice exercise
Challenges, terminology, methods and technology2m
5 hours to complete

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.

8 videos (Total 52 min), 2 readings, 4 quizzes
8 videos
ApacheSpark and how it supports the data scientist7m
Programming language options on ApacheSpark10m
Functional programming basics6m
Introduction of Cloudant2m
Overview of how the test data has been generated (optional)8m
IBM Watson Studio (formerly Data Science Experience)3m
2 readings
Apache Parquet (optional)10m
Create the data on your own (optional)10m
3 practice exercises
Data storage solutions, and ApacheSpark12m
Programming language options and functional programming12m
ApacheSparkSQL, Cloudant, and the End to End Scenario12m
4 hours to complete

Scaling Math for Statistics on Apache Spark

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

7 videos (Total 35 min), 1 reading, 4 quizzes
7 videos
Standard deviation3m
Covariance, Covariance matrices, correlation13m
Multidimensional vector spaces5m
1 reading
Exercise 210m
3 practice exercises
Averages and standard deviation10m
Skewness and kurtosis10m
Covariance, correlation and multidimensional Vector Spaces16m
4 hours to complete

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

4 videos (Total 24 min), 2 readings, 2 quizzes
4 videos
Plotting with ApacheSpark and python's matplotlib12m
Dimensionality reduction4m
2 readings
Exercise 3.110m
Exercise 3.210m
1 practice exercise
Visualization and dimension reduction10m
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Top Reviews

By HSSep 10th 2017

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

By MTFeb 8th 2019

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



Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

About 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....

About the Advanced Data Science with IBM Specialization

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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you have started a course that depends on the IBM Bluemix, and your trial has expired, you can continue taking the course on the same environment by providing your credit card information. To avoid being charged, close any application instances you are not using and pay attention to the usage of your environment details.

    Alternative, you can export any projects you are working on. Then, you can register for a new trial using a different email account, not used on IBM Bluemix before. Finally, import the projects to the new account.

    When exporting your projects, for Node-RED use the process used when submitting assignments (export flow form the old project, then import to the new project via clipboard). For Node.js you can redeploy the code to Bluemix using your new account credentials.

    If you have customized your GIT repository, or registered devices, migrating to a new environment will require you to redo those steps to reflect in the new environment.

  • If you already have an IBM Bluemix account, but your trial period has expired, you can always create a new account with a different email address.

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