About this Course
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Learner Career Outcomes

65%

started a new career after completing these courses

50%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Approx. 12 hours to complete

Suggested: 14 hours/week...

English

Subtitles: English, Vietnamese

Skills you will gain

StatisticsData ScienceInternet Of Things (IOT)Apache Spark

Learner Career Outcomes

65%

started a new career after completing these courses

50%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Approx. 12 hours to complete

Suggested: 14 hours/week...

English

Subtitles: English, Vietnamese

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Introduction the course and grading environment

2 videos (Total 3 min), 2 readings, 3 quizzes
2 videos
Overview of technology used within the course1m
2 readings
Assignment and Exercise Environment Setup10m
IMPORTANT: How to submit your programming assignments10m
1 practice exercise
Challenges, terminology, methods and technology2m
Week
2
5 hours to complete

Tools that support BigData solutions

8 videos (Total 51 min), 2 readings, 4 quizzes
8 videos
Parallel data processing strategies of Apache Spark7m
Programming language options on ApacheSpark10m
Functional programming basics6m
Introduction of Cloudant2m
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6m
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 and Cloudant12m
Week
3
4 hours to complete

Scaling Math for Statistics on Apache Spark

7 videos (Total 35 min), 1 reading, 4 quizzes
7 videos
Averages5m
Standard deviation3m
Skewness3m
Kurtosis2m
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
Week
4
4 hours to complete

Data Visualization of Big Data

4 videos (Total 24 min), 2 readings, 2 quizzes
4 videos
Plotting with ApacheSpark and python's matplotlib12m
Dimensionality reduction4m
PCA5m
2 readings
Exercise 3.110m
Exercise 3.210m
1 practice exercise
Visualization and dimension reduction10m
4.3
175 ReviewsChevron Right

Top reviews from Fundamentals of Scalable Data Science

By XWApr 11th 2017

Very useful courses to take if you are beginner of data science. The course was not detailed enough sometime. But you will surely get a global view of IOT data analysis after this courses.

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.

Instructor

Avatar

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.

More questions? Visit the Learner Help Center.