IBM  Data Science  Experience

Experience IoT with Coursera

I’m very happy and proud to announce that IBM is the first non-academic supplier to offer a data science course on Coursera. We've worked very hard to make this course a great learning experience for anyone interested in data science and IoT, and IBM Data Science Experience is central to the course.

It has been great to see all of our hard work payoff. In addition to launching our first Coursera course, Exploring and Visualizing IoT Data, on January 9, 2017, we also kicked off a data science degree program.

Since my team and I are working for the IBM Watson IoT division, and IoT is one of the most prominent disruptors in that space, the choice was obvious that we create a course on the topic of exploring and visualizing IoT data. The course is applicable to any time series problem including stock exchange data or social media streams, and even non-time series data.

Those interested in learning more on the hardware and cloud data integration part of this topic might want to have a look at the course A developer's guide to the Internet of Things (IoT).

I really would have loved to immediately start with artificial intelligence methods for IoT time-series forecasting and anomaly detection, but this would have been the wrong starting point of the journey. To help guide you through that journey, we decided to create a data science degree (in Coursera terms, a specialization) and the courses mentioned above will set the stage and make you familiar with technologies like message brokers, NoSQL databases, Object Storage, Apache SparkSQL, Python and Matplotlib.

Using that technology stack, we introduce statistical measures to gain insight on IoT data and learn how to visualize it.

Having laid the foundation with the 1st course, we are currently creating a 2nd course on IoT time-series analysis using Apache Spark 2.0 Structured Streaming on the highly optimized tungsten and catalyst engine. We will teach you how to detect anomalies and predict future events using advance statistical methods.

Then finally, the last course will talk about artificial intelligence methods using deep learning frameworks – auto encoders and recurrent LSTM networks for anomaly detection and forecasting. So stay tuned! And take the course to start your journey :)

Course links:

Romeo Kienzler

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