Data Science for IoT course: Strategic foundation for decision makers
The course is based on an open problem solving methodology for IoT analytics which we are developing within the course. The course starts in Sept 2016. To sign up or learn more email firstname.lastname@example.org.
Data Science for Internet of Things course
Strategic foundation for decision makers
To sign up or learn more email email@example.com
The course starts in Sep 2016
We have had a great response to the Data Science for Internet of Things course. The course takes a technological focus aiming enabling you to become a Data Scientist for the Internet of Things. I also had many requests for a Strategic version of the Data Science for Internet of Things Course for decision makers.
Today, we launch special edition of the course only for decision makers.
The course is based on an open problem solving methodology for IoT analytics which we are developing within the course.
Why do we need a methodology for Data Science for IoT?
IoT will create huge volumes of Data making the discovery of insights more critical. Often, the analytics process will need to be automated. By establishing a formal process for extracting knowledge from IoT applications by IoT vertical, we capture best practise.
This saves implementation time and cost. The methodology is more than Data Mining (i.e. application of algorithms) - but rather, it leans more to KDDM (Knowledge Discovery and Data Mining) principles. It is thus concerned with the entire end-to-end Knowledge extraction process for IoT analytics.
This includes developing scalable algorithms that can be used to analyze massive datasets, interpreting and visualizing results and modelling the engagement between humans and the machine. The main motivation for Knowledge Discovery models is to ensure that the end product will be useful to the user.
Thus, the methodology includes aspects of IoT analytics such as validity, novelty, usefulness, and understandability of the results(by IoT vertical). The methodology builds on a series of interdependent steps with milestones. The steps often include loops and iterations and cover all the processes end to end (including KPIs, Business case, project management). We explore Data Science for IoT analytics at multiple levels including Process level, Workflow level and Systems level.
The concept of a KDDM process model was discussed in 1990s by Anand, Brachman, Fayyad, Piatetsky-Shapiro, and others. In a nutshell, we build upon these ideas and apply them to IoT analytics. We also create code in Open source for this methodology.
As a decision maker, by joining the course, you have early and on-going access to both the methodology and the open source code.
Please contact us to sign up or to know more firstname.lastname@example.org
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