Data Science for Internet of Things course - Strategic foundation for decision makers
To sign up or learn more email firstname.lastname@example.org 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 email@example.com
Testimonials for our current course
Jean Jacques Bernand – Paris – France
“Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful.
As for myself, it allowed me to go into topics of interests that help me in reshaping my career.”
Johnny Johnson, AT&T – USA
“This DSIOT course is a great way to get up-to-speed. The tools and methodologies for managing devices, wrangling and fusing data, and being able to explain it are taking form fast; Ajit Jaokar is a good fit. For me, his patience and vision keep this busy corporate family man coming back.”
Yongkang Gao, General Electric, UK.
“I especially thank Ajit for his help on my personal project of the course — recommending proper tools and introducing mentors to me, which significantly reduced my pain in the beginning stage.”
karthik padmanabhan Manager – Global Data Insight and Analytics (GDIA) – Ford Motor Pvt Ltd.
“I am delighted to provide this testimonial to Ajit Jaokar who has extended outstanding support and guidance as my mentor during the entire program on Data science for IoT. Ajit is a world renowned professional in the niche area of applying the Data science principles in creating IoT apps. Talking about the program, it has a lot of breadth and depth covering some of the cutting edge topics in the industry such as Sensor Fusion, Deep Learning oriented towards the Internet of things domain. The topics such as Statistics, Machine Learning, IoT Platforms, Big Data and more speak about the complexity of the program. This is the first of its kind program in the world to provide Data Science training especially on the IoT domain and I feel fortunate to be part of the batch comprising of participants from different countries and skill sets. Overall this journey has transformed me into a mature and confident professional in this new space and I am grateful to Ajit and his team. My wish is to see this program accepted as a gold standard in the industry in the coming years”.
Peter Marriott – UK – www.catalystcomputing.co.uk
Attending the Data Science for IoT course has really helped me in demystifying the tools and practices behind machine learning and has allowed me to move from an awareness of machine learning to practical application.
Yair Meidan Israel – https://il.linkedin.com/in/yairmeidandatamining
“As a PhD student with an academic and practical experience in analytics, the DSIOT course is the perfect means by which I extend my expertise to the domain of IoT. It gradually elaborates on IoT concepts in general, and IoT analytics in particular. I recommend it to any person interested in entering that field. Thanks Ajit!”
Parinya Hiranpanthaporn, Data Architect and Advanced Analytics professional Bangkok
“Good content, Good instructor and Good networking. This course totally answers what I should know about Data Science for Internet of Things.”
Sibanjan Das – Bangalore
Ajit helped me to focus and set goals for my career that is extremely valuable. He stands by my side for every initiative I take and helps me to navigate me through every difficult situation I face. A true leader, a technology specialist, good friend and a great mentor. Cheers!!!
Manuel Betancurt – Mobile developer / Electronic Engineer. – Australia
I have had the opportunity to partake in the Data Science for the IoT course taught by Ajit Jaokar. He have crafted a collection of instructional videos, code samples, projects and social interaction with him and other students of this deep knowledge.
Ajit gives an awesome introduction and description of all the tools of the trade for a data scientist getting into the IoT. Even when I really come from a software engineering background, I have found the course totally accessible and useful. The support given by Ajit to make my IoT product a data science driven reality has been invaluable. Providing direction on how to achieve my data analysis goals and even helping me to publish the results of my investigation.
The knowledge demonstrated on this course in a mathematical and computer science level has been truly exciting and encouraging. This course was the key for me to connect the little data to the big data.
Barend Botha – London and South Africa – http://www.sevensymbols.co.uk
This is a great course for anyone wanting to move from a development background into Data Science with specific focus on IoT. The course is unique in that it allows you to learn the theory, skills and technologies required while working on solving a specific problem of your choice, one that plays to your past strengths and interests. From my experience care is taken to give participants one to one guidance in their projects, and there is also within the course the opportunity to network and share interesting content and ideas in this growing field. Highly recommended!
- Barend Botha
Jamie Weisbrod – San Diego - https://www.linkedin.com/in/jamie-weisbrod-3630053
Currently there is a plethora of online courses and degrees available in data science/big data. What attracted me to joining the futuretext class “Data Science for ioT” is Ajit Jaokar. My main concern in choosing a course was how to leverage skills that I already possessed as a computer engineer. Ajit took the time to discuss how I could personalize the course for my interests.
I am currently in the midst of the basic coursework but already I have been able to network with students all over the world who are working on interesting projects. Ajit inspires a lot of people at all ages as he is also teaching young people Data science using space exploration.
Robert Westwood – UK – Catalyst computing
“Ajit brings to the course years of experience in the industry and a great breadth of knowledge of the companies, people and research in the Data Science/IoT arena.”
Note that the schedule is personalized and flexible for the strategic course
i.e. we discuss and personalize your schedule at the start of the course
- Problem solving with Data Science: Is an overall process of solving Data Science problems(agnostic of a language) and covers aspects such as exploratory Data analysis)
- IoT analytics (includes analysis for each vertical within iot. This will be ongoing throughout the course including in the methodology)
- Foundations of R: The basics of one Programming language ( R ) and how to implement Data science algorithms in R
- Time Series – which forms the basis of most IoT data (code in R)
- Spark and NoSQL databases: Code in Scala and implementation in Cassandra
- Deep Learning
- Data Science for IoT Methodology
- Maths and Stats – (this will also be ongoing but will be a core module)