$383.90 – $548.90

Certified Fundamentals of Data Science 2017 [May 2017]

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Petrel College of Technology

Toronto, ON

Canada

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Description

Certified Fundamental of Data Science



Petrel College of Technology



Is this for you?

If you are working in any of the above industries either as an employee or as a founder of a startup company, Big Data Analytics can benefit your business through:

- Re-developing your product

- Performing Risk Analysis

- Creating new revenue streams

- Customizing your website in real-time

- Reducing Maintenance Costs

- Offering Tailored Healthcare

- Offering Enterprise-wide Insights




Course Description

This course will provide you with an introduction to the world of Big Data Analytics/Data Science with focus on major concepts in the field, the most prominent tools and examples from areas of application of Data Science.

Big Data Analytics application is growing from limited application in IT industry into a mature transferable skill that can benefit variety of other areas. The figure below (Which by the way is generated by a big data analytics application) explains the influence of this field in different industries.




Date and Time

Runs every Thursday
Start Date: May 4th, 2017 (Thursday)
End Date: June 22th, 2017 (Thursday)




Certification

Petrel College will issue a formal certificate after a course is completed and will include ASME logo. In order to receive a certificate student have to attend not less than 70% of the class sessions and pass final project with minimum 70% grade. A college will be responsible for informing students about certificate readiness upon successful completion of training. Students shall pick up certificates from college directly.




Registration:

Please register as soon as possible, as we have a limited capacity for this training. Minimum 12 students are required to run this training.




Learning objectives:

By successful completion of this course, you will gain an intermediate knowledge and skill sets for understanding and solving problems in Data Science. This course is also the pre-requisite for our advanced Big Data Analytics with focus on more advanced topics in the field.




Class Content:

- What is Data Science?

- What is Machine Learning?

- What is Big Data?

- What is Big Data Analytics?

- Making an old dog do old tricks - Machine Learning is Statistics

- Relevant Statistical Algorithms - Classifications (LDA, QDA, SVM), Regression, Deep Learning (Neural Networks), Random Forest

- Relevant Capabilities to carry out Big data analytics - Python, R, Scala

- Working with unstructured and Big data: Preprocessing, Feature Selection, Post Analysis

- Big Data Infrastructure - Hadoop, Spark

- Visualization for Big Data Analytics




Instructors:

Shiva Amiri, PhD

Shiva Amiri is the CEO of BioSymetrics Inc. where they are developing a unique real-time machine learning technology for the analysis of massive data in biomedicine. BioSymetrics specializes in providing optimized pipelines for complex data types and effective methods in the analytics of integrated data.

Prior to BioSymetrics she was the Chief Product Officer at Real Time Data Solutions Inc., she has led the Informatics and Analytics team at the Ontario Brain Institute, where they developed Brain-CODE, a large-scale neuro-informatics platform across the province of Ontario. She was previously the head of the British High Commission’s Science and Innovation team in Canada. Shiva completed her Ph.D. in Computational Biochemistry at the University of Oxford and her undergraduate degree in Computer Science and Human Biology at the University of Toronto. Shiva is involved with several organisations including Let’s Talk Science and Shabeh Jomeh International.

Babak Afshin-Pour, PhD

Babak Afshin-Pour is the VP Technology at BioSymetrics Inc. and has been putting in place a unique platform for the complex data types which BioSymetrics works with in the medical space. His interests are in the areas of big data analytics, advanced medical signal and image processing, evaluation and optimizing of fMRI analysis techniques, graph theoretical network analysis, and analysis of multi-site neuroimaging data. His proposed analysis frameworks have been published in high impact journals such as HBM, NeuroImage, and TMI. Babak received a B.S, degree in biomedical engineering as well as his M.S. and Ph.D. in electrical engineering from the University of Tehran with distinction. After the Ph.D., he was awarded a three-year post-doctoral fellowship at the Rotman Research Institute, University of Toronto.




For any further questions please contact:

Sina K. Maram at

email: sinakm@petrelcollege.ca or

tel.: +1 (289) 842-3360 ext. 1228




We certainly look forward to seeing you there!

ASME Ontario Section | "Discovery Your Passion" | http://asmeontario.eventbrite.com


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Petrel College of Technology

Toronto, ON

Canada

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