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Data Mining Workshop, AUA

Organization:

Disciplines:

Event Date:

February 23, 2015 - May 13, 2015

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The goal of the workshop is to introduce data mining as well as big data analytics concepts and solutions to Armenian professionals. No prior knowledge in the field is required to participate in the workshop. Prior experience with any programming language is welcomed.
The workshop will cover two basic data science applications – R and Python. The participants will learn basics of the data mining environment as well as implementation of different data mining techniques in R and Python.
The workshop is organized by Datamotus LLC and American University of Armenia’s College of Science and Engineering.
 
Audience
Young professionals who are currently holding positions related to data mining/data science, or are pursuing a career in data mining/data science.
Dates and venue
The workshop will take place at AUA. The Workshop will start on February 23 and will end on March 13.
Time: 19:00-21:00 (including coffee break).
 
How to apply
Please send your resume with a short cover letter on why you want to apply for the training workshop to datascience@aua.am. The number of participants is limited and all accepted participants will be notified by email. The workshop is free of charge for all participants.
 
About the trainers
 
Vazgen Hakobjanyan is an experienced engineer mostly specialized in building intelligent and innovative projects using Python (Django/Pyramid), Big Data (analysis, storage, verification, normalization, noSQL) and C++ (QT, Boost). He is the co-founder and VP of engineering at Teamable.
 
Vahe Hakobyan has a master degree in Mathematics from Yerevan State University and is currently finishing his master’s degree in Computer Science at the University of Bonn. Vahe has hands on experience in data mining and the machine learning domain.
 
Harutyun Khachatryan has a Ph.D. degree in Physics from Yerevan State University. He currently works at Monitis as Statistical Analyst. Harutyun is an expert in R, machine learning algorithms, time series modeling, etc.

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