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  • DATA SCIENCE ANALYTICS AND AI COURSE 2019

    OLEKSANDR ROMANKO

    SENIOR RESEARCH ANALYST, IBM CANADA

    20.04-21.04

    INNOHUB, 10 KYIV


    Course instructor

    Oleksandr Romanko

    Senior Research Analyst, IBM Canada
    Adjunct Professor, University of Toronto
    Ukrainian Catholic University

    In each industry, field and specialization there are people who are undisputed professionals. Mentors, whose advice they listen to, seek to collaborate, take on experience.

    On April 20 and 21, Data Analytics and AI 2019 course will take place. During the course Oleksandr Romanko will talk about the basics of data analysis, modeling, and IBM AI experience.

    Oleksandr Romanko is a Senior Researcher at IBM Canada, lecturer at the University of Toronto and UCU (Ukrainian Catholic University) and KSE.

    Oleksandr received a Ph.D. and Master's Degree in Computer Science at McMaster University (Canada), a Master's degree in economics at Karlovo University (Czech Republic) and a specialist diploma from Sumy State University.

    Just in 15 hours of intensive course you will learn:

    • how to obtain and clean data
    • how to build models
    • how to crytically analyze modeling results
    • how to make optimal decisions based on modeling results

    Who will be interested in:

    • junior-middle developers
    • business and financial analysts
    • junior data scientists
    • managers who would like to transform their companies based on data
    • students who seek to study real cases instead of dry theory

    Special guests:

    Veronica Tamayo Flores
    Head of consulting, Data Science UA
    Alexander Savsunenko
    Head of AI Lab, Skylum Software, PhD
    Vasyl Sergienko
    Marketing manager, Skylum Software

    Venue: InnoHub, 6Z, Vatslava Havela Blvd.Kyiv, Ukraine

    Date and time: April 20-21 from 10:00 to 18:00 (registration - at 9:30)


    Course Schedule

    April 20

    Introduction to data science and analytics

    • Data science concepts
    • Application areas

    Getting data into Python

    • Working with CSV and JSON format/files
    • Web-scraping in Python
    • Using APIs in Python (Twitter API, New York Times API, etc.)
    • Using cloud AI services from Python

    Machine Learning I – linear and logistic regressions

    • Modeling process and machine learning
    • Optimization for regression modeling, data science and AI
    • Linear regression

    Machine Learning I – linear and logistic regressions

    • Logistic regression
    • Regression case studies in Python

    April 21

    Machine Learning II – advanced classification and clustering

    • Classification (decision trees, SVM, kNN)
    • Clustering (K-means, Fuzzy C-means, Hierarchical Clustering, DBSCAN)
    • Association rules
    • Ensemble methods (random forests, Xgboost)
    • Machine learning case studies in Python

    Cognitive computing and artificial intelligence

    • Text analytics and Natural Language Processing (NLP)
    • Neural networks and brief introduction to deep learning
    • Spatio-temporal analytics
    • Cognitive computing case studies in Python

    Visual analytics and storytelling based on analytics

    • Visual analytics and visualizations
    • Validating analytics
    • Storytelling based on analytics
    • Decision-making based on analytics

    Course language: ukrainian, slides and Python examples in English


    Tickets
    BUY 3 TICKETS — GET 10% OFF
    BUY 5 TICKETS — GET 15% OFF
    25% discount for students
    690
    until March 31
    790
    April 1 - April 11
    890
    April 12 - April 20