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  • Course

    Data Science
    Analytics and AI

    Oleksandr Romanko — Senior Research Analyst, IBM Canada

    INNOHUB, Kyiv

    Course instructor

    Oleksandr Romanko

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

    On September 7 — 8, 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 Research Analyst at Watson Financial Services, IBM Canada, a lecturer at the University of Toronto and UCU (Ukrainian Catholic University) and KSE.

    Oleksandr received a PhD 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.

    In 2 days you will:

    • learn how to obtain and clean data;
    • get the basic understanding of Data Science models;
    • learn how to build models using Python;
    • know how to critically analyze modeling results;
    • know 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:

    Dmytro Lavrinenko
    Director — Technology, GlobalLogic
    Ievgen Medvedskyi

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

    Date and time: September 7-8 from 10:00 to 18:00 (registration - at 9:30)

    Course Schedule

    September 7

    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
    • Logistic regression
    • Regression case studies in Python

    September 8

    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

    Early birds
    until August 15
    August 16 - August 31
    Last chance
    September 1 - September 7
    5% — from 2 tickets
    7% — from 3 tickets
    10% — from 5 tickets
    25% — for students

    Send a photo of your student card to info@data-science.com.ua to get a discount promo code.