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  • Course

    Introduction to Data Science, Analytics and AI

    Oleksandr Romanko

    Senior Research Analyst, IBM Canada

    Adjunct Professor, University of Toronto
    Ukrainian Catholic University

    Course instructor

    Oleksandr Romanko

    Senior Research Analyst, IBM Canada

    Adjunct Professor, University of Toronto
    Ukrainian Catholic University

    On April 14-15 Oleksandr Romanko, Senior Research Analyst, Watson Financial Services, IBM Canada will come to Kyiv again to hold a two-day Data Science, Analytics and AI course!


    The objective of the course is to learn analytical models and overview quantitative algorithms for solving business problems. Data science or analytics is the process of deriving insights from data in order to make optimal decisions. It allows hundreds of companies and governments to save lives, increase profits and minimize resource usage. Considerable attention in the course is devoted to applications of computational and modeling algorithms to finance, risk management, marketing, health care, smart city projects, crime prevention, predictive maintenance, web and social media analytics, personal analytics, etc. We will show how various data science and analytics techniques such as basic statistics, regressions, uncertainty modeling, simulation and optimization modeling, data mining and machine learning, text analytics, artificial intelligence and visualizations can be implemented and applied using Python. Practical aspects of computational models and case studies in Python (Jupyter notebooks) are emphasized.


    Course instructor: Oleksandr Romanko – Adjunct Professor at Toronto University and Ukrainian Catholic University, Senior Research Analyst at IBM Canada. Oleksandr received a Ph.D. and Master’s degree in Computer Science at McMaster University (Canada), a Master’s degree in Economics at Charles University (Czech Republic) and a Bachelor degree at Sumy State University.


    Why should I attend the course?


    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


    How does it differ from last year course?


    – a deeper immersion in the machine learning and artificial intelligence algorithms
    – more detailed analysis of code examples on Python to get an understanding of the code. It will be useful even for those who can not code
    – an expanded business side of analytics use to improve the functioning of the business


    Venue: Pyrohova St., 9, Kyiv (National Dragomanov Pedagogical University)


    Time: registration starts at 9 am on April 14″


    Course language: Ukrainian, slides and Python examples in English

    Course Schedule

    14 April

    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

    15 April

    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

    Part I – Cognitive computing and artificial intelligence

    • Text analytics and Natural Language Processing (NLP)
    • Reinforcement learning
    • Neural networks and brief introduction to deep learning

    Part II – Cognitive computing and artificial intelligence

    • 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

    Сourse schedule

    Buy 3 tickets Get 10%
    Buy 5 tickets Get 15%
    20% discount for students
    590 ₴
    before 5 March
    790 ₴
    March 6-31
    950 ₴
    April 1-14