Introduction to Data Science, Analytics and AI

Oleksandr Romanko

Senior Research Analyst, IBM Canada

September 9 - 10

from 10:00 am till 6:00 pm

National Dragomanov Pedagogical University

Pyrohova St., 9

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September 9-10, Oleksandr Romanko – Senior Research Analyst at IBM Canada, will again visit Kyiv to conduct an intensive course on Introduction to Data Science Analytics and Artificial Intelligence.

Oleksandr Romanko – Professor at the University of Toronto, Senior Fellow at IBM Canada. Oleksandr received his doctorate and master’s degrees in computer science from McMaster University (Canada), a master’s degree in economics from Charles University (Czech Republic) and a specialist degree from Sumy State University.
Everyone who has attended Oleksandr’s lectures at least once, has learned from his own experience that he is one of the best speakers in artificial intelligence and data science, moreover, our compatriot!

Why you should attend the course?
You will learn how:
– to prepare data;
– build models;
– optimize the received models;
– and most importantly, make a decisions according received data.

Why you should attend the course?
You will learn how:
– to prepare data;
– build models;
– optimize the received models;
– and most importantly, make a decisions according received data.

Panel discussion.
The panel discussion on “Data Science Industry in Ukraine: Prospects, Opportunities, Projects” will be a great conclusion to the course, with participants Dmitry Chaplinsky, Anton Trubnikov, Igor Staraprav, Maria Korolyuk, Vladimir Rybalko and Andriy Milinevsky.

Job Fair and Startup Presentations: Positions from leading IT companies and presentations from startups and young projects will be presented.

So come September 9th and 10th on the full Data Science days.
Organizer: Data Science UA, step by step we form a full-fledged Data Science community in Ukraine, organizing conferences, myths, courses, and employing world-leading companies.
Important: The course will cover practical Python examples, so it is advisable to have a laptop with you.

Course Schedule

September 9

Introduction to data science and analytics

  • Data science concepts
  • Application areas of quantitative modelling

Python programming, data science software

  • Introduction to Python
  • Comparison of Python, R and Matlab usage in data science

Basic statistics

  • Random variables, sampling
  • Distributions and statistical measures
  • Hypothesis testing
  • Statistics case studies in IPython

Overview of linear algebra

  • Linear algebra and matrix computations
  • Functions, derivatives, convexity

Modeling techniques, regression

  • Mathematical modelling process
  • Linear regression
  • Logistic regression
  • Regression case studies in IPython

Data visualization and visual analytics

  • Visual analyticsI
  • BM Watson Analytics
September 10

Simulation modeling

  • Random number generation
  • Monte Carlo simulations
  • Simulation case studies in IPython

Part I – Cognitive computing and artificial intelligence

  • Text analytics and Natural Language Processing (NLP)
  • Reinforcement learning
  • 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