Data Science, Analytics and AI Course 2019

Oleksandr Romanko

Senior Research Analyst, IBM Canada

April 20-21

From 10:00 to 18:00 (registration - at 9:30)

InnoHub

6Z, Vatslava Havela Blvd.Kyiv, Ukraine

Facebook

Event

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.

In 2 days you will:

  • 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.

Course language: ukrainian, slides and Python examples in English

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

[:en]nika[:]
Veronica Tamayo Flores

Head of consulting, Data Science UA

[:en]savsunenko[:]

Alexander Savsunenko

Head of AI Lab, Skylum Software, PhD

[:en]Vasyl Sergienko[:]

Vasyl Sergienko

Marketing manager, Skylum Software

Course Schedule

Day 1

1. Introduction to data science and analytics

  • Data science concepts
  • Application areas

2. 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

3. 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
Day 2

1. 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

2. 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

3. Visual analytics and storytelling based on analytics

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