Machine Learning practical course

Dmitry Guzenko and Veronica Tamayo Flores

Dmitry Guzenko, Data Analyst
Veronica Tamayo Flores, Head of Consulting, Data Science UA

November 23-24

from 10:00 am till 7:00 pm

Creative Quarter, Gulliver

Sportyvna Square 1A


How to interpret data correctly, build models, test theories, prototype concepts? It is important for data managers and analysts to keep up with all the updates, but time and new libraries are often not enough to develop code. That’s why we created the Machine Learning Workshop  with Dmitry Guzenko and Veronica Tamayo Flores. In this course you will learn how to use the tools to build machine learning models almost without coding.

For whom this course is intended:

  • analysts seeking for further development in machine learning;
  • managers who want to understand the work of algorithms without writing code;
  • business representatives looking to apply a data-driven approaches to their own business.

Necessary software

  • it is necessary to have your own laptop

The course is designed to minimize the need for programming, but some of the minimal features will need to be done with Python and R.

  • Azure ML account
  • MS Power BI Desktop (English) 
  • DAX Studio
  • Microsoft R Open and RStudio (latest version)
  • Python (latest version)
  • Anaconda for Windows (latest version)

Dmitry Guzenko

Dmitry has more than 20 years of experience in business process automation & ERP systems implementation, 10 years of experience in system analysis and business models architect, 3 years expertise in Data Management approach to improve company efficiency.
Experience in Solution Architecting, Business Analysis best practice implementation to improve valuable changes for stakeholders.

Veronica Tamayo Flores, Head of Consulting, Data Science UA

In 2018, Nika graduated from IE Business School (Spain) majoring in Business Analytics and Big Data. She worked in marketing and digital analytics for retail. Veronica manages project of data science and business intelligence implementation at companies. The main expertise is business analysis, business translation (combination of business and technical skills), conducting analytical projects and business development.


Dmitry Guzenko

Data Analyst

Veronica Tamayo Flores

Head of consulting, Data Science UA

Course program

November 23

Block 1: Introduction into Data Science and Machine Learning

  • What is Data Science and Machine Learning?
  • Common tasks that can be solved using machine learning
  • What big data is and what value it brings to organizations
  • Data-Driven approach to business development
  • Existing standards and practices of Data Science projects
  • The most striking and significant examples of Data Science projects applying
  • Overview of successful Big Data and Machine Learning projects in Ukraine and abroad

Block 2: Business Analysis in Machine Learning Projects

  • Detailed overview of the current Data Science process and its step
  • Overview and understanding of the basic Data Science terms
  • Fundamentals of machine learning and types of tasks to be solved
  • Business analysis stage in machine learning projects
  • Features of business analysis for Data Science projects
  • New complexity of DS projects and ways to overcome them
  • Workshop: business analysis and decision recommendation using Data Science and Machine Learning technologies
  • Data exploration stage
  • Difficulties associated with the Data Understanding stage
  • Data structures and artifacts to jump to the project start
  • Workshop: Using the Data Exploration phase for a DSML project

Block 3: Initial preparation and data visualization

  • Basics of datasets, features, and target variables understanding
  • Import and merge data
  • Works with poor quality data
  • Data processing tools
  • Workshop: Import and pre-process data

Block 4. Data visualization for intelligence analysis

  • Approaches and techniques for data visualization in intelligence analysis
  • Basic visualization types for data understanding
  • Common errors during visualization
  • Best practices and guidance in visualization design
  • Power BI for data visualization
  • Advanced visualization methods
  • Practice: Data visualization with Power BI and R
November 24

Block 5: Supervised Machine Learning Problem Solving

  • Theory and principles of machine learning
  • Types of tasks to be solved
  • Problem Solving with Supervised Machine Learning
  • Types of metrics to evaluate the solution
  • Statistical metrics for task evaluation
  • Workshop: Develop a model of cost forecasting
  • Workshop: Develop a model for predicting employee dismissal

Block 6: Unsupervised Machine Learning Problem Solving

  • Overview of problems and types of tasks to be solved in Unsupervised Machine Learning
  • Algorithms, approaches and complexities of the clustering problems solution
  • Building recommendation systems
  • Solving fraud and atypical problems
  • Workshop 4: Solution to the clustering problem

Block 7: Implementation of machine learning models

  • Implementation of machine learning models for future usage
  • Examples of complete project architectures
  • Workshop 5: Publish a model as a work product
  • Resources with ready-made datasets
  • Resources with ready-made solutions
  • Resources for self-development


Sold out


25% — for students. In order to get a discount promo code, send a photo of a student card to the

5% — from 2 tickets
7% — from 3 tickets
10% — from 5 tickets

Career partner

[:uk]Work UA[:]