Please, see agenda of the webinar below: 

15:00 — 15:05 Moscow time
Introduction: why did we start ”Demystifying Data Science” series and who is it for.

Dilyara Saetova, DataArt, Project manager     
Bio: Project manager at DataArt and Andministrative coordinator in Women in Tech Russia, Program manager for WIT ”Demystifying Data Science” series. Over 6 years of experience in management of software development cycle, online marketing and business application management.
Core competencies include: building fast and scalable services and applications, team leadership, cross-functional team management, problem resolution, stakeholder communications, project management, solution design, strategic planning.

15:05 - 16:00 Moscow time   
Introduction to Machine Learning.
Description: The lecture aims at providing a foundational understanding of machine learning (ML) and its potential benefits. We will cover basic terminology, types of problems (e.g. classification, clustering) and machine learning and deep learning algorithms (e.g. linear and logistic regression, ensemble models, neural networks). The emphasis will be on intuition and examples rather than theory and math, and we will also let the audience interact with some state-of-the-art ML models and play the game of “can you distinguish between human and computer” (aka the Turing test).
Dr. Shaul Dar, Dell, Technologist, data scientist, manager        
Bio: Dr. Shaul Dar is a technologist, data scientist, manager and lecturer with extensive experience in research and software development, in areas related to big data, machine learning and AI. Shaul has worked in leading research institutions (UW-Madison, AT&T Bell Labs), start-ups (Mercado, Savantis) and large corporations (McKesson, AGT, PayPal / eBay, Check Point, Intuit, Dell).

16:00 — 17:00 Moscow time
ML-based manufacturing process control. 
Description: The speech is about the role of ML in Industry 4.0: how ML models help to prevent major equipment failures and decrease human factor in production downtime. We will discuss the algorithm of working with data in manufacturing and metalworking, what challenges there can be, and which industrial needs lead us to use ML as a primary tool in developing solutions for clients.

Lydia Khashina, Zyfra, Product owner. 
Bio: Lydia Khashina is a product owner at Zyfra. Her responsibility is to develop AI-based products for manufacturing and metalworking clients in times of the fourth industrial revolution. She manages the team of data scientists to help them apply their skills and knowledge to real life needs.

17:00 — 17:55 Moscow time
ML powered Crime Prediction
Description: What if we could predict when and where next crimes will be committed? Crimes in Chicago is a publicly published dataset which reflect the reported incidents of crime that occurred in Chicago since 2001. Using this data, we would like not only be able to explore specific crimes to find interesting trends, but also predict how many crimes will be taking place next week, and even next month.

Or Herman Saffar, Dell, Senior data scientist     
Bio: Or Herman-Saffar is a senior data scientist at Dell’s Data Science Factory. She holds an MSc in biomedical engineering, where her research focused on breast cancer detection using exhaled breath analysis and machine learning techniques. At Dell, Or technically leads data science engagements with internal business units for implementation and design of data science projects. Or is also responsible for the team’s branding and education materials for sharing knowledge and expertise in relevant communities.

17:55 - 18:00 Moscow time
Сlosing speech
A short summary on the webinar

Elina Valeeva, CEO & Founder of Meditivity / Women in Tech Russia ambassador
Bio: Founder and CEO of Meditivity, the AI assistant that helps people and companies be more productive and fulfilled. Meditivity’s unique set of tools shifts focus from planning to achieving and from stress to mindfulness.
Selected entrepreneur for participation in the Global Entrepreneurship Summit 2019 in the Netherlands.