89th PMI Bulgaria Chapter Meeting
18 May 2021, 18:30h
Exclusive Monthly Events Sponsor:
About the eventWHEN: 31 May 2021, 18:30h
HOW TO REGISTER: Please access and fill in the registration form online by clicking HERE
Free for chapter members/volunteers/sponsors
Fee for non-members: 20 BGN (or 10 EUR) for 1 webinar or 100 BGN (or 50 EUR) for 1 year webinar subscription
Name: PMI Bulgaria Chapter
DSK BANK BIC: STSABGSF
Please use reference: "Webinar Fee" or "Такса семинар"
For annual subscriptions: "Annual Subscription" or "годишен абонамент"
Or "Summit Ticket" or "Такса за конференция"
Please also communicate your EGN number as it is mandatory for invoicing purposes.
1 PM Summit Ticket + 1 year webinar subscription + 4 PDU data science training
105 BGN for non-members/70 BGN for members
Limited to first 50 orders
PMI members can easily become a chapter member by visiting the PMI store:
Agenda18:30 Welcoming (15 minutes)
18:45 Presentation: "Data Science 101: Introduction for Project Managers" (45 minutes)
19:30 Q&A Open Discussion (15 minutes)
19:45 Closing + PDU Registration + Feedback Survey
Speaker: Demir Tonchev is an applied data scientist with an academic background. In his current company Q-Factorial he employs 8 years of experience in data science (DS), machine learning (ML) and skepticism. He has applied DS algorithms to profile the customer base of Fortune 500 companies such as Ameren, Kellogg’s and U.S. Bank modelling customer behavior enriched with external data. He led machine learning projects in an early-stage startup building recommendation engines and complex lead scoring, duplicate and anomaly detection algorithms. Together with business partner Lyubomir Varbanov, they integrate, test and monitor ML algorithms inside the core software product. He also has experience in teaching statistics at university and every year teaches “introduction to data science” at a summer camp.
- Understand the structure and typical project team roles in data science projects
- Basic process of initiating and planning and monitoring a data science project
- Typical use cases and stakeholders
- Why data science projects may fail
- Technical skills required and agile workflow for project management
- Risk management aspects
- Project lifecycle phases including discovery, modelling and deployment
- Lessons learned from big projects