How to manage Data Science project properly:
from team handpicking to delivery management

Transformation begins when you understand the challenge you are facing

Webinar participants will get EXCLUSIVE BONUSES,
which will be announced on the webinar
Tips and tricks you'll get on this FREE webinar
the indispensable project team from the pool
Estimate pitfalls
Most common risks while estimating a Data Science project
Delivery is not the end
Keeping the project evergreen
60% of data projects will fail to go beyond piloting and experimentation and will be abandoned according to Gartner
Why you should watch this webinar

Companies are investing now more than ever in Big Data and related technologies. According to a SNS Research, the value of global investments in data science technologies will surpass the $57 billion mark by the end of 2017. Both business intelligence (BI) and analytics software market continuously grow and, by the end of this year, will generate global revenue value of $18.3 billion, as reported by Gartner.

It is important to wisely manage every stage of project development process and safely ensure success by applying the right techniques and leading a team properly. In this webinar, we covered main questions: what people do you need for successful launching and delivery, where to get those people, how to weed out underperformers, how to estimate and then deliver commitments, how to evolve the project after launch.

About the Hosts
Julia Liubevych
CBDO at Unicsoft
5 years experience as a Customer Success Advocate and CBDO
IT business women with extensive experience in establishing long-term relationships. Advocating the success means helping partners achieve their business goals effectively through custom approach to each Data Science project. Leeds Unicsoft to success through team coaching and mentorship, business development and strategic planning.
Vyacheslav Basov
СТО, Data Science at Unicsoft
PhD, 9 years’ experience as a Lead Data Scientist, MBA in progress
Experienced R&D Delivery & Engineering Lead, Manager of Big Data, Data Science teams with strong competencies in system analysis, quantitative analysis. Has hands-on experience with huge stack Machine Learning & Big Data technologies. Working areas: Finance, Assurance, Advertisement, FMCG, Banks, Logistics, Fraud Detection, Multimedia & Digital.