Data Science by R-Ladies and Prometeia[Webinar]

We are back again! In this particular situation, like other meetups groups we are moving our events to online version. This time we proudly are hosting an event with Prometeia with super interesting topics:

Please register in the event by this link:
https://register.gotowebinar.com/register/5263059103792573710
(The webinar link will be shared after registration).

Agenda of event:

5:00PM - 5:15PM: Opening and Data Science team introduction by Oriana Angelucci (Senior Analyst)
5:15PM - 5:45PM: Focus 1 - Business Translator: your ally to avoid Mexican standoffs in business contexts by Marilena Di Bari (Senior Analyst)
5:45PM - 6:15PM: Focus 2 - The riddle of the Sphinx in the era of AI: answering the “whys” of your results through SHAP by Martina Dossi (Junior Analyst), Giulia Gavazzi (Junior Analyst)
6:15PM - 6:30PM: Q&A session
6:30PM - 6:45PM: Closing remarks by Maddalena Amoruso (Partner)

ABSTRACT - FOCUS 1

Business translators have a huge impact on the success of business projects. Their job is to follow a data science project from the early design until the end: to identify its business requirements, translate them into analytical problems and support all the parties involved in arriving towards a satisfactory solution... and on time. In this talk, we will go through the nitty-gritty of business translation and we will show how some of the tricks could benefit all of us working in data science, regardless of our role.

ABSTRACT - FOCUS 2

In the last decade, the complexity of predictive models has increased. This trend has favored accuracy and prediction rates at the expense of interpretability. However, the ability to correctly interpret the output is extremely important to justify future actions. Many methods have been proposed to deal with the need for understanding black boxes. SHAP is a powerful framework based on game theory that allows interpreting any machine learning model. In this talk we will first define the basic concepts of model interpretability and explainability and discuss the importance of explainable machine learning models, then we will introduce SHAP through a practical case study.

Speakers:
*Oriana Angelucci is a senior business translator. She worked in market research and now focused on data-driven transformation of Customer Relationship Management processes through the application of advanced analytics.

*Marilena Di Bari, Ph.D. is a senior business translator. She previously worked in the fields of computational linguistics and translation, and is now involved in data-driven projects of Customer Relationship Management and Bancassurance.

*Martina Dossi is a statistician and junior data scientist. She worked in the area of risk management and is now involved in text analytics-based solutions within the credit risk area.

*Giulia Gavazzi is a statistician and junior data scientist. She is mainly involved in machine learning solutions within the areas of operational and credit risk and prepayment risk modelling for ALM.

*Maddalena Amoruso is a partner in Prometeia. She manages the Data Science division, which aims to foster and support customers' digital transformation needs.

Looking forward to e-meet you all!