Breakfast Event - 'The Pitfalls of Data Science'

At this breakfast event we'll be hearing from seasoned practitioners about their experience's on how data science can go wrong.

Specifically in;

a) model building
b) communicating
c) managing teams

7:45 - breakfast
8:00 - talks commence

Els Godecharle - Data Science Communication

As Data Scientists we often start our careers with a quantitative background. We are trained to solve problems by thinking logically and to communicate the details of our work in the same way. However, when we communicate with non-technical audiences this approach doesn’t always resonate. The aim of this talk is to help you increase the impact of your work through effective communication.

https://www.linkedin.com/in/elsgodecharle/

Michael Brand - Pitfalls of Analytics

Statistical modelling is an error-prone endeavour. Mistakes are easy to make and hard to detect. For over a decade now, Michael Brand has been running regular peer reviews for data science projects, and almost without exception these reviews uncovered serious issues that required major revision to the analysis.

In this talk, Michael will recount some of the more dazzling blunders he caught over the years. More importantly, he will demonstrate that these are not one-off, unpredictable human errors; rather, they are a direct result of the standard practices of data science, standard practices that in recent years have only been getting worse. Michael will discuss what each of us can do, individually, to avoid falling into the cognitive traps, and what we can do together, as a community, to shift to better standard practices

https://www.linkedin.com/in/michael-brand-b230736/

Sandra Hogan - Operationalising Data Science

How can you make sure that the outputs of your hard work as a Data Scientist are valued by the business? How many times have you developed a strong predictive model but it never quite reaches the people who can extract value from it?

In this chat, we will touch on some challenges organisations face in operationalising data science outputs, why and how things go wrong. We will also discuss some activities that can help to mitigate the issues and increase the likelihood of a successful outcome

https://www.linkedin.com/in/sandra-hogan-9409421/