The Talk: Starting a Data Science Project

I have found that some of the more inspiring moments in my career have come from talks about AI and Data Science at various conferences that I have attended in the past like Grace Hopper, and The Data Science Conference. The inspiration comes more from the passion and genuine interest with which the speakers express themselves than the subject matter itself (although it helps if it is a topic I find interesting). On the 19th of June, I found myself in the extraordinary position of having the opportunity to apply for a spot in the Bit by Bit Virtual Learning Conference and on the 24th of June I was told that I would be giving my very first talk at a conference! The talk was scheduled to be held on the 2nd of July 2020.

Since this conference was attended by mostly college students interested in knowing more about how to kickstart their careers in data science, I decided to talk about something that got me started in my career: a project. More specifically a data science project. In this talk, I go over the following topics:

• A brief introduction to what data science is

• An outline of what a data science project looks like

• The process of selecting the right data

• The process of cleaning and understanding the data

• The importance of justifying the use of each machine learning model in the project

• Presenting the projects to the world (i.e. creating a data science portfolio).

You can find the talk below, and to make things even better, I got some great questions at the end by the attendees.