We're a data science podcast, focused on the latest & greatest of the DS ecosystem, sprinkled in with our musings & data science expertise. With topics ranging from ethical AI and transparency to robot pets, our hosts, Triveni & Will, are here to keep you up to date on the latest trends, news, and big convos in data. Click hereto to listen to the Banana Data Podcast.
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In our season 3 kickoff, we’re challenging ourselves to ask --who grants authority to those in charge of validating content? How do we remain cognizant of big tech and corporations that shape our content and decisions? In a landscape filled with big, competitive players - we explore how data scientists should focus their learnings.
This episode, in honor of draft season, we’re discussing the NFL’s newest tactics to quantify and predict players’ success, and diving into Spotify’s case for data discovery. Leaving behind the problems of “not enough data,” Will and Triveni ask new questions: when we have so much data, where do we start, how do we organize it, and how can we use it?
With the upcoming 2020 presidential election, there's a lot for data scientists and analysts to learn from the political realm and its unending streams of messy data. Will and Triveni sit down with seasoned political data expert, Grace Turke-Martinez, Analytics Director at The Messina Group to understand how political data professionals extrapolate insights from messy data, work around human indecision, and forecast using imperfect data sets.
AI is meant to help us expedite processes and get to the conclusions quicker. But, what happens when the process that AI takes to get to the end goal is erroneous? In this episode we discuss how you can prevent your AI from cheating and define what it means to be a successful AI company in today’s tech-saturated world.
For our Season 3 finale, we're taking a look at model accuracy, the threat of generalized results, and how to understand and demonstrate the nuanced results of your models. Is the onus on scientists and journalists to subdue buzzy headlines or should media consumers be more wary of extrapolated statistics?
We also take a peek into how the NYT applies Machine Learning to their comment moderation, and how human-in-the-loop monitoring works behind the scenes, especially in fast-paced and ethically questioning environments.
This is also our final episode with Will on the team - and we'd like to thank him for all of the hard work, great ideas, and many laughs he's provided with us along the way. He's been an invaluable team member, but do not fear!