Data & Science with Glen Wright Colopy

Philosophy of Data Science Series

Session 1: Scientific Reasoning for Practical Data Science

Episode 3: Communicating the Science in Data Science

One of the biggest challenges in data scientists is to communicate why your work matters. Kathy Ensor (ASA 2022 President and Rice University’s Noah Harding Professor of Statistics) covers how to distinguish yourself as a professional by communicating both your scientific and technical value. (Hint: The same scientific reasoning that helps you do good work in data science will also help you critically assess “how” and “what” to communicate.)

Watch it on...

YouTube: https://youtu.be/Vtasc0GGKDs

Podbean:

You can join our mail list at: https://www.podofasclepius.com/mail-list

We're always happy to hear your feedback and ideas - just post it in the YouTube comment section to start a conversation.

Thank you for your time and support of the series!

Philosophy of Data Science Series

Scientific reasoning plays an essential role in data science and statistics, both for developing new methods and applying our methods to real-world problems. In Session 1's titular episode, Andrew Gelman talks through the role of scientific thinking in his approach to data analysis. He also highlights the good ideas that have been generated by the wider statistical community. 

Coming up next week: Communicating the Science in Data Science with Kathy Ensor (Rice University & 2022 ASA President)

We're always happy to hear your feedback and ideas - just post it in the YouTube comment section to start a conversation.

Thank you for your time and support of the series!

You can join our mail list at: https://www.podofasclepius.com/mail-list

#datascience #statistics #machinelearning #ai #science #stem

Philosophy of Data Science Series

Session 1: Scientific Reasoning for Practical Data Science

Episode 1: Critical Reasoning in Medical Machine Learning

Data science in medicine and healthcare requires not only algorithmic and statistical knowledge but also a strong appreciation of the clinical environment in which (i) the data is being collected and (ii) the algorithm will be used. I'll showcase a scenario where a machine learning system failed to perform a "simple" clinical task and how critical reasoning was used to resolve the problem.

Guest-host Kristin Morgan (University of Connecticut) joins us to lead the discussion in how this example is applicable to the broader field of biomedical data science.

This is...

Session 1: Scientific Reasoning for Practical Data Science

Episode 1: Critical Reasoning in Medical Machine Learning

Watch it on... YouTube: https://youtu.be/o5YmdoCiyug

Podbean:

Coming up next week: Applying Scientific Reasoning to Statistical Practice with Andrew Gelman (Columbia University)

We're always happy to hear your feedback and ideas - just post it in the YouTube comment section to start a conversation.

Thank you for your time and support of the series!

The Philosophy of Data Science Series

Session 1: Scientific Reasoning for Practical Data Science

Episode 0: Welcome to the Philosophy of Data Science Series!

This is our very first episode of "The Philosophy of Data Science" series on Pod of Asclepius!

We go over our plans for the series plus some thoughts on why data science is such a rich field for discussions on scientific reasoning. Your time is valuable and you deserve a good explanation of why the topics were chosen and how the series is structured to maximize learning.

Topic List

0:00 New intro jingle for the series!

0:10 Welcome to the Philosophy of Data Science Series!

1:07 Modes of reasoning

5:33 Session 1 Overview: Scientific Reasoning for Practical Data Science

10:15 Session 2 Overview: Essential Reasoning Skills for Data Science

11:32 Keynotes and Session 4

14:15 Future Sessions

Coming up next week: Critical Reasoning in Medical Machine Learning

Thank you for your time and support of the series! It only gets better from here! (Seriously, it really does only get better from here. We've got Andrew Gelman coming up, plus Cynthia Rudin, Mihaela van der Schaar...)

Lisa LaVange (Gillings School of Global Public Health at the University of North Carolina at Chapel Hill) was the 2018 American Statistical Association (ASA) president and the director of the Office of Biostatistics in the Center for Drug Evaluation and Research (CDER) at the FDA.

She give a high-level overview of issues surrounding Innovative Trial Design and Master Protocols. A great listen for anyone wanting to be introduced to the subject or (for those already familiar) interested in its growing breadth of applications.

#datascience #statistics #biopharm #pharma #FDA

Amy Shi (SAS), Emily Griffith (North Carolina State University), and Elizabeth Mannshardt (EPA) discuss the many activities of the North Carolina Chapter of the American Statistical Association, including a lot of online activities that can be enjoyed even if you aren't in NC. The recording was made on the cusp of COVID...so updated information is posted below. NC ASA Activities NC ASA YouTube Channel: https://www.youtube.com/channel/UCPMPV3vCOY2dZka5ELPBWpA NC ASA Website: https://community.amstat.org/northcarolina/home

Molham Aref and Nathan Daly describe their experience using Julia to build a next-generation knowledge graph database that combines reasoning and learning to solve problems that have historically been intractable. They explain how Julia's unique features enabled them to build a high-performance database with less time and effort. Both Nathan and Molham with be speaking at JuliaCon 2020 at the end of July. It's free and online, so there's no reason not to attend. You can register for JuliaCon 2020 here: https://juliacon.org/2020/

0:00 Intro

1:25 RelationalAI

3:25 Advantages of Julia as a foundation

4:21 "Full stack" data science

5:38 Advantages of Julia in the tech stack

6:30 Technical requirements of RelationalAI

7:45 Advantages of Julia (cont.)

10:00 Data munging, preprocessing, and transparency

14:30 Advantages of Julia (cont.)

18:35 RelationalAI's Innovation

22:00 Data Analysis and taking computational efficiency for granted

23:38 Who are the users of RelationalAI?

25:45 What are "knowledge graphs"?

28:30 Knowledge graphs for AI and Software 2.0

32:43 Julia as "executable math"

34:10 "Multiple dispatch" in a nutshell

36:20 Julia in the scientific community

38:53 See Nathan and Molham again at JuliaCon 2020