Data & Science with Glen Wright Colopy

Michael McRoberts | Football Analytics and Data-Driven Decisions

Michael McRoberts (Championship Analytics Inc.) uses Monte Carlo simulations to provide strategy analytics to college and NFL football teams. Topics include communicating data-driven recommendations, the need to create counterfactual data, and asymmetric decision rewards.

0:00 The challenge of sports analytics

5:00 Analytics recommendations

16:00 Communicating data-driven recommendations

24:35 Vegas Odds & Ancillary Data

30:00 Football is way behind / Data science projects with a "runway"

41:25 Creating experiments and counterfactuals

49:30 Implementing data science insights

56:15 Asymmetric decision rewards

58:50 How to start in sports analytics

1:10:00 Data science vs analytics vs statistics

Andrew Gelman & Megan Higgs | Statistics' Role in Science and Pseudoscience

#datascience #statistics #science #pseudoscience

Our science vs pseudoscience discussion continues with Andrew Gelman (Columbia) and Megan Higgs (Critical Inference LLC). Andrew and Megan describe two critical roles that statistics plays in science.... but also how statistics can add the air of scientific rigor to bad research or help statisticians fool themselves. From there the conversation goes on in a way that only a conversation with Andrew and Megan can! A very fun episode.

0:00 - Two roles of statistics in science

Irina Gaynanova (Texas A&M) describes why she thinks that replicability is a prerequisite for reproducibility in science and how scientists can (personally) start improving the replicability of research. We also discuss how the concepts of replicability/reproducibility can differ according to the domain-specific context and the methods used.

Please forward to any students or colleagues who would find this of interest!

#datascience #science #pseudoscience #criticalthinking #reasoning

We each like to think of ourself as scientific. I'm yet to meet someone who would embrace being called "pseudoscientific". But what makes the difference? In this episode, Neil Manson talks about the fallout from Thomas Kuhn's 1962 book "The Structure of Scientific Revolutions" and how this created a playbook for many modern critiques/attacks on scientific activity.

We have a new series that centers on the discussion of science vs. pseudoscience. Guests of different backgrounds share their insights on what really constitutes science and the highly-contested pseudoscience.  The implications for data scientists and statisticians is very interesting, since many of the examples around this debate involved the conflicts between hypothesis-driven science vs data-driven science.

0:00 - Intro

We have a new series that centers on the discussion of science vs. pseudoscience. Guests of different backgrounds share their insights on what really constitutes science and the highly-contested pseudoscience. In today’s episode, we talk to Professor Dien Ho, PhD, a Professor of Philosophy and Healthcare Ethics, of the Massachusetts College of Pharmacy & Health Science University. Discover how philosophical ideas and theories are applied in hopes of understanding what really counts as science and what pseudoscience really is.

00:03 - Introductions

We're launching a series on "Science vs Pseudoscience" tomorrow! Also we've rebranded to better reflect the focus of the podcast. The focus of the podcast isn't changing - it's still data science, critical scientistic reasoning, and figuring out how to figure stuff out!

Some fun reading on pseudoscience: https://philpapers.org/archive/MONP-1...

We've received a lot of questions from early career data scientists interested in starting a career in environmental science and climate science. Elizabeth Mannshardt (EPA), Grant Weller (Optum Labs), and Megan Higgs (Critical Inference LLC) sit down to give you your answers!

Thinking about a career change to Environmental Data Science? We invite you to listen to some career growth strategies and opportunities in environmental data science” podcast.

Throughout the episode we discuss how to transition from other careers to an environmental data scientist. How to get quantitative skills in order to switch to environmental science. Ways someone can learn environmental science and get an entry job as an environmental scientist. Plus, “in career growth should one focus on a specific domain or to go broad?”.

00:00:00 Start

00:03:14 Introduction

00:09:03 Transition from other fields to Environmental Scientist.

00:22:27 How to get quantitative skills in order to switch to environmental science.

00:36:20 In career growth should one focus on a specific domain or be broad.

00:44:00 Ways someone can learn environmental science.

00:48:49 Ways people can get an entry job as an environmental scientist

01:07:54 Final comments

#datascience #career #job

Data Science Career Q&A for Undergrads with Mallory LaRusso

We continue to answer data science career questions. We've heard back from a lot of different groups about the world of data science. In this episode, we're talking about undergraduate DS job prospects. Mallory LaRusso is a senior at NCSU finishing her BS in Statistics, and Minor in Genetics. Watch/Listen as Glen and Richard answer questions from our guest Malory as she tries to understand ways of how to properly transition from being an undergrad student to becoming a data scientist. From questions about data scientists’ typical workday to their most challenging projects to date, we’ve got it all covered in this episode!

Keywords: data analytics, data science, programming, coding, workday, work culture, educational background

0:00 - Introduction

02:20 - Series overview

06:44 - Educational and career path

09:16 - Typical work day

17:00 - The importance of writing in data science

20:39 - Work culture

23:30 - Type of data you work with

26:00 - Mathematical vs Statistical Models

31:45 - The harder DS jobs are what’s left

32:35 - Favorite project as data scientist

36:00 - Work on a real problem 

39:55 - Data scientists’ degrees

44:15 - Difference of data analytics and data science

54:13 - Favorite programming language

1:01:29 - How data science jobs will change

1:07:42 - Largest data set to have worked with

1:17:02 - Advice for students to prepare for data science roles

1:36:30 - What advantage does an undergraduate have?

1:40:22 - Wrap-up

#datascience​ #ai​ #earlycareer​

Philosophy of Data Science Series

Session 3: Data Science Highlight Reel

Episode 4: Alex Bolton on Step-change and Anomaly Detection

Who makes it into the highlight reel of data science? Alex Bolton for doing the hard work of analyzing data to figure out exactly when things don't look "normal". We discuss the critical reasoning behind step-change detection and anomaly/novelty detection. Alex provides several real-world examples of the data and challenges.

Watch it on...

YouTube: https://www.youtube.com/watch?v=097FO1JDkhU

Podbean: 

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 

Session 3: Data Science Highlight Reel

Episode 2: Irina Gaynanova on Replicating Clinical Metrics & Innovating New Methods

Who makes it into the highlight reel of data science? Irina Gaynanova for her work on replicating clinical metrics for deployment. She then goes into how her grasp of the scientific domain helps her innovate new methods and metrics. Regardless of whether you work in the clinical domain, this is an example of rigorous scientific thinking in data science.

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!