The Pod of Asclepius
Data Science Career Q&A for Undergrads

Data Science Career Q&A for Undergrads

February 25, 2021

#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

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Philosophy of Data Science | Step-change and Anomaly Detection | Alex Bolton

Philosophy of Data Science | Step-change and Anomaly Detection | Alex Bolton

February 16, 2021

#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

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Irina Gaynanova | Replicating Clinical Metrics & Innovating New Methods

Irina Gaynanova | Replicating Clinical Metrics & Innovating New Methods

February 8, 2021

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!

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Karel Moons | Validating Medical Predictive Models | Philosophy of Data Science

Karel Moons | Validating Medical Predictive Models | Philosophy of Data Science

January 21, 2021
Philosophy of Data Science Series
Session 3: Data Science Highlight Reel
Episode 2: Karel Moons on Validating Medical Predictive Models
 
Watch it on...
YouTube: https://www.youtube.com/watch?v=Y6Qik_5hZog
Podbean:
 
Who makes it into the highlight reel of data science? Karel Moons and the classic BMJ Series on validating predictive/prognostic models for the clinic. You can start reading the BMJ Series for your self here:
 
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Philosophy of Data Science | S3 E1 | NeuralNets, GANs, Causality, and Medicine

Philosophy of Data Science | S3 E1 | NeuralNets, GANs, Causality, and Medicine

December 15, 2020

Philosophy of Data Science Series 
Session 3: Data Science Highlight Reel
Episode 1: Adler Perotte on NeuralNets, GANs, Causality, and Medicine

Watch it on... 
YouTube: https://www.youtube.com/watch?v=DOf2lVHzZS4
Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s3-e1-neuralnets-gans-causality-and-medicine/

Who makes it into the highlight reel of data science? Adler Perotte, because he's a clear thinker on why his data needs a specific type of analysis. In this case, it's the need to draw causal inferences from observational data. Go, GANS! Go!

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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! 

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Career Q&A: 10 Questions From a Beginner Data Scientist

Career Q&A: 10 Questions From a Beginner Data Scientist

December 9, 2020

Career Q&A: 10 Questions From a Beginner Data Scientist

Watch it on...
YouTube: https://youtu.be/ftikMj7MoYM
Podbean: https://podofasclepius.podbean.com/e/career-qa-10-questions-from-a-beginner-data-scientist/

This week's episode is likely of interest to early career data scientists or those interested in joining the field. Richard Franzese (Certara) & Glen Wright Colopy (Pod of Asclepius) team up to answer 10 questions from Ujjwal Oli, an MSc student at George Washington University MSc Program.

The questions range from technical requirements, to desirable soft skills and domain knowledge, to "how can I get an internship if they require prior experience?"

Please forward to any early-career statisticians or data scientists who would be interested.

Thank you for your support of the series!
You can join the mail list here: https://www.podofasclepius.com/mail-list

 

#datascience #career #job #jobadvice

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Philosophy of Data Science | Keynote 1 Presentation | Philosophy of Science & Statistics

Philosophy of Data Science | Keynote 1 Presentation | Philosophy of Science & Statistics

December 1, 2020

Philosophy of Data Science | Keynote 1 Presentation | Philosophy of Science & Statistics

Philosophy of Data Science Series 
Keynote with Deborah Mayo
Episode 2: The Philosophy of Science & Statistics

In the first keynote of the Philosophy of Data Science Series we have a 2-part interview with Deborah Mayo (Virginia Tech).
In the second part of our keynote, Deborah Mayo covers the interplay between scientific and statistical philosophy. Deborah highlights some common scientific fallacies, along with suggestions of where statistical thinking can be made more rigorous.

Watch it on... 
YouTube: https://youtu.be/9GGAXZ6htrA
Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-keynote-1-presentation-philosophy-of-science-statistics/

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Philosophy of Data Science | Keynote 1 Interview | Revolutions, Reforms, and Severe Testing in Statistical Thinking

Philosophy of Data Science | Keynote 1 Interview | Revolutions, Reforms, and Severe Testing in Statistical Thinking

November 24, 2020

Philosophy of Data Science Series 
Keynote with Deborah Mayo
Episode 1: Revolutions, Reforms, and Severe Testing in Statistical Thinking

In the first keynote of the Philosophy of Data Science Series we have a 2-part interview with Deborah Mayo (Virginia Tech).
In the first part of our keynote with Deborah Mayo we cover...
- The role of scientific revolution and its implications for statistics and data scientist.
- The necessity of statistical reforms and why philosophy will play a role.
- The value of severe testing of scientific claims.

Watch it on... 
YouTube: https://youtu.be/S4VAEShM3BU
Podbean: 

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Thank you for your time and support of the series! 

 

Topics:

0:00 - Preface to First Keynote Interview
2:00 - Welcome Deborah Mayo!
5:05 - What is the Philosophy of Statistics?
8:15 - What does philosophy add to data science?
16:10 - Scientific revolution in statistics
20:10 - Statistical reforms
24:25 - Replication & hypothesis pre-specification
31:00 - Failure is severe testing
37:25 - Error statistics
48:00 - Scientific progress and closing remarks

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Philosophy of Data Science | S01 E04 | Values and Subjectivity in Data Science

Philosophy of Data Science | S01 E04 | Values and Subjectivity in Data Science

November 16, 2020

Philosophy of Data Science Series

Session 1: Scientific Reasoning for Practical Data Science

Episode 4: Values and Subjectivity in Data Science

 

The Value-Free Ideal is a central tenant of objective science. But how do values, value judgements, and subjectivity leak into the practice of data science and statistics. To what extent is it desirable for science to be informed by values? Kevin Zollman (Carnegie Mellon University) covers the range of key ideas, from Heather E. Douglas to W.E.B. du Bois.

 

Watch it on...

YouTube: https://youtu.be/9USkWtX-ydc

Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s01-e04-values-and-subjectivity-in-data-science/

 

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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!

 

0:00 Intro

0:03 Welcome Kevin Zollman (Carnegie Mellon University)!

1:44 Is Science Value-Free?

6:08 How might values affect science?

9:00 Choice of Research Problem

10:45 Loss Functions

18:34 Choice of Variables

24:10 Choice of Statistical Model

29:30 Minimizing the Values in Science (W.E.B. du Bois)

35:20 Philosopher in Science

41:20 Statements on Generalizability

47:45 Clarifying Subjective Choices

52:45 Conflicts between Scientific Disciplines

61:18 Scientific Value Judgments & Self Correcting Science

67:50 Choice in Metrics and Research Focus

70:30 Concluding Ideas

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Philosophy of Data Science | S02 E04 | Intro to Abductive Reasoning for Data Scientists

Philosophy of Data Science | S02 E04 | Intro to Abductive Reasoning for Data Scientists

November 9, 2020

Philosophy of Data Science Series 
Session 2: Essential Reasoning Skills for Data Science
Episode 4: Intro to Abductive Reasoning for Data Scientists

Watch it on... 
YouTube: https://youtu.be/SzQn9SPVhRU
Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s02-e04-intro-to-abductive-reasoning-for-data-scientists/

The third and final of our (planned) short tutorials on key modes of critical reasoning. Abduction is common called "inference to the best explanation"...so it's easy to see why this concept is important for data scientists. 

Huub Brouwer (Utrecht University) walks us through a brief tutorial on how even a world-famous infer-er can get this wrong and how data scientists can avoid the same mistake.

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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! 

0:00 Intro
0:18 Example of Abduction in Action
4:55 Definition of Abduction
6:21 Applying Abductive Reasoning
8:35 Why is Abduction Not Deduction?
14:55 Abduction in Data Sciences
17:40 Conclusion

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