This week ... Joyce and Jen interview Jana, a Data Scientist. Is it data, or is it science. Dang girl, it's both! We find out what a day looks like for Jana, what type of problems they solve in the workplace, and everything in between!
Resources to learn more about Data Science in the show notes. You can also check out a timestamp of the interview below. Thanks for listening, and be sure to follow and subscribe!
Resources from Jana
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Timestamps!
6:50 Intro to Jana Dodson
7:15 Career Title: Data Scientist
09:01 What does a data scientist do?
10:33 Difference between analytics vs machine learning vs engineer
13:45 How is data measured, organized, and determined whether it is valuable
16:00 Are data scientists a combination of analytics, machine learning, and engineer
17:10 What made you want to be a data scientist 18:20 What Jenna studied in undergrad
20:25 How much computer science background do you need
22:02 How to become a data scientist - classes, resources, etc
24:17 Day in a life
26:30 Behavioral models, customer behavior, vs operational data
28:50 How is a data scientist similar to an: Economist? Business analyst?
31:43 What area in physics did you specialize in, plan to pursue?
33:45 What comprises the core of your job?
36:30 How do you approach your data for analysis? How often is your data informed by a pre-formed hypothesis?
39:00 Prediction of the data scientist label in the future
41:15 Soft skills recommended for data scientists
42:40 What do you gravitate towards more - analytics, machine learning, engineer
44:38 What type of data do you work with; the question, how to find a solution, etc
47:03 Startups vs veteran company expectations 48:30 Obstacles and how the job is evolving
50:52 QF: What about your job gives you energy vs drains you energy
51:43 QF: What kind of person would thrive in a job like this
52:48 QF: How does one determine the subrole they should pursue
