The Importance of Data Scientists in Interpretable Machine Learning

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Kristen has been ranked #8 Global LinkedIn Top Voice in Data Science & Analytics. She has 10 years’ experience in data science delivering innovative and actionable machine learning solutions across the eCommerce, healthcare, & utility industries. Kristen is currently an instructor at UC Berkeley Ext. teaching Practical Data Science in collaboration with Emeritus Institute of Management, holds an MS in Applied Statistics from Worcester Polytechnic Institute and a BS in Mathematics.

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Kristen Kehrer’s Website: https://datamovesme.com 

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:39) – Remote learning environments are the world of Work 2.0

(03:33) – Many people believe Data Science means working remotely, but you need to build reputation and experience first

(05:41) –Education and work are moving towards remote environments but we’re not completely there yet

(08:18) – Working remotely simplifies busy schedules. It's a model that works for people and the lives that they've built

(11:49) – Without diminishing the experience of being able to learn on campus and build real relationships, online learning is very useful for professional development 

(13:46) – Saving for future education requires financial advice, considering the college admission scandal and changing world conditions

(17:21) – Models must be interpretable. AutoML does not allow to set parameters

(18:38) – Some tasks can be automated by interpreting these AutoML models and some others should be done manually, depending on the factors to be determined by them

(19:29) – Dehumanization by automation: products that we're using each and every day implementing AutoML or auto AI solutions.

(22:29) – There will always be a need for analysts who have business context and understand how variables interact

(23:28) – Data science, the beginning of machine learning, is the foundation that helps get into more advanced algorithms

(24:45) – Stop learning and start applying to positions. Get feedback from the market in order to know what skills or gaps you have on your resume that keep you from getting a job in the industry. Work your way up.

(28:14) – We're living in a data-swamped life among devices, information and messages that are distracting 

(29:40) – Defining the role of a Data Scientist is quite complicated. People trying to get into the industry, data getting bigger, tools getting easier. Things are becoming more automated. Data is the ‘new oil’

(33:41) – Data Science is going to focus the highest priority on the objectives that are going to result in the highest ROI

(35:15) – A data-obsessed culture that wants every single decision to be made on data. And it does not need to be used for everything, if it's not actually going to inform what it is that we're going to do