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Data Science vs. Analytics: Approaches to Problem Solving

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Nicholas Kolegraff

on 2 March 2013

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Transcript of Data Science vs. Analytics: Approaches to Problem Solving

Data Science VS Analytics Approaches to Problem Solving Twitter: @nickkolegraff
Email: nicholas.kolegraff@accenture.com
My Boss: vincent.u.dellanno@accenture.com
Cool Person: andrew.musselman@accenture.com Nick Kolegraff, Lead Data Scientist @ Accenture Puzzles Two Types: pieces exist. we just need to put them together. All pieces might not exist. Solutions are unknown. Problem deconstruction becomes critical. "How" Approach + Abstract Problem = Cool solution. Wrong problem. Solving for the "How" without understanding the "Why" and you get a solved problem for the wrong reasons. Assumptive When variable relationship is better understood. Temperature = Hot | Cold | Warm Wear = Shorts | Coat | Pants Assume nothing-ish and attempt to account for everything, kinda. when variables have a complex relationship. Being = Mother | Girlfriend | Grandmother | Dog
Action = Kiss | Call | Hug | Play Target specifically and adjust accordingly. "Why" Approach + Abstract Problem = Good Solution. Understanding the "Why" then solving the "How" and you get a solved problem for the right reasons. After multiple failed attempts of me trying to explain what I do differently to my boss, Vince, he suggested this. Not Just Database Structured
Unstructured
Transactional
Text
Social
Sensor
Signal Data Types
Data Speed
Data Size Millisecond,
Minutes,
Days Speed: BS Statistics, BA Computer Science Culture is Key to solving these problems Hard to structure innovation, better to enable it. Abstract Non-Assumptive generally don't ask why, the pieces are in the box we just need to assemble them. How? We Have to ask 'why' in order to understand the unknowns. WHY: We need both and use both so really,

Data Science + Analytics Product vs Project Why xTB, do we need that?
Could we sample down, then scale up? Asking 'why' naturally forces you to deconstruct a problem to a more pure form. Standards Structure Efficiency and Consistency. Unstructured-ish Process Innovative With puzzles, we don't have to ask 'why'
we just define how we do it. Discriminative Modeling Generative Modeling Creativity, Curiosity, Creation How is difficult because Why wasn't solved. <Many Business Problems Go Here> When a problem makes it here, we call it "industrialized" Industrialize? Psssh. #innovationalize Ugh. It didn't work. If we are industrialized ... how do we innovationalize? Why? vs. Understanding what pieces we need for a possible solution requires deep questions. Make you question that something different might exist. You Decide. Problems
Basic Deconstruction
Types of Modeling
Infrastructure, kinda
Decisions Not just infrastructure needs to support these.
models do too. Trying to solve abstract problems in a 'how' culture is difficult. Cool... Cool... Tying this in to modeling .. But, not only do we have different modeling approaches. Infrastructure is different too. Types: Size Not limited to disk, billions of ids are large too Creates many abstract unsolved problems. (Puzzle) (Abstract) Come talk during office hours right after this. Accenture Data Science. Solve for why and how becomes easy. There is more... Calculus helps us understand our world.
Statistics helps us live in our world. Translation: We have decisions to make. Conscious Instinctive Data Repetition. aka practice. (think athletics) You don't know why, you just know. (think athletics again) Critical for business leaders. (many decisions are still instinct) I don't think that will change anytime soon.
Imagine ... (hold on while I take you down) But how do we get better? ... we have data, models, infrastructure, decisions with specific requirements ... Because we don't really know what we are doing. We have xTB of data, we want to predict the probability of <event> How do we do that? .... You'll focus on scale when you probably don't need it. Still Difficult.
Different Problem.
Different Approach.
Tactical. Objective:
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