A perspective on data science that I presented at the Hollywood Data Science Meetup on April 28, 2016

1. © Manish Kurse, 2016
Data Science - a Perspective
Manish Kurse, Ph.D.
Data Scientist, Google
28 April 2016
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This is my perspective and is not necessarily intended to represent that of my employer

2. © Manish Kurse, 2016
Agenda
An Introduction
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Insights on being a Data Scientist in the Industry
Thoughts about this evolving field
Lessons learnt on transitioning to data science

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An Introduction
Insights on being a Data Scientist in the Industry
Thoughts about this evolving field
Lessons learnt on transitioning to data science
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Extracting insights from structured
and unstructured data
Creating actionable solutions and
products based on these insights
What is Data Science?
Courtesy : Drew Conway 4
(Programming)
(Domain expertise)

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Popular Examples of Data Science
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Recommendation Systems Inventory planning Dynamic
pricing

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Interest in data science has grown rapidly!
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Why this rise in interest?
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Digital Connected World
Data storage is cheap
Computational power is cheap
Need to make sense of data

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Blind Men and an Elephant
Taken from the internet. Original artist: Not sure
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What do data scientists do in the industry?

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Developing
models and
building products
using data
Data Science today is a spectrum
Business
analysts
generating
insights
Researchers
developing new
mathematical
techniques and
algorithms
Insight 1:
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Data Scientists wear several hats
Dashboards
Continuous
Business Insights
Insight 2:
Slide-decks
Actionable insights
Software
Products
Prototyping
Tools and
infrastructure
Data science
platforms
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Data Science Interfaces with Several Teams
Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
Determine need with stakeholders.
Experimentation
Data cleaning
Insight 3:
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Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
Work with engineers, set-up new data
logging
Experimentation
Data cleaning
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Insight 3:
Data Science Interfaces with Several Teams

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Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
Data engineering
Experimentation
Data cleaning
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Insight 3:
Data Science Interfaces with Several Teams

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Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
Experimentation
Data cleaning
Clean raw data, exploratory
analysis
Insight 3:
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Data Science Interfaces with Several Teams

16. © Manish Kurse, 2016
Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
Experimentation
Machine learning/
computational models
Data cleaning
Insight 3:
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Data Science Interfaces with Several Teams

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Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
U/X
Experimentation
Data cleaning
Data Science Interfaces with Several Teams
Insight 3:
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Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
Get user feedback
Experimentation
Data cleaning
Data Science Interfaces with Several Teams
Insight 3:
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Data Science Interfaces with Several Teams
Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
ProductionizeWork with Software Engineers
Experimentation
Data cleaning
Insight 3:
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20. © Manish Kurse, 2016
Data Science Interfaces with Several Teams
Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
Launch to customers/stakeholders
Experimentation
Data cleaning
Insight 3:
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21. © Manish Kurse, 2016
Data Science Interfaces with Several Teams
Define project
Define data sources
Build pipelines
Build models
Visualization
Evaluate with users
Launch
Productionize
ExperimentationA/B Experiments
Data cleaning
Insight 3:
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Every Stage in Business is a Data Science Opportunity
Product
Sales
Customer SupportCustomer engagement
Marketing
Understanding need
Insight 4:
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Getting the right data could take time, effort
Change is constant and not everything can be modeled
Data cannot solve everything
Gaining stakeholder trust and showing value
Data Science is Challenging
Insight 5:
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Thoughts on Data Science Evolution

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Need for data scientists will continue to exist
Growing data science tools
Data scientists are needed to ask the
right questions
Define the data, the solution
Role of a data scientist will evolve
Google Cloud Machine
Learning
Thought 1:
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Data science will be an integral part of business strategy
Thought 2:
Data Infrastructure
Understanding
Business Need
Understanding
Customers
Data Logging
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Machine learning will influence non-data scientist roles
Thought 3:
Machine learning becomes
mainstream
Business analysts apply more
complex predictive models
Software engineers are trained in
building machine learning software
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Security and Privacy should/will be a focus
“With Great Power
Comes Great
Responsibility”
Data Science
Thought 4:
28Source: Marvel

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Journey towards Data Science
Source: rei.com
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Spend time to understand the field
Books
Data Science for
Business
Doing Data
Science
Big Data: A
Revolution...
Longer List
Podcasts
Linear Digressions
Data Skeptic
Partially Derivative
...
Longer list
Follow
Subscriptions on
online magazines
like Flipboard
Data scientists in
your field of
interest
Longer list
Blogs
KDNuggets
DataTau
Analytics Vidya
Longer List
Lesson 1:
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Online tutorials
Algorithms and data structures
Python: Tutorials, Python for Data
Analysis
R: Tutorials
SQL: Tutorials
Knowledge of tools is important, but understanding
of fundamentals is key
Lesson 2
Classes
MOOCs: Udacity, Coursera
Bootcamps: Logit, Insight, General
Assembly, Data Incubator
Mentored Courses: Thinkful, Springboard
Machine Learning, Statistics, Programming
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Free Datasets
Interesting data-sets for
statistics
Datasets curated by data
scientists
Data sources for cool data
science projects
Side-Projects are invaluable
Lesson 3
Side projects
Mini projects
Online contests like kaggle.
com
Article about choosing
projects
Create a web
portfolio
Host code on github
Creating a website
hosted on github
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Exciting Time to be in Data Science!
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An Introduction
Insights on being a Data Scientist in the Industry
Thoughts about this evolving field
Lessons learnt on transitioning to data science