data science perspective, manish kurse, 2016

© Manish Kurse, 2016

Data Science - a PerspectiveManish Kurse, Ph.D.

Data Scientist, Google28 April 2016

1This is my perspective and is not necessarily intended to represent that of my employer


Agenda

An Introduction

2

Insights on being a Data Scientist in the Industry

Thoughts about this evolving field

Lessons learnt on transitioning to data science


An Introduction



Lessons learnt on transitioning to data science3


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)

http://drewconway.com/


Popular Examples of Data Science

5

Recommendation Systems Inventory planning Dynamic pricing


Interest in data science has grown rapidly!

6


Why this rise in interest?

7

Digital Connected World

Data storage is cheap

Computational power is cheap

Need to make sense of data


Blind Men and an Elephant

Taken from the internet. Original artist: Not sure8

© Manish Kurse, 2016 99

What do data scientists do in the industry?


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:

10


Data Scientists wear several hats

Dashboards Continuous

Business Insights

Insight 2:

Slide-decksActionable insights

SoftwareProducts

Prototyping

Tools and infrastructureData science

platforms11


Data Science Interfaces with Several TeamsDefine project

Define data sources

Build pipelines

Build models

Visualization

Evaluate with users

Launch

Productionize

Determine need with stakeholders.

Experimentation

Data cleaning

Insight 3:

12


Define project

Define data sourcesBuild pipelines

Build models

Visualization

Evaluate with users

Launch

Productionize

Work with engineers, set-up new data logging

Experimentation

Data cleaning

13

Insight 3:Data Science Interfaces with Several Teams


Define project

Define data sources

Build pipelines

Build models

Visualization

Evaluate with users

Launch

Productionize

Data engineering

Experimentation

Data cleaning

14

Insight 3:Data Science Interfaces with Several Teams


Define project

Define data sources

Build pipelines

Build models

Visualization

Evaluate with users

Launch

Productionize

Experimentation

Data cleaningClean raw data, exploratory

analysis

Insight 3:

15

Data Science Interfaces with Several Teams


Define project

Define data sources

Build pipelines

Build modelsVisualization

Evaluate with users

Launch

Productionize

Experimentation

Machine learning/ computational models

Data cleaning

Insight 3:

16

Data Science Interfaces with Several Teams


Define project

Define data sources

Build pipelines

Build models

VisualizationEvaluate with users

Launch

Productionize

U/X

Experimentation

Data cleaning

Data Science Interfaces with Several TeamsInsight 3:

17


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 TeamsInsight 3:

18



Define data sources

Build pipelines

Build models

Visualization

Evaluate with users

Launch

ProductionizeWork with Software Engineers

Experimentation

Data cleaning

Insight 3:

19



Define data sources

Build pipelines

Build models

Visualization

Evaluate with users

LaunchProductionize

Launch to customers/stakeholdersExperimentation

Data cleaning

Insight 3:

20



Define data sources

Build pipelines

Build models

Visualization

Evaluate with users

Launch

Productionize

ExperimentationA/B Experiments

Data cleaning

Insight 3:

21


Every Stage in Business is a Data Science Opportunity

Product

Sales

Customer SupportCustomer engagement

Marketing

Understanding need

Insight 4:

22


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 ChallengingInsight 5:

23


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:

25


Data science will be an integral part of business strategyThought 2:

Data Infrastructure

Understanding Business Need

Understanding Customers

Data Logging

26


Machine learning will influence non-data scientist rolesThought 3:

Machine learning becomes mainstream

Business analysts apply more complex predictive models

Software engineers are trained in building machine learning software

27


Security and Privacy should/will be a focus

“With Great Power Comes Great Responsibility”

Data Science

Thought 4:

28Source: Marvel


Journey towards Data Science

Source: rei.com29


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:

30

http://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323




http://www.amazon.com/Doing-Data-Science-Straight-Frontline/dp/1449358659




http://www.amazon.com/Big-Data-Revolution-Transform-Think/dp/0544227751




http://www.mastersindatascience.org/blog/7-must-read-books-for-the-budding-data-scientist/

http://www.mastersindatascience.org/blog/7-must-read-books-for-the-budding-data-scientist/

http://lineardigressions.com/



http://dataskeptic.com/

http://dataskeptic.com/

http://www.partiallyderivative.com/

http://www.partiallyderivative.com/

http://dataconomy.com/top-10-data-science-and-machine-learning-podcasts/


https://flipboard.com/@manishkurse/data-science-1c0n1klgy

http://www.kdnuggets.com/2012/12/most-influential-data-scientists-on-twitter.html



http://www.kdnuggets.com/

http://www.kdnuggets.com/

http://www.datatau.com/

http://www.datatau.com/

http://www.analyticsvidhya.com/

http://www.analyticsvidhya.com/

http://www.kdnuggets.com/2015/10/best-blogs-analytics-big-data-science-machine-learning.html

http://www.kdnuggets.com/2015/10/best-blogs-analytics-big-data-science-machine-learning.html


Online tutorialsAlgorithms and data structuresPython: Tutorials, Python for Data Analysis R: TutorialsSQL: Tutorials

Knowledge of tools is important, but understanding of fundamentals is key

Lesson 2

ClassesMOOCs: Udacity, CourseraBootcamps: Logit, Insight, General Assembly, Data IncubatorMentored Courses: Thinkful, Springboard

Machine Learning, Statistics, Programming

31

http://interactivepython.org/runestone/static/pythonds/index.html

http://interactivepython.org/runestone/static/pythonds/index.html

https://developers.google.com/edu/python/?csw=1

http://shop.oreilly.com/product/0636920023784.do



https://www.datacamp.com/courses/free-introduction-to-r

http://sqlzoo.net/wiki/SQL_Tutorial

http://www.kdnuggets.com/2015/09/top-20-data-science-moocs.html

http://www.kdnuggets.com/2015/09/top-20-data-science-moocs.html

http://www.mastersindatascience.org/schools/data-science-bootcamps/




Free DatasetsInteresting data-sets for statistics

Datasets curated by data scientists

Data sources for cool data science projects

Side-Projects are invaluableLesson 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

32

http://rs.io/100-interesting-data-sets-for-statistics/




http://blog.mortardata.com/post/67652898761/6-dataset-lists-curated-by-data-scientists




http://101.datascience.community/2014/10/17/data-sources-for-cool-data-science-projects-part-1-guest-post/




http://kaggle.com

http://kaggle.com

http://kaggle.com

http://www.getadatasciencejob.com/advice/how-to-choose-a-data-science-project-for-your-data-science-portfolio




https://github.com/

https://github.com/

http://thejackalofjavascript.com/your-portfolio-website-with-github-pages/





Exciting Time to be in Data Science!

33

An Introduction



Lessons learnt on transitioning to data science

data science perspective, manish kurse, 2016

Data & Analytics