Data science - Bricks with Clay

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Welcome to the 2 nd session of BET.. Faculty for the session: Dr. Sanjay Bhatikar Director, Monsanto Research Centre 11 th August 2016, from 2.25 pm to 4:00 pm In this session we will hear about B. E. T. - Business Education Timeout! Bricks with Clay: Building blocks for a career in Data science

Transcript of Data science - Bricks with Clay

Welcome to the 2nd session of BET..

Faculty for the session:Dr. Sanjay BhatikarDirector, Monsanto Research Centre

11th August 2016, from 2.25 pm to 4:00 pm

In this session we will hear about

So.. Let’s BET!

B. E. T. - Business Education Timeout!

Bricks with Clay: Building blocks for a career in Data science

I. THE ART OF SCIENCE

Art becomes science

Cogito, ergo sum

Cogito, ergo ..ummm

II. AI – AND SO IT BEGAN

Create

Market

Make

III. HOW TO DECIDE

ARE YOU SMARTER THAN 134 OTHER WOMEN IN MONSANTO WOMEN’S NETWORK?

1.In this short puzzle, you’ll try to outwit the masses – who are also trying to outwit you.

2. Your mission is to read the minds of your fellow Prasiti members.

3.Pick a number from 0 to

100, with that number representing your best guess of two-thirds of

the average of all numbers chosen in the

contest.

4. Stop and think for a

second. What is everyone

else going to do?

EXAMPLE I

Average: _______You Win by Picking: _______

41, 37, 42, 49, 33, 51, 43, 35, 47 4228

EXAMPLE II

Average: _______You Win by Picking: _______

21, 1, 12, 10, 13, 9, 17, 5, 11 117

When you’re ready, write the number down.

Stop and think for a second. What is everyone else going to do?

GAME ON!

We tend to make every decision like it is the first time ever.

DECISION

ALTERNATIVES

ARGUMENTSCRITERIA ASSUMPTIONS CONSTRAINTS

IV. OF SHAKESPEAREAN INSULTS & BEER

How to represent Date in an IT system?

String Julian Object

1. Ease of interpretation

2. Multiple nomenclatures

3. Operations require implementation

1. Counter-intuitive

2. One nomenclature for all purposes

3. Operations simpler with libraries

1. Platform dependent

2. Ease of operations

mm/dd/yy 1470900783

<interpretability> <operability> <portability>

V. DATA MODELS &

VISUALIZATION

Let’s have a party and invite all our friends!

Let’s do that!

Great! I’ll get a list going and mail it out

to you. Update it and revert.

What the

@*#?<!

# Name Veg Beer

1 Marco N Y

2 Ryan Y Y

# Name Veg Beer

1 Marco N Y

2 Ryan Y Y

3 Jane N Y

# Name Veg Beer

1 Marco N Y

3 Jane N N

# Name Veg Beer

1 Marco N Y

2 Ryan Y Y

3 Jane N N

MODELVIEW CONTROLLER

Movie

NameYear_of_ReleaseGenreRatingStars

Movie

IDNameYear_of_ReleaseRatingStars

Genre

IDName

MG_Link

Movie_IDGenre_ID

Movie

IDNameYear_of_ReleaseRating

Genre

IDName

MG_Link

Movie IDGenre ID

Actor

IDNameBornDiedAddress

MA_Link

Movie IDActor ID

Movie

IDNameYear_of_ReleaseRating

Genre

IDName

MG_Link

Movie_IDGenre_ID

Actor

IDNameBornDiedAddress

MA_Link

Movie_IDActor_ID

SELECT Movie.Name FROMMovie, Genre, MG_Link,Actor, MA_Link

WHEREGenre.ID = MG_Link.Genre_ID

AND MG_Link.Movie_ID = Movie.IDAND Actor.ID = MA_Link.Actor_IDAND MA_Link.Movie_ID = Movie.IDAND Genre.Name = “Thriller”AND Actor.Name = “Al Pacino”AND Movie.Rating > 8.5

Movie

IDNameYear_of_ReleaseGenreRatingStars

Genre

IDName

MG_Link

Movie IDGenre ID

Actor

IDNameBornDiedAddress

MAS_Link

Movie IDActor IDScreen_name ID

Screen_name

IDNameSynopsisQuotes

VI. APPLICATION PROGRAMMING

INTERFACE

POST

GET

PUT

DELE

TE

Request

Response

http://www.omdbapi.com/?t=Star+wars

{“Title”: “Star Wars”, “Year”: “1983”“Rated”: “N/A”…

}

VOLUME

VARIETY

VELOCTY

SHAREABLE

INTEGRATABLE

SCALABLE

UN

ICO

RNS

Construct the productImplement in code 3

Translate the problemSpeak business and IT 1

Re-imagine the processCast in digital avatar 2

JUST

DO

IT

Design APIsUse: Behavior Driven Developm3

Construct OntologiesUse: Semaphore 1

Build Data Models & Viz.Use: Spotfire, SQL, Excel 2

BACKUP

Artifacts1. Recipes2. Marker Pen 3. Chocolates