datascience101

1. Data Science
-by Sujeet

2. Data
Optimization Prediction
Mathematics, Statistics, Algorithms
Quantitative
(Forecasting)
Qualitative
(Classification)
Languages: R, SAS, Python etc.

3. Big Data
Storage Problem, Processing Problem
Optimization Prediction
Mathematics, Statistics, Algorithms
Quantitative
(Forecasting)
Qualitative
(Classification)
Languages: R, SAS, Python etc.

4. Data
Optimization Prediction
Mathematics, Statistics, Algorithms
Quantitative
(Forecasting)
Qualitative
(Classification)
Hadoop Architecture, Map Reduce for processing
Languages: R, SAS, Python etc.

5. Real Time Bidding
- A Real Problem

6. Real Time Bidding
DSP 1
DSP 2
DSP N
A
B
C
D
E
F
G
H
I
SSP
Advertisers Demand Side
Platform
Supply Side
Platform
Webpage
Viewers
Bid Request
Bid Response

7. Request & Response
Requset:
user information as url
For example : OS, Location, Gender, Age, Language, browser..etc.
Response:
Advertisement creative url, Bid Price

8. Advertisers Input Data
• Target Segment
• Budget
• Flight Period(Campaign Period)
• Optimization goals and directives

9. Advertiser’s Goal
• Minimize Cost per Impression
• Minimize Cost per Click
• Minimize Cost per View

10. Advertiser’s Optimization Directives
• Even Budget spent
• Fast Budget spent
• Aggressive Budget spent

11. Problem Statement
What is the number of bids an advertiser should put at certain price in
a certain part of the day so that it achieves its goal while respecting its
budget constraints.

12. Advertiser’s Expectation
• An advertiser wants to know what is the average bidding price going
on before bidding.
• An advertiser wants to know which are the favourable parts of a day
where he should bid.

13. Certain Definition
• CPM(cost per 1000 Impression) = Budget*1000/ Impression
• CTR( Click Through Rate) = Clicks/Impression
• Win rate = Number of Wins/ Number of Bids

14. Thank You