Data Analytics Intro Session 1, 2013

Introduction to Data Analytics Session 1 Florent Renucci, Data Scientist, Amadeus North America

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• I – What is it ?

• II – What’s in it for us ?

• III – How to succeed ?

• IV – How to fail ?

• V – Does it really work ?

• VI – Does it really create value ?

Introduction to Data Analytics

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“Field of study that gives the computer the ability to learn without being explicitly programmed.”

- Arthur Samuel, 1959

I – You said Data Science ?

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input X

function f output Y = f(X)

input X

output Y Model f such as Y = f(X) + ε

Computer Sciences

Data Sciences

I – Building a model “Numbers have an important story to tell. They rely on us to give them a voice.”

– Stephen Few

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I – Using a model “Numbers have an important story to tell. They rely on us to give them a voice.”

– Stephen Few

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I – What is it ?

• Feed a metaheuristic with the features and the explained phenomenon. The output is the link between them.

• Use the link to emulate new decisions.

In a nutshell








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II – What ‘s in it for us? “Those who ignore Statistics are condemned to reinvent it.”

- Brad Efron

Examples

Concept

Goal

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- Brad Efron

Examples

Concept

Goal Make predictions

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- Brad Efron

Examples

Classification Regression

Concept


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- Brad Efron

Examples

• Spam detection

• biology/medicine

• fraud detection

• scoring (Google, Meetic)

• Weather prediction

• stock prediction


Concept


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- Brad Efron

Examples

• Spam detection


• fraud detection





Concept

Goal Make predictions Understand systems

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- Brad Efron

Examples

• Spam detection


• fraud detection





Clustering Concept


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- Brad Efron

Examples

• Spam detection


• fraud detection





Clustering Concept


• speech recognition

• e-marketing

• sentiment mining

• recommendation system : "you would also like" in Amazon

• rare event detection Obama’s campaign

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- Brad Efron

Examples

• Spam detection


• fraud detection





Clustering Concept

Goal Make predictions Optimize functions Understand systems


• e-marketing




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- Brad Efron

Examples

• Spam detection


• fraud detection





Clustering Discrete optimization

Reinforcement Learning Concept



• e-marketing




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- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing




• automatic investment on financial markets

• game playing

• Yield management

• Pavlov’s dog

• those funny things : https://www.youtube.com/watch?v=Lt-KLtkDlh8

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- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


? 18


- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


? 19


- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


? 20


- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


? 21


- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


? 22


- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


? 23


- Brad Efron

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


? 24








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27 Map (filter, clean, project)

Reduce (count)

Machine Learning (learn)

III – From Data to Knowledge : Typical workflow “You can have data without information, but you cannot have information without data.”

– Daniel Keys Moran

Business insight

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III – How to succeed ?

• Keep only the variables that are the most “linked” with the pattern, basing on knowledge about the observed phenomenon. Usually used in a MapReduce framework.

• Clean the data by deleting non-meaningful observations.

• Preprocessing the features to “make them talk”.

• Feed a metaheuristic with this data. The output is the model between them.

• Use the link to emulate new decisions.

In a nutshell








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IV – What is a good predictive model ?

Feature X

Predicted Variable Y

“Essentially, all models are wrong, but some are useful.”

- G. Box

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Feature X



- G. Box

Undersmoothing

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Feature X



- G. Box

Undersmoothing

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Feature X



- G. Box

Overfitting Undersmoothing

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Feature X



- G. Box

Overfitting Undersmoothing

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Feature X



- G. Box

Undersmoothing Good level of complexity Overfitting

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Feature X



- G. Box

Undersmoothing Good level of complexity Overfitting

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Feature X



- G. Box

Error rate : how to measure it ?

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IV – Choose the good metric ! “What gets measured, gets managed.”

- Peter Drucker

• 𝐑𝐌𝐒𝐄 = 𝐲 − 𝐲 𝟐𝐧𝐢=𝟏

• 𝐚𝐝𝐣𝐮𝐬𝐭𝐞𝐝 𝐑𝐌𝐒𝐄 = 𝐲 −𝐲 𝟐𝐧𝐢=𝟏

𝐧−𝐤

• 𝐀𝐈𝐂 = 𝟐 𝐤 − 𝐥𝐨𝐠 𝐋

• 𝐀𝐈𝐂 = 𝐤. 𝐥𝐧 𝐧 − 𝟐. 𝐥𝐧(𝐋)

…

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IV – Choose the good metric ! “What gets measured, gets managed.”

- Peter Drucker

• 𝐑𝐌𝐒𝐄 = 𝐲 − 𝐲 𝟐𝐧𝐢=𝟏

• 𝐚𝐝𝐣𝐮𝐬𝐭𝐞𝐝 𝐑𝐌𝐒𝐄 = 𝐲 −𝐲 𝟐𝐧𝐢=𝟏

𝐧−𝐤

• 𝐀𝐈𝐂 = 𝟐 𝐤 − 𝐥𝐨𝐠 𝐋

• 𝐀𝐈𝐂 = 𝐤. 𝐥𝐧 𝐧 − 𝟐. 𝐥𝐧(𝐋)

…

No Silver Bullet

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Learning set

Complexity

error

time

error

Test set

“If you do not know how to ask the right question, you discover nothing.”

- Deming


Accuracy Robustness

…








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II – What ‘s in it for us? “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”

- Geoffrey Moore

Examples

• Spam detection


• fraud detection









• e-marketing





• game playing


• Pavlov’s dog


II – What ‘s in it for us?

• Random Forest

• Naive Bayes classifier

• Support Vector Machine

• Artificial Neural Network • Bagging

• Time Series





• K-means

• Fuzzy clustering

• Artificial Neural Network

• DBSCAN

• Expectation-Maximisation

• Gradient Descent

• Max-flow min-cut

• Belief propagation

• Temporal difference learning

• Q-learning

Algos

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”

- Geoffrey Moore


• Random Forest




• Time Series





• K-means



• DBSCAN






• Q-learning

Algos


- Geoffrey Moore


• Random Forest




• Time Series





• K-means



• DBSCAN






• Q-learning

Algos

No Silver Bullet


- Geoffrey Moore


• Random Forest




• Time Series





• K-means



• DBSCAN






• Q-learning

Algos

No Free Lunch Theorem


- Geoffrey Moore

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V – Example 1 (see R simulation) Classification and Regression Tree

Color ?

x

y

z

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Color ?


x

y

z Color ?

up down

Green !

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x

y

z Color ?

up down

left right

Green ! Color ?

Purple ! Orange !


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α1 α2 α3 α𝑛

…

Prediction step What is the color/value of the output ?

Learning step Building n trees from n random subsets

Testing step Can I really trust this tree ?

Boosting step Smartly combining the trees to avoid overfitting

𝑌 = α𝑖

𝑛

𝑖=1

𝑦𝑖

…

Evaluation step How good are my predictions ?

error rate = ?

V – Example 1 (see R simulation) Random Forest

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V– Example 2 (see R simulation) Univariate Time-Series AutoRegression

• The process is entirely explained by its past values.

• The goal is to find the link between k successive values and the next one. “k” (the lag order) and the link have to be estimated.

• Each process (travel) is considered independently.

The idea

• 𝐘𝐭 = f Yt−k, … , Yt−1

• “k” : How many points are related to each other ?

• “f” : what was the underlying pattern ? what happened from (t − k) to (t − 1) ?

Definition

t

Predicted

Variable Yt

t t

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• 𝐘𝐭 = f Yt−k, … , Yt−1, 𝑍𝑡−𝑘′ , … , 𝑍𝑡−1, …

• Again “k”, “f”.

• “p”, “ k’ ” : What neighbors should I consider ? How “late” are they ?

Definition

Predicted

Variable Yt

t

• The process is entirely explained by its past values AND by the past values of its “neighbors”.

• The goal is to find the link between k successive values and the next ones. “k” (the lag order), the p neighbors, and the link have to be estimated.

• Processes (travels) are considered as correlated.

The idea

𝑍t, a "good" neighbor

V– Example 2 (see R simulation) Multivariate Time-Series AutoRegression








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• Random Forest




• Time Series





• K-means



• DBSCAN






• Q-learning

Algos

IV – Data Analytics “Data really powers everything that we do.”

– Jeff Weiner

• Random Forest




• Time Series





• K-means



• DBSCAN






• Q-learning

Algos


– Jeff Weiner

Business value ?

• Random Forest




• Time Series





• K-means



• DBSCAN






• Q-learning

Algos


– Jeff Weiner

Business value ?

Mathematical concept ?

• Random Forest




• Time Series





• K-means



• DBSCAN






• Q-learning

Algos

Implementation ?

Business value ?

Mathematical concept ?


– Jeff Weiner

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IV – Data Analytics “Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model ?”

- Steve Lohr

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- Steve Lohr

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- Steve Lohr

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- Steve Lohr

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IV – Data Analytics “Information is the oil of the 21st century, and Data Analytics is the combustion engine.”

- P. Sondergaard

Business

IT Data

Sciences

High tech

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IV – Data Analytics

Business

IT Data

Sciences

High tech Business Insights

“Information is the oil of the 21st century, and Data Analytics is the combustion engine.”

- P. Sondergaard

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Business

IT Data

Sciences


Data processing


- P. Sondergaard

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Business

IT Data

Sciences

High tech

Breakthrough Innovation

Business Insights

Data processing


- P. Sondergaard

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Business

IT Data

Sciences


Data processing

Business expertise.

Technical Expertise.

Mathematical expertise and links with academical research.

Big Data Meaningful Data, and relevant questions.

Investments in R&D.

Consistency with Core Activities.

Maturity of the industry.

Infrastructure.

Culture of innovation.

Requirements

Breakthrough Innovation


- P. Sondergaard

Thanks. Any questions ?

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“It’s easy to lie with statistics. It’s hard to tell the truth without it.”

- Andrejs Dunkels

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Annex A– Univariate Time-Series AutoRegression

∃ k ∈ ℕ, ∃ α1, … , αk ∈ ℝk such as ∶

Yt= α1Yt−1 + α2Yt−2+…+ αkYt−k+εt

i.e. : Yt= αiYt−iki=1 + εt

So 𝐘𝐭 = αiYt−iki=1 = Yt−1, … , Yt−k

T. α1, … , αk ≝𝐗𝐀

Definition

Penalization for non-parsimonious models :

• k, Αoptim = argmink,Yt

Yt − Yt 22+ 2k (Mallows, 73)

• Akaike Criterion (same with log) (Akaike, 73)

• Student test and forward-backward algorithm

Estimating 𝐤

𝐘𝐭 ≝ 𝐗𝐀 Then 𝐘𝐭 - 𝐘𝐭= 𝛆𝐭

Αoptim = argminYt Yt − Yt 2

2= argmin

AXA− Yt 2

2

first-order condition : 2 XTXΑoptim − 2YTX = 0

So 𝚨𝐨𝐩𝐭𝐢𝐦 = 𝐗𝐓𝐗−𝟏𝐘𝐓𝐗

Estimating 𝐀 (OLS Method)

• The process is entirely explained by its past values.

• The goal is to find the link between k successive values and the next one. “k” (the lag order) and the link have to be estimated.

• Each process (travel) is considered independently.

The idea

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∃ k ∈ ℕ, ∃ α1, … , αk ∈ ℝk∗𝐩 𝐯𝐞𝐜𝐭𝐨𝐫𝐬 ! such as ∶

Yt= α1. Yt−1 + α2. Yt−2+…+ αk. Yt−k+εt

i.e. : Yt= αi. Yt−iki=1 + εt

So 𝐘𝐭 ≝ 𝐗𝐀 (matrices !)

Definition

Penalization for non-parsimonious models :

• k, Αoptim = argmink,Yt

Yt − Yt 22+ 2k (Mallows, 73)

• Akaike Criterion (same with log) (Akaike, 73)

• Student test and forward-backward algorithm

Estimating 𝐤𝐦𝐚𝐱

• The process is entirely explained by its past values AND by the past values of its “neighbors”.

• The goal is to find the link between k successive values and the next ones. “k” (the lag order), the p neighbors, and the link have to be estimated.

• Processes (travels) are considered as correlated.

The idea

cross − correlation Yt0 , Yt0+i t (𝜏) = 𝑐𝑜𝑣 𝑌𝑡0𝑡 , 𝑌𝑡0+𝑖

𝑡−𝜏

p = argmaxi <kmax

(cross − correlation Yt0 , Yt0+i (0)(𝜏) > treshold)

ki = argmax𝜏∈ℕ

(cross − correlation Yt0 , Yt0+i 𝑡 (𝜏) > treshold)

Treshold : chosen empirically from a given grid.

Estimating 𝐤 𝐟𝐨𝐫 𝐞𝐚𝐜𝐡 𝐧𝐞𝐢𝐠𝐡𝐛𝐨𝐫

Annex B – Multivariate Time-Series AutoRegression

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Annex C – Gradient descent algorithm in a 3-dimensional space

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Data Analytics Intro Session 1, 2013

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