DATA SCIENCE Lesson 4 Data Science Predictive Method Parsing Process Topic Modeling conference Jean-Antoine Moreau

PPrreeddiiccttiivvee MMeetthhoodd –– AAllggoorriitthhmm
MMeetthhooddoollooggyy –– PPrroocceessss
DDaattaa PPrroocceessssiinngg
DDaattaa MMiinniinngg
GGooaallss
Conference - Study : Jean-Antoine Moreau (Engineer - Lecturer)
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CCoonntteenntt AAnnaallyyssiiss
NNaattuurraall LLaanngguuaaggee PPrroocceessssiinngg
TTooppiicc MMooddeelliinngg
PPaarrssiinngg PPrroocceessss
DDaattaa PPrroodduucctt

NNaasshh eeqquuiilliibbrriiuumm uussee ttoo tthhee ssppeecciiffiiccaattiioonn
SScchheelllliinngg MMooddeell
MMaacchhiinnee lleeaarrnniinngg

DDaattaa sscciieennccee
UUssiinngg iinn BBuussiinneessss
uussee pprroocceessss iinn tthhee ccoommppaannyy

DDAATTAA SSCCIIEENNCCEE
LLeeccttuurree 44

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DDAATTAA SSCCIIEENNCCEE -- LL44
“Nothing in life is to be feared, it is only to be understood.
Now is the time to understand more, so that we may fear
less.”
JAM 6 / 218
Contact http://www.jean-antoine-moreau.fr.nf
Marie Curie

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JAM 7 / 218
PPrreeddiiccttiivvee aannaallyyttiiccss

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Measure the probability that event occurs
JAM 8 / 218
Predictive analysis
Retrieve a large amount of data
To extract statistical purposes

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JAM 9 / 218
Predictive Analysis
Process

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JAM 10 /
Predictive Analysis
Decision Tree

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Using the Graph Theory to define a structure
JAM 11 /
Predictive Analysis

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JAM 12 /
predict consumer behavior
predict investor behavior
predict the behavior of voters

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JAM 13 /
adaptation of means
the results of predictive analytics

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JAM 14 /
• track financial trends;
• track social trends;
• track economic trends.

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individual intelligence collective intelligence
JAM 15 /
algorithms
modeling

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JAM 16 /
New algorithms
are
Method

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JAM 17 /
predictive roles

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JAM 18 /
Data
• Social data;
• Posts;
• Comments;
• Behavioral data;
• Geographic data;
• Environmental data.

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 PredictiveDB is a predictive database, an online RDBMS to
learn, classify or predict from SQL data without knowledge in
Machine Learning or Natural Language/
JAM 19 /
Exemple
• http://blog.predictivedb.com/

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constructing a predictive model
JAM 20 /
data mining
Input | Output > Association

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environmental description Property that we want
to predict or determine.
JAM 21 /
Association
Input Ouput

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JAM 22 /
Semantic Approach
Semantic Analysis
Machine Learning

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• uses aggregated Google search data to estimate flu activity.
JAM 23 /
example
Google Flu Trends :
• Explore flu trends around the world
http://www.google.org/flutrends/

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Uncertainty Management Strategy
JAM 24 /
Predictive Device

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JAM 25 /
proactive assessment
of the behaviors.

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participatory assessment of behavior
statistical body
of the individual
(behaviors that could happen,
JAM 26 /
physical body
of the individual
possible future)

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JAM 27 /
Behavioral detection
facial expression
motion recognition
Patterns
behavior patterns

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JAM 28 /
Structures of behavior
Algorithms
analysis of mass data
use of profiling framework

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JAM 29 /
behavior
using the graph theory
initial behavior

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Using a Finite State Machine and a Behavior Tree
JAM 30 /

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JAM 31 /
definition of profiling framework
characteristics
profiling database
predictive behavior
psychological profile
predictive choices

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use the asymmetry of the information
JAM 32 /

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JAM 33 /
 topological study
 allocation study

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JAM 34 /
Spaces of studies

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JAM 35 /
BBiigg DDaattaa
DDaattaa SScciieennccee
Creation of data product
DDaattaa PPrroodduucctt

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JAM 36 /
Model behavior
error correction
Feedback model

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JAM 37 /
predictive analysis
predictive logic

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mathematical models of conflict and cooperation
between intelligent rational decision-makers
JAM 38 /
GGaammee TThheeoorryy
(John von Neumann, Oskar Morgenstern)
study of strategic decision making

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predictive assumptions about future events
JAM 39 /
Game Theory
(John von Neumann, Oskar Morgenstern)
analysis of present and past facts

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JAM 40 /
Game Theory;
(John von Neumann, Oskar Morgenstern);
 NNaasshh eeqquuiilliibbrriiuumm::
Simply reveal each person's strategy to all players.
 The Nash equilibrium existes if no players change their
strategy, ...

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JAM 41 /
Data
Pr
operti
es
behavior predictive models

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JAM 42 /
improvement
Data
Pr
operti
es
behavior predictive models

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JAM 43 /
predictive mode
opportunity risk
method
algorithm

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JAM 44 /
Data
model
method
• Opportunity
• Risk

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JAM 45 /
method
logical analysis
relationship
behavior issue
condition
factor

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to estimate the probability of a behavior
stimulus scenario
JAM 46 /
Analyze past performance
simulate human behavior

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a little break, to ask questions.
“ Whosoever desires constant success must change his
conduct with the times.”
Niccolò Machiavelli
JAM 47 /

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Descriptive models describe the relationships between data.
JAM 48 /
DDeessccrriippttiivvee mmooddeell

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Descriptive models describe the relationships between
JAM 49 /
data and information

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Descriptive modeling is a mathematical process that
describes real-world events and the relationships
between factors responsible for them.
JAM 50 /

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Descriptive models do not order the objects.
JAM 51 /

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JAM 52 /
mathematical modeling, the process

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JAM 53 /
Software / method > modeling

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JAM 54 /
for DDaattaa MMiinniinngg

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Process of preparing data for the bbuussiinneessss mmooddeelliinngg
JAM 55 /

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the relationship between elements of a decision
JAM 56 /
decision models describe

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development of a logical / decision method
data relation result / outcome
JAM 57 /
decision model
method

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JAM 58 /
decision models
examples
• Medical: help system for clinical decision;
• Financial: recovery system;
• Risk management system;
• Transportation: driving assistance system.

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JAM 59 /
Statistical techniques
• regression model:
• linear regression;
• autoregression;
• logistic regression;
• multinomial logistic regression.

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JAM 60 /
Graphical decision model

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JAM 61 /
Objective / Profit

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JAM 62 /
Business

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• In statistics, a probit model is a type of
regression where the dependent variable can
only take two values,
– for example married or not married .
JAM 63 /
probit model

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creation of mathematical equation
models to represent the interactions between
the different variables considered.
JAM 64 /
regression model

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to study, define, test, the representation of
interactions between the variables considered.
JAM 65 /
regression model
method

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JAM 66 /
dependent variable
response variables
relationship
set of independent
variables
set of predictors
Equation
(adjusted and optimized)

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The Gauss-Markov hypothesis must be verified.
JAM 67 /

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• Total power of prediction of the dependent
variable explained by the variation of the
independent variables.
JAM 68 /
Test the model

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autoregressive integrated moving average (ARIMA) model
JAM 69 /
Autoregressive model
autoregressive moving average (ARMA) model.

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JAM 70 /
DDiissccrreettee cchhooiiccee mmooddeellss
have become an essential tool in
modeling individual behavior

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JAM 71 /
The different kinds of regression
• Linear regression;
• Multiple Linear Regression;
• Polynomial regression;
• Logistic regression;
• Generalized linear model;
• Nonparametric regression;
• Multiple regression models postulated and not postulated;
• Circular regression;
• Elliptical regression;
• Local regression.

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Method transforming information on the
binary dependent variable in an estimated
unlimited continuous variable using a
multivariate normal model analysis.
JAM 72 /
Logistic regression
Logistic model

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Whenever a relationship within or between data
items can be expressed as a statistical model with
parameters to be estimated from a sample, the
Wald test can be used to test the true value of the
parameter based on the sample estimate.
JAM 73 /
TThhee WWaalldd TTeesstt
is a parametric statistical test
(statistician Abraham Wald)

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JAM 74 /
TThhee WWaalldd TTeesstt
LLiikkeelliihhoooodd rraattiioo tteesstt
MMeetthhoodd ooff lleeaasstt ssqquuaarreess
ssttaattiissttiiccaall eevvaalluuaattiioonn

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• In the probit model, the inverse standard normal
distribution of the probability is modeled as a
linear combination of the predictors.
JAM 75 /
Probit regression
• also called a probit model, is used to model
dichotomous or binary outcome variables.

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– R (R software - RapidMiner is a powerful visual tool for data
mining, machine learning, and predictive analytics);
– Weka (Waikato Environment for Knowledge Analysis)
JAM 76 /
PPrreeddiiccttiivvee aannaallyyttiiccss ttoooollss
 Commercial software:
– SAS;
– SPSS (IBM);
– STATISTICA (statoff);
 Free software:
• machine learning algorithms for data mining tasks.

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“A thinker sees his own actions as experiments and questions - as
attempts
to find out something. Success and failure are for him answers above all.”
JAM 77 /
Friedrich Nietzsche

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JAM 78 /
PPaarrssiinngg

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tthhee pprroocceessss ooff aannaallyyzziinngg aa ssttrriinngg ooff ssyymmbboollss
JAM 79 /
PPaarrssiinngg
ssyynnttaaccttiicc aannaallyyssiiss

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JAM 80 /
ttaaxxoonnoommyy aallggoorriitthhmm
tthhee ttaaxxoonnoommyy lleeaarrnniinngg aallggoorriitthhmm

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JAM 81 /
taxonomy algorithm
• Resolves issue:
– Recursive parser;
– Predictive parser;
– Predictive recursive parser.

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JAM 82 /
pprreeddiiccttiivvee ppaarrssiinngg

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JAM 83 /
predictive parsing
The goal of predictive parsing
is to construct a top-down parser

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JAM 84 /
• Parse Table entries;
• Parse Table;
• Predictive parsing;
• Assignment.

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JAM 85 /
PPrreeddiiccttiivvee ppaarrssiinngg aallggoorriitthhmm
• Use :
– The parse table;
• An input buffer containing a sequence of tokens;
• A stack of grammar symbols.

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JAM 86 /
PPrreeddiiccttiivvee ppaarrssiinngg
• Example :
Parse the string (id+num)*id

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JAM 87 /
Predictive parsing
Process and use

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• Prédictive parcing, the front-end (exemple –
compiler):
– Parsing is the core of the front end of a compiler.
– The predictive recursive-descent parsing technology
is most widely used in a traditional compiler design.
JAM 88 /

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Front end
Source code IR Machine code
JAM 89 /
Front end Back end
error management

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Source code tokens IR
Source code Scanner
 lexical analysis, preprocessing, parsing, semantic analysis (Syntax-directed
translation), code generation, and code optimization.
JAM 90 /
The Front-End-Scanner
Errors management

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JAM 91 /
UML modelisation
an example

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JAM 92 /
The Front-End-Scanner
Source code tokens IR
scanner Parser
Error management

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– Checks the stream of words and their parts of speech
(product by the scanner) for grammatical correctors;
– Determines if the input is syndically well formed;
– Guides checking at deeper levels than syntax;
– Build an IR representative of the code.
JAM 93 /
• Parser (other example) :
• ((IIntermediate / IIndependent RRepresentation)

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• The parse table;
• An Input buffer containing a sequence of tokens;
• A stack of grammar symbols.
JAM 94 /
Predictive Parsing Algorithm

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JAM 95 /
• Roadmap (where we are?)
– Specifying tokens;
– Specifying syntax;
• Predictive top-down parsing :
– First, Follow …
– The condition;
– Table-driven parsers.

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“Do what you can, with what you have, where you are.”
Theodore Roosevelt
JAM 96 /

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PPaarrssiinngg TTeecchhnniiqquueess
JAM 97 /

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JAM 98 /
PPaarrssiinngg TTrreeee
first example

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JAM 99 /
second example

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JAM 100 /
Using the Graph Theory

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– Start at the root of the parse tree and grow
toward leaves;
– Pick a production & try to mach the input;
JAM 101 /
• Top Down parser:

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• Bottom-up parsers :
• Start of the leaves and grow toward root;
• As input is consumed, encode, possibilities in a
internal state;
• Start in a state valid for legal first tokens;
• Bottom-up parsers handle a large class of grammars.
JAM 102 /
– Operator procedure

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JAM 103 /
• Top-down parsers:
– Recursive descend.

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SSiimmppllee mmeetthhoodd
 Construct the root of the parse tree;
 Repeat until the fringe of the parse tree matches
the input string;
 Select the product of each symbol and construct
the child;
 Find each node to be expanded.
JAM 104 /
• Top-down parsing algorithm :

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JAM 105 /
EExxaammpplleess ooff uussiinngg

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JAM 106 /
Parallel Parsing Process

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PPaarrssiinngg AAnnaallyyssiiss PPrroocceessss
Exemple de modélisation: le plan du gouvernement pour augmenter l’impôt sur le revenu.
JAM 107 /
Example of modelisation : the government's plan to increase the income tax.

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JAM 108 /
ppaatttteerrnn rreeccooggnniittiioonn
in this case the recognition of the shape of the furniture
component

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JAM 109 /
EExxaammppllee ooff TTrreeee
TThhee HHTTMMLL ppaarrssee ssttrruuccttuurree

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JAM 110 /
PPaarrssiinngg AAllggoorriitthhmm

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JAM 111 /
PPaarrssiinngg
aannaallyyssiiss tthhee ddeeccoommppoossiittiioonn ooff tthhee pprroocceessss

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JAM 112 /
pphhrraassee ppaarrssiinngg
PPrroocceessss // AAllggoorriitthhmm

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JAM 113 /
PPhhrraassee PPaarrssiinngg
EExxpprreessssiioonn SSttrreeaammiinngg
AAllggoorriitthhmm

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HHooww ttoo ddeeffiinnee tthhee ssppaannnniinngg ttrreeee ppaarrssiinngg mmiinniimmuumm ??
JAM 114 /
UUssiinngg tthhee GGrraapphhss TThheeoorryy

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PPaarrssiinngg ttoo ffiinndd tthhee ssttrruuccttuurree ooff aa sseenntteennccee
JAM 115 /

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AAuuddiioo :: FFoouurriieerr uussee ttoo ddeeffiinnee aa ppaarrssiinngg ttrreeee..
JAM 116 /

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JAM 117 /
PPaarrssiinngg ccllaassssiiffiiccaattiioonn

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JAM 118 /
PPaarrssiinngg ccllaassssiiffiiccaattiioonn

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« If I am walking with two other men, each of them will serve as my teacher. I will pick out the good points of the one and
imitate them, and the bad points of the other and correct them in myself. »
JAM 119 /
Confucius

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JAM 120 /
PPrreeddiiccttiivvee ppaarrssiinngg

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JAM 121 /
Predictive parser

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JAM 122 /

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JAM 123 /

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JAM 124 /
The roles

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Top-down parsing while scanning the input from left on right
JAM 125 /
Example :

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JAM 126 /
Example: Using PLC algorithm.
decoder
signal processing data processing
PLC:
• Packet Loss Concealment is a technique used to mask the effects of lost or discarded packets.

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JAM 127 /
parsing continued
parsing continued algorithm

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JAM 128 /
EExxaammppllee :: LLaanngguuaaggee ttrraannssllaattoorr

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JAM 129 /
SSeenntteennccee aannaallyyssiiss

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JAM 130 /
PPaarrssiinngg AAllggoorriitthhmm
ddyynnaammiicc pprrooggrraammmmiinngg

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JAM 131 /
the movement of the hand
using tacking

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JAM 132 /
BBeehhaavviioorr AAnnaallyyssiiss aanndd PPaarrssiinngg

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JAM 133 /
Predictive parser
• Top-down parser;
• Implementations:
– Recursive-descent parser;
– Table-driven parser.

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• Build parse tree from the tops (start symbol)
down to leaves (terminals);
JAM 134 /
Top Down Parsing

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 A grammar specifications may contain directives that are used to customize the
generated parser or declare how some symbols will be used..
JAM 135 /
Predictive parser
• Predictive parser : top-down parser;
• Implementations:
– Recursive – descent parser:
– Each non-terminal parsed by a procedure;
– Call other procedure to parse sub-nonterminals recursively;
• Table-driven parser:
– Push-down automata;
– Generate by a tool from a grammar specification.

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JAM 136 /
PPaarrssiinngg pprroocceessss
EExxaammppllee:: ffoorr aa sseenntteennccee ssttrruuccttuurree
Step (1)

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JAM 137 /
PPaarrssiinngg pprroocceessss
EExxaammppllee:: ffoorr aa sseenntteennccee ssttrruuccttuurree
Step (n+1)

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JAM 138 /
Defining the predictive need
Algorithm
Method
Tools
Programming

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• Construct parse tree for input;
• Bottom-up parsing is more general than top-down
JAM 139 /
Bottom-up parsing
parsing and just as efficient -
generally preferred in practice.

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JAM 140 /
LL(k) parsing
LR(k) parsing
• LL(k) parsing: Leftmost derivation
• LR(k) parsing :
– LLeft-to-RRight scan of input,
• rightmost derivation;
– K tokens of a look ahead.

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JAM 141 /
Content analysis
Topic modeling

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not final, failure is not fatal: it is the courage to continue that Winston JAM 142 /
a break, you can ask
your questions.

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 Search words that regularly come together in a document;
 Gather these words in a "theme« (area);
 Measure the frequency of each themes in each documents.
JAM 143 /
Topic modeling

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JAM 144 /
Topic modeling
Process's example

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JAM 145 /
Topic modeling
Machine learning

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JAM 146 /
Machine learning

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JAM 147 /
Machine learning
Artificial intelligence

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JAM 148 /
Artificial intelligence
Modifiable Algorithm

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statistics analysis multidisciplinary modeling
JAM 149 /
Topic Modeling

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JAM 150 /
Data Science
Data Mining
Predictive analytical processing
Machine Learning

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JAM 151 /
Natural Language Processing

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JAM 152 /
Data Science
Natural Language Processing
Parsing Scanning

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Machine learning Natural language
JAM 153 /
Process Topic
Generative Model

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– There are many more complex kinds of language models, such as bigram
language models , which condition on the previous term.
JAM 154 /
Unigram model
• Types of language models
• How do we build probabilities over sequences of terms?
– The simplest form of language model simply throws away all
conditioning context, and estimates each term independently.

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• Probabilistic Latent’ Semantic Analysis :
– is a novel statistical technique for the analysis
of two--mode and co-occurrence data, which
has applications in information retrieval and
filtering, natural language processing, machine
learning from text, and in related areas.
JAM 155 /

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• Latent Dirichlet allocation (LDA), a generative
probabilistic model for collections of discrete data such as
text corpora.
• LDA is a three-level hierarchical Bayesian model, in
which each item of a collection is modeled as a finite
mixture over an underlying set of topics. Each topic is
inturn, modeled as an infinite mixture over an underlying
set of topic probabilities.
JAM 157 /
LDA Model

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JAM 159 /
three-level hierarchical Bayesian model

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JAM 160 /
Example: the case of a image

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JAM 161 /
Example: a scene of life

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JAM 162 /
Example: a local Network

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a topic model for text or other discrete data that models
correlation between the occurrence of, different topics in a
document.
For the CTM model, you can use the Value Estimation Model
JAM 163 /
CTM Model
• The Correlated Topic Model (CTM):

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Model of interacting decision-makers
JAM 164 /
Game Theory
Strategic Game

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JAM 165 /
Game Theory
Nash Equilibrium
Strategic Game
Model of interacting decision-makers

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• A sseett ooff ppllaayyeerrss (actors);
• For each player, a sseett ooff aaccttiioonnss;
• For each player, preferences over the sseett ooff
aaccttiioonnss pprrooffiilleess.
JAM 166 /
SSttrraatteeggiicc ggaammee
Consist of :

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• As in the model of rational choice by a
single decision-maker;
• Time is absent from the model.
JAM 167 /

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• The idea is that each player chooses her action once and for all,
– and the players choose their actions « simultaneously » in the sense that
no player is informed, when the action is chosen by any other player.
• However an action may involve activities that extend over time, and
• But the fact that time is absent from the complications, that may arise,
if a player is a allowed to change her plan as events unfold : we
assume that actions an chosen once and for all.
JAM 168 /
may take into account an unlimited number of contingencies.

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JAM 169 /
SSttrraatteeggyy ggaammee
For each situation define :
define
Players
(actors)
Actions preferences
Specifications

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JAM 170 /
Specifications Matrix
Players
(actors)
Players
(actors)
Action 1
Action 2
Action 1 Action 2
Preference (Action 1, Action 1) Preference (Action 1, Action 2)
Preference (Action 1, Action
2)
Preference (Action 2, Action 2)

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JAM 171 /
Matrix Use case
Players
(actors)
Players
(actors)
Action 1
Action 2
Action 1 Action 2
2)
Use case / set

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Use case Specification
JAM 172 /
Players
(actors)
Players
(actors)
Action 1
Action 2
Action 1 Action 2
2)
Use case / set
Specification / set

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 What actions will be chosen by the players (actors) in a
strategic game ?
 We wish to assume as in the theory a rational decision-maker,
that each player chooses the best available action.
 In a game, the best actions for any given player depends,
in general; on the other players’ actions.
JAM 177 /

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• We assume that each player has experience playing the
game, we assume that he view each play of the game in
isolation;
• He does not become familiar with the behavior of specific
opponents.
JAM 178 /

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• It is helpful to think of the following idealizing
circumstances:
– For each player in the game there is a population of the
game, there is a population of many decision-makers
who many, on many occasions, take that player's role;
– Thus each player engages in the game repeatedly,
against ever-varying opponents;
– Her experience leads her to beliefs about the actions of
"typical" opponents.
JAM 179 /

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NNaasshh eeqquuiilliibbrriiuumm
A NNaasshh eeqquuiilliibbrriiuumm is an aaccttiioonn pprrooffiill (a) with
the property that no player (I) can do better by
choosing an action different from (a), given that
every other player (j) adheres to (a).
A Nash equilibrium ccoorrrreessppoonndd ttoo aa sstteeaaddyy ssttaattee.
JAM 180 /

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The second component of the theory of Nash
equilibrium, that the player's beliefs about each
other's actions are correct-implies, in particular,
that two players' belief about a third player's
action are the same.
For this reason, the condition is sometimes said
that the players' "expectations are coordinated".
JAM 181 /

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JAM 182 /
your questions.
« What we play is life. »
Louis Armstrong
of course

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 a Nash equilibrium designed to model a
steady state among experienced players.
JAM 183 /

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How to know? How to understand ?
 an alternative approach to understanding players'
actions in strategic games assumes that the
players know each others' preferences, and
considers what each player can deduce about the
other players' actions from their inclination or
tastes and their knowledge of each other's
rationality.
JAM 184 /

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JAM 185 /
Specification
define de requirement
use a Nash equilibrium

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Use the Nash equilibrium Specification
JAM 186 /
Players
(actors)
Players
(actors)
Action 1
Action 2
Action 1 Action 2
(1,1) Preference (Action 1, Action 2)
2)
to
Use case / set
Specification / set
The Nash
Equilibrium

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“Patience is the companion of wisdom. »
JAM 187 /
your questions.
Saint Augustine

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JAM 188 /
BBuussiinneessss
UUssiinngg

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JAM 189 /
How to do ?
How to use ?

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JAM 190 /
• Data crunchers;
• Data modeling;
• Data modeler;
• Data distillers;

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Computer Science
JAM 191 /
• Data crunchers;
• Data modeling;
• Data modeler;

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Computer science
Business Analysis
JAM 192 /
• Data crunchers;
• Data modeling;
• Data modeler;

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JAM 193 /
Tools for
• Human Resources
• Financial
• Prospective / Perspective
• Marketing

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Get new clients
Get new markets proactivity
JAM 194 /
Tools for
• Human Resources
• Financial
• Marketing

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Get new clients
Get new markets proactivity
WWiinnnniinngg tthhee iinntteerrnnaattiioonnaall ccoommppeettiittiioonn
JAM 195 /
Tools for
• Human Resources
• Financial
• Marketing

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JAM 196 /
RRiisskk MMaannaaggeemmeenntt
• Performance management;
• Cost control;
• Risk Analysis;
• Fraud detection;
• …

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JAM 197 /
OOppeerraattiioonnss aanndd pprroodduuccttiioonn
• Supply chain optimisation;
• Resource allocation;
• Human Resource analysis;
• Asset Management;
• Customer Relationship’ Management;
• Marketing approach;
• ...

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JAM 198 /
SSeerrvviicceess
• Data science consulting;
• Business analytics;
• Business intelligence;
• Data driven applications;
• …

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JAM 199 /
Data
• Web browsing;
• Social networks;
• Commercial transactions;
• Financial transaction;
• Industrial sensors;
• Smart grids;
• Geolocation;
• Sensors;
• Medical equipment;
• Competitive intelligence;
• Public data;
• Pooled data;
• Open data;
• Data offered.

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JAM 200 /
Parallel programming model
Distributed File System
Information management tools
MapReduce
Hadoop
HDFS
Hadoop
• NoSQL database;
• Data mining;
• Machine Learning;
• Query Tool;

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JAM 201 /
OOppeerraattiioonnaall aarreeaass of use:
• Analytical;
• Action;
• Decision;
• Information;
• Data;
• Financial.

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JAM 202 /
UUssee CCaassee
• Industrial asset management;
• Target marketing;
• Price transparency;
• Remote diagnostics;
• Carrier mobility;
• Production management;
• Risk management.

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Data science, use process in the company
measure understand predict decide acting Innovate
JAM 203 /

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external data
composite data
JAM 204 /
Data
data segment
data on individuals
internal data transversal data

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JAM 205 /
PPrroocceessss
1. Formalization of the issues;
2. Data collection;
3. Formalization of the issues;
4. Model development;
5. Actions of automation;
6. Decisions;
7. Commitment decisions;
8. Implementation.

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JAM 206 /
Logical architecture
Transactional
data
Machine Data
Text - Image - Video
Data
Business Application
Data Lab
Business Intelligence
Actors - Players
Clients
Data Scientist
Business Manager
Others …

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JAM 207 /
DDaattaa SScciieennttiisstt
jjoobb aanndd rroolleess ffoorr tthhee bbuussiinneessss
• Correlation analysis;
• Causal study;
• Predictive modeling;
• Influence of measurement;
• Detecting Patterns.

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• Strategic planning;
• Tactical analysis;
• Operational management;
• Study the customers behavior or set of clients.
JAM 208 /
BBuussiinneessss MMaannaaggeerr

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JAM 209 /
SSkkiillllss
• Statistics;
• Machine Learning;
• Marketing;
• Network;
• Operations;
• Architecture;
• Algorithms;
• Application;
• Computer science application.

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Thomas Crombie Schelling’s Model
JAM 210 /

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 Improve our understanding of the mechanisms
of conflict and cooperation, through the analysis
of game theory. "
JAM 211 /

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• The game is played on a checkerboard, which is a city;
• The checkerboard is filled of dimes and nickel representing two differents types of agents;
• The different types of agents can be thought of: different sexes, different consumers or others ...
• Each agent evaluates its current position based on a " happiness rule ", which depends on
• Unhappy agents are allowed to switch places with the other (there are various ways);
• The game continues until all the agents are happy, then it represents the equilibrium outcome.
• Then all the agents found the place where they are happy. They are next to those who have the
JAM 212 /
Thomas Crombie Schelling’s Model,
Example :
adjacent squares (happiness rule, example : I love ice cream);
same “happiness rule” than them. Equilibrium is reached .

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example: common rules, consolidation area and area of influence.
Area ( b)
JAM 213 /
Rule (a)
Rule ( c ) Rule (b)

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JAM 214 /
 The checkerboard can be:
– A store chain;
– Business services;
– Financial services;
– International business;
– …

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 If the checkerboard is a concert hall.
 The outcome primarily depends on the happiness
rule. One example of a happiness rule would be "I
want all my neighbors are musicians".
Not surprisingly this rule give the outcome of
complete orchestra.
JAM 215 /

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• To implement this model, you only need
information about the individual preferences of
each of the players (actors), and a dichotomy for
defining distinct groups.
JAM 216 /

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• The model Schelling shows with a very
simple simulation, that a strong
differentiation may be the collective result
of individual decisions, which are not
intended to make this differentiation.
JAM 217 /

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• End of part of this lecture published on the
internet.
JAM 218 /

DATA SCIENCE Lesson 4 Data Science Predictive Method Parsing Process Topic Modeling conference Jean-Antoine Moreau

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DATA SCIENCE Lesson 4 Data Science Predictive Method Parsing Process Topic Modeling conference Jean-Antoine Moreau