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Business Analytics

 Business Analytics : October 17th & 18th, 2011 - Online Registration

Topic :  

Applied Business analytics 
Location :    Paris

Target audience :

 

 

 

  

Designers and developers of analytics systems for any vertical (e.g. healthcare, finance and accounting, human resource, customer support, transportation) who work within any business organizations and BPO companies around the world.
University students and teachers, especially those in business schools, who are studying and teaching in the field of analytics
 

Instructors :

 

  

Dr. Subrata Das 

Machine Analytics : Founder & President

Dr. Kamel Mekhnacha

Probayes : Founder and CTO

Description :

 

 

 

 

 

 

  

Traditional business analytics have so far focused mostly on descriptive analyses of historical data using a myriad of sound statistical techniques. Numerical statistical techniques can be augmented/enriched with techniques from symbolic artificial intelligence (AI), machine learning (ML)/data mining and control theory for enhance descriptive, predictive, and prescriptive (a.k.a. decision support) analytics.


The unique nature of this training course is its coverage of both traditional probabilistic/statistical and cutting-edge AI/ML-based approaches to descriptive and predictive analytics and associated decision support.

For more information, please click here

Inscription :

 

 

  

To register, 2 options :
     

 

Detailed program

  • Lesson 1 : Introduction

Descriptive, predictive and prescriptive analytics, Big players and market size, State-of-the-art in statistics and beyond, Candidate architectures.

  • Lesson 2 : Statistics for Descriptive and Predictive Analytics

Descriptive statistics (distributions, central tendency, dispersions), Inferential statistics (generalization, test hypothesis, estimate, prediction or decision), Dependence methods (decision tress, CART/CHAID, linear and logistics regressions, auto-regression, factor analysis, survival analysis), Interdependence methods (hierarchical and k-means clustering).

  • Lesson 3 : Analytics Problem Modeling in Symbolic Artificial Intelligence

Approaches to handling uncertainty, Deductive, inductive and abductive reasoning, Ontology and knowledge representation, Rule-based system, Bayesian Belief Networks.

  • Lesson 4 : Machine Learning/Data Mining for Descriptive and Predictive Analytics

Generative vs. discriminative models, Supervised, unsupervised and semi-supervised learning, Decision trees (C4.5), Naïve Bayesian Classifier, Neural networks, Singular Value Decomposition, Latent Semantic Analysis, Support Vector Machine, Bagging and Boosting.

  • Lesson 5 : Time-Series Modeling for Predictive Analytics

ARMA/ARIMA, ARCH/GARCH, Hidden Markov Models, Dynamic Bayesian Networks, Kalman filtering and extensions, Particle filtering.

  • Lesson 6 : Prescriptive Analytics and Decision support

Test hypothesis, Expected Utility Theory, Influence diagrams, Symbolic argumentation, Reinforcement Learning Markov Decision Process.

  • Lesson 7 : Case Studies

Churn detection in banking, Fraud management for credit card companies.

 

 

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