5 credits
30.0 h
Q1
Teacher(s)
Fouss François;
Language
English
Prerequisites
/
Main themes
Nowadays, data are everywhere. For most organizations, potentially every area of its business, as well as every relationship related to its business, can now be quantified and recorded. Such amount of data led to the emergence of powerful methods for storing, processing, querying, and extracting useful information/knowledge from these data. This course will be focused on methods for data understanding, design, management, preparation, modeling, querying, and visualization, as a global means for the organization of making better decisions. As a central element in data analytics, modeling and methodology will play an important role in this course, including, e.g., data design for business intelligence analytics, predictive modeling, or fitting statistical models to data.
Content
In addition to the main topics of the course, i.e., introducing the main tasks in data analytics and appropriate methodolgy for analysing data, the scope of the course is broad and the instructor will certainly not be able to cover all of the material concerning data analytics in business. Depending of his background, interests and experience, he will focus on some specific techniques or skim through a broad range of methods and examples.
Potential covered topics are (but not limited to)(and should not overlap topics covered in the course of the Business Analytics major): business intelligence techniques, reporting, intelligence management, dimensionality reduction for data visualization, extracting recurrent patterns from data, cluster analysis, predictive modeling (supervised classification and regression methods), data analysis algorithms scaling to big data, etc. All these techniques must be illustrated through business applications.
Potential covered topics are (but not limited to)(and should not overlap topics covered in the course of the Business Analytics major): business intelligence techniques, reporting, intelligence management, dimensionality reduction for data visualization, extracting recurrent patterns from data, cluster analysis, predictive modeling (supervised classification and regression methods), data analysis algorithms scaling to big data, etc. All these techniques must be illustrated through business applications.
Teaching methods
Classical courses, student and expert talks, and case studies.
Bibliography
Sources potentielles :
Provost & Fawcett (2013) 'Data science for business'. O'Reilly.
Sherman (2014) 'Business intelligence guidebook: from data integration to analytics'. Morgan Kaufmann.
Efraim, Sharda & Delen (2010) 'Decision support and business intelligence Systems'. Pearson.
Leskovec, Rajaraman & Ullman (2014) 'Mining of massive datasets, 2nd ed'. Cambridge University Press.
Kelleher, Mac Namee & D'Arcy (2015) 'Fundamentals of machine learning for predictive data analytics. MIT Press.
Hastie, Tibshirani & Friedman (2009), "The elements of statistical learning, 2nd ed". Springer-Verlag.
Izenman (2008), 'Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer.
Bellanger & Tomassone (2014), "Exploration de données et méthodes statistiques : data analysis & data mining avec le Logiciel R". Ellipses.
Provost & Fawcett (2013) 'Data science for business'. O'Reilly.
Sherman (2014) 'Business intelligence guidebook: from data integration to analytics'. Morgan Kaufmann.
Efraim, Sharda & Delen (2010) 'Decision support and business intelligence Systems'. Pearson.
Leskovec, Rajaraman & Ullman (2014) 'Mining of massive datasets, 2nd ed'. Cambridge University Press.
Kelleher, Mac Namee & D'Arcy (2015) 'Fundamentals of machine learning for predictive data analytics. MIT Press.
Hastie, Tibshirani & Friedman (2009), "The elements of statistical learning, 2nd ed". Springer-Verlag.
Izenman (2008), 'Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer.
Bellanger & Tomassone (2014), "Exploration de données et méthodes statistiques : data analysis & data mining avec le Logiciel R". Ellipses.
Faculty or entity
CLSM
