Course 2016-2017 a.y.

20538 - PREDICTIVE ANALYTICS FOR DATA DRIVEN DECISIONS MAKING


CLMG - M - IM - MM - AFC - CLEFIN-FINANCE - CLELI - ACME - DES-ESS - EMIT - GIO

Department of Decision Sciences

Course taught in English

Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-S/01) - M (6 credits - II sem. - OP  |  SECS-S/01) - IM (6 credits - II sem. - OP  |  SECS-S/01) - MM (6 credits - II sem. - OP  |  SECS-S/01) - AFC (6 credits - II sem. - OP  |  SECS-S/01) - CLEFIN-FINANCE (6 credits - II sem. - OP  |  SECS-S/01) - CLELI (6 credits - II sem. - OP  |  SECS-S/01) - ACME (6 credits - II sem. - OP  |  SECS-S/01) - DES-ESS (6 credits - II sem. - OP  |  12 credits SECS-S/01) - EMIT (6 credits - II sem. - OP  |  SECS-S/01) - GIO (6 credits - II sem. - OP  |  SECS-S/01)
Course Director:
LUCA MOLTENI

Classes: 31 (II sem.)
Instructors:
Class 31: LUCA MOLTENI



Course Objectives

The course provides an overview of the integration and analysis process of structured and unstructured data (Big Data), focusing on the most important applications of predictive analytics in managerial issues.
The goal of the course is to improve the student's skills to manage and to take advantages of the huge availability of data nowadays produced by a great variety of sources. The contents of the course covers both technical aspects of data analytics and more interpretation related topics (how to translate the analytical outputs into meaningful business insights).

Course Content Summary

  • Main characteristics of Big Data
  • Introduction to Hadoop platform
  • Models and statistical techniques applied to Big Data:
    • Linear and logistic regression
    • Regression and classification trees
    • Factor analysis
    • Cluster analysis
    • Time series analysis
    • Social networks analysis
    • Machine learning alghorithm and neural networks
  • Applications and real cases using open-source software (KNIME and R) in the following areas: internet of things, social & web content analysis, customer relationship management, fraud detection and operations.

Detailed Description of Assessment Methods

There are two distinct grading procedures, the first one restricted to attending students (at least 70% of attendance in class) and the second for not-attending students.

Not attending students:
individual final exam in the computer lab (100% weight).

Attending students:
it will be based both on a group assignment (to be developed during the course and submitted before the end of the lessons; 40% of the final grade) and on an individual final exam in the computer lab (60% of the final grade), proposed in a reduced version compared to the full not-attending exam.

Textbooks

  • M. Kuhn, K. Johnson, Applied predictive modeling, Springer, 2013.
Exam textbooks & Online Articles (check availability at the Library)

Prerequisites

The course requires no prior knowledge of this methodological area, except for the data analysis fundamentals learned in a basic statistics course (undergraduate bachelor degree).
Last change 19/04/2016 15:03