Course 2024-2025 a.y.

20538 - PREDICTIVE ANALYTICS FOR DATA DRIVEN DECISIONS MAKING

Department of Decision Sciences

Course taught in English

Student consultation hours
Class timetable
Exam timetable
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  |  12 credits SECS-S/01) - MM (6 credits - II sem. - OP  |  SECS-S/01) - AFC (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  |  SECS-S/01) - EMIT (6 credits - II sem. - OP  |  SECS-S/01) - GIO (6 credits - II sem. - OP  |  SECS-S/01) - DSBA (6 credits - II sem. - OP  |  SECS-S/01) - PPA (6 credits - II sem. - OP  |  SECS-S/01) - FIN (6 credits - II sem. - OP  |  SECS-S/01) - AI (6 credits - II sem. - OP  |  SECS-S/01)
Course Director:
LUCA MOLTENI

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


Suggested background knowledge

Students are expected to have basic knowledge of descriptive and inferential statistics.

Mission & Content Summary

MISSION

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 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).

CONTENT SUMMARY

  • Data management architectures: a brief overview.
  • Data understanding and data preparation.
  • Models and statistical techniques applied to Big Data.
    • Linear and logistic regression.
    • Regression and classification trees.
    • Time series analysis.
    • Machine learning algorithms and neural networks.
  • Models' performance evaluation.
  • 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.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

Get the following competences:

  • Big Data ingestion and management.
  • Data preparation and cleaning.
  • Machine learning algorithms application.
  • Machine learning model evaluation.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Improve his skills to manage and to take advantages of the huge availability of data nowadays produced by a great variety of sources, using one of the machine learning software preferred by data scientists.

Teaching methods

  • Guest speaker's talks (in class or in distance)
  • Practical Exercises
  • Collaborative Works / Assignments

DETAILS

In the course two different approaches are used: theoretical and applicative. A number of machine learning data analysis case histories are shown, on Big and Small Data, using specific machine learning software (Knime and R). At the end of the course, students are able to reply all the analysis by themselves.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
   x x
  • Individual Works/ Assignment (report, exercise, presentation, project work etc.)
 x    
  • Peer evaluation
 x    

ATTENDING STUDENTS

It is based both on a group assignment (to be developed during the course and submitted before the end of the lessons; 50% of the final grade) and on an individual final written exam (50% of the final grade), proposed in a reduced version compared to the full not-attending exam.

 

With the written exam we test the technical knowledge of the students,with the assignment the ability to manage and to take advantages of the availability of data from a great variety of sources, using one of the machine learning software preferred by data scientists (Knime).


NOT ATTENDING STUDENTS

Individual final written exam (100% weight).

 

With the closed question we test the technical knowledge of the students, with the open questions the ability to interpret the results emerging from the application of the analytical software proposed in the course and to evaluate the quality of alternative machine learning models.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • M. KUHN, K. JOHNSON, Applied predictive modeling, Springer, 2013.
  • Teacher's slides.
Last change // :