Course 2018-2019 a.y.

20592 - STATISTICS AND PROBABILITY

Department of Decision Sciences

Course taught in English
Go to class group/s: 23
DSBA (8 credits - I sem. - OB  |  2 credits MAT/06  |  6 credits SECS-S/01)
Course Director:
REBECCA GRAZIANI

Classes: 23 (I sem.)
Instructors:
Class 23: REBECCA GRAZIANI


Mission & Content Summary

MISSION

The course aims at providing students with a solid background in statistics and probability needed for running basic and advanced data analyses. Building on knowledge of probability and calculus, students are introduced to formal statistical reasoning with an emphasis of understanding the logic behind data analysis procedures. Students are exposed as well to advanced methods for data description and to computational tools they can proficiently use to analyze data and explore the conceptual challenges of inferential reasoning. Statistical models for classification and prediction is discussed in the last part of the course. The lectures switch between frontal lecturing, small group discussions and simulations. Students are introduced to the use of Python for retrieving and describing data, writing codes for implementing statistical methods and running model based data analyses.

CONTENT SUMMARY

  • Advanced methods for data description and visualization.
  • Dimension reduction and clustering.
  • Probability: notable distributions, limit theorems.
  • Statistical inference and prediction: Maximum Likelihood approach.
  • Algorithms for optimization and integration.
  • Generalized Linear Models.
  • Classification models.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Define and explain rigorously the main notions of probability and statistical learning.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Design, apply and implement proper statistical analsys and uncertainty quantification.
  • Prepare effective reports.

Teaching methods

  • Face-to-face lectures
  • Case studies /Incidents (traditional, online)
  • Group assignments

DETAILS

Group assignments: statistical analysis of real data.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Group assignment (report, exercise, presentation, project work etc.)
 x   x

ATTENDING STUDENTS

  • Written exam: general exam is marked out of 31 and contributes 50% to the final mark.
  • Periodic assignments: individual or group work aiming at building the skills needed for the final project. Marked out 31 contribute by 10% to the final mark.
  • Project: Individual or group real data analysis. Marked out 31 contributes by 40% to the final mark.

NOT ATTENDING STUDENTS

  • Written exam: general exam is marked out of 31 and contributes 60% to the final mark.
  • Project: Individual or group real data analysis. Marked out 31 contributes by 40% to the final mark.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

References to textbooks and papers and Python notebooks are provided.

Last change 05/07/2018 11:45