Course 2022-2023 a.y.

20564 - BIG DATA FOR BUSINESS DECISIONS

Department of Accounting

Course taught in English
Go to class group/s: 31
CLMG (6 credits - I sem. - OP  |  12 credits SECS-P/07) - M (6 credits - I sem. - OP  |  SECS-P/07) - IM (6 credits - I sem. - OP  |  SECS-P/07) - MM (6 credits - I sem. - OP  |  SECS-P/07) - AFC (6 credits - I sem. - OP  |  SECS-P/07) - CLELI (6 credits - I sem. - OP  |  SECS-P/07) - ACME (6 credits - I sem. - OP  |  SECS-P/07) - DES-ESS (6 credits - I sem. - OP  |  SECS-P/07) - EMIT (6 credits - I sem. - OP  |  SECS-P/07) - GIO (6 credits - I sem. - OP  |  SECS-P/07) - PPA (6 credits - I sem. - OP  |  SECS-P/07) - FIN (6 credits - I sem. - OP  |  SECS-P/07)
Course Director:
FRANCESCO GROSSETTI

Classes: 31 (I sem.)
Instructors:
Class 31: FRANCESCO GROSSETTI


Suggested background knowledge

A good familiarity with statistics: hypothesis testing, linear modeling. A basic knowledge of what a programming language is. Familiarity with the R programming language.

Mission & Content Summary

MISSION

Today's world is constellated by interdisciplinary professional figures, like data scientists, who are able to successfully mix different technical skills to provide extremely powerful insights from data. The mission of this course is to teach students of business administration and related fields how to reduce the gap when interacting with more quantitative colleagues. The course provides some basic knowledge about statistical modeling, data visualization as well as computer programming which are all fundamental aspects when developing an impactful storytelling.

CONTENT SUMMARY

  • Definition of Big Data.
  • Parallel and distributed computing.
  • Statistical modeling: from linear regression to clustering and classification.
  • Model evaluation.
  • Data visualization.
  • Natural Language Processing
  • Artificial Neural Networks.
  • Introduction to programming in R and Python.
  • Statistical modeling in R and Python.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Recognize business problems where Big Data can be applicable.
  • Recognized the main statistical models generally adopted to extract insights.
  • Understand the complexity of analyzing textual data.
  • Understand what an Artificial Neural Network is and what are its main components.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Solve business problems by data-analytic thinking.
  • Use several tools and techniques to practically implement solution methods.
  • Use R to carry out simple statistical analyses and visualizations.
  • Prepare and discuss a scientific report.

Teaching methods

  • Face-to-face lectures
  • Online lectures
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments

DETAILS

  • Reviews of programming lectures are given to students for home studying.
  • For attending students only, a practical group assignment is presented in class at the end of the course.

Assessment methods

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

ATTENDING AND NOT ATTENDING STUDENTS

-


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

Main source:

  • Slides provided by the instructor.
  • Papers will also be circulated by the instructor.

 

Additional sources:

  • G. RUBERA, F. GROSSETTI (edition by Egea BUP), Python for non-Pythonians: How to Win Over Programming Languages.
  • J. SILGE, D. ROBINSON (edition by O'REALLY),Text Mining with R: A Tidy Approach.
  • G. GROLEMUND, H. WICKHAM (edition by O'REALLY), R for Data Science.
  • G. GROLEMUND (edition by O'Really), Hands-On Programming with R: Write Your Own Functions and Simulations.

 

Advanced readings:

  • Trevor Hastie, Robert Tibshirani, Jerome Friedman:The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (available in pdf here: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

  • F. CHOLLET (edition by Manning Publications), Deep Learning with R.
  • F. CHOLLET (edition by Manning Publications), Deep Learning with Python.
Last change 08/06/2022 09:25