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Course 2020-2021 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  |  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
  • Online lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments
DETAILS
  • Speakers from both practice and academia are brought to class.
  • 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 STUDENTS
    • One group assignment representing 80% of final grade to be presented and discussed in class.
    • One final multiple-choice written exam representing 20% of final grade.
    NOT ATTENDING STUDENTS
    • One individual assignment representing 40% of final grade to be given to the instructor for review.
    • One final multiple-choice written exam representing 60% of final grade.

    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

    Main source:

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

     

    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 HastieRobert TibshiraniJerome 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 21/07/2020 18:02