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Course 2019-2020 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)

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


Suggested background knowledge

Some familiarity with basic statistics and programming in R and Python.


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.
  • Analyzing textual data.
  • 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.

Teaching methods
  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Company visits
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
  • Group assignments
  • Participation in external competitions
DETAILS
  • Speakers from practice are brought to class to discuss about the challenges and costs involved in implementing Big Data solutions.
  • Home programming exercises are given to students.
  • A practical group assignment is presented in two sessions, at the half and end of the semester.

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING STUDENTS
    • One assignment representing 30% of final grade.
    • One final multiple-choice written exam representing 70% of final grade.
    NOT ATTENDING STUDENTS
    • One final multiple-choice written exam representing 100% of final grade.

    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS
    • Slides provided by the instructors.
    • F. PROVOST, T. FAWCETT (edition by O'REALLY), Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking.
    • 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.
    • F. CHOLLET (edition by Manning Publications), Deep Learning with R.
    • F. CHOLLET (edition by Manning Publications), Deep Learning with Python.
    Last change 16/06/2019 20:28