Course 2019-2020 a.y.

20568 - EMPIRICAL METHODS FOR INNOVATION STRATEGIES

Department of Management and Technology

Course taught in English
Go to class group/s: 31
CLMG (6 credits - I sem. - OP  |  SECS-P/08) - M (6 credits - I sem. - OP  |  SECS-P/08) - IM (6 credits - I sem. - OP  |  SECS-P/08) - MM (6 credits - I sem. - OP  |  SECS-P/08) - AFC (6 credits - I sem. - OP  |  SECS-P/08) - CLELI (6 credits - I sem. - OP  |  SECS-P/08) - ACME (6 credits - I sem. - OP  |  SECS-P/08) - DES-ESS (6 credits - I sem. - OP  |  SECS-P/08) - EMIT (6 credits - I sem. - OP  |  SECS-P/08) - GIO (6 credits - I sem. - OP  |  SECS-P/08) - PPA (6 credits - I sem. - OP  |  SECS-P/08) - FIN (6 credits - I sem. - OP  |  SECS-P/08)
Course Director:
SANDEEP DEVANATHA PILLAI

Classes: 31 (I sem.)
Instructors:
Class 31: SANDEEP DEVANATHA PILLAI


Suggested background knowledge

Basic computer literacy. Install Anaconda (www.anaconda.com/distribution). Install Stata (to be done by Bocconi IT Department; instructions given in the first class).

Mission & Content Summary

MISSION

This course provides the students with the tools and methods to produce empirical evidence to support decision-making processes in firms and it is designed for whoever wants to use these methods in practice to solve concrete managerial problems. The initial part of the course reviews the basic tools and technique for data analysis, including introduction to Stata and Python, to put each student at the level of students who have already worked on these techniques. For attending students, the course then focuses on a group project that represents the main goal and learning tool of the course. In this respect, the course is an advanced course that teaches mainly through practical applications of the concepts and material. The methods, goals and projects of the course are broad enough to deal with and apply to different managerial problems and contexts, from marketing to entrepreneurial decisions, innovation, firm strategy, business models. Along with the main group project the course features the use of data made available to the students to address concrete managerial problems and questions. The course also develops an important focus on the use of textual data, large data-set, and machine learning techniques.

CONTENT SUMMARY

  • A summary of tools to make data-driven managerial decisions: from correlation and tests of hypotheses to regressions and causal relations.
  • Advanced tools (textual analysis, machine learning).
  • Group project.
  • Data analyses and exercises.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Learn how to use Stata and Python to make data-driven managerial decisions.
  • Learn how to use these tools in practice.
  • Learn how to use large datasets, and some basic techniques of textual analysis and machine learning.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Understand the basis to make data-driven managerial decisions.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments
  • Interactive class activities (role playing, business game, simulation, online forum, instant polls)

DETAILS

For attending students learning in the course depends mostly on the development of a group project throughout the course. The group project employs real data to address a concrete managerial problem. Lectures, discussions in class about the projects developed by each group enhance the learning opportunities of the attending students.


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
  • Active class participation (virtual, attendance)
     x

ATTENDING STUDENTS

  • Group project accounts for about 60%
  • Individual assignments for about 40%. 

An attending student is a student who participated in no less than 20 classes. Class attendance is strongly encouraged.


NOT ATTENDING STUDENTS

Assessment based only on a written final exam.


Teaching materials


ATTENDING STUDENTS

  • Lecture slides.
  • General references for some of the more technical material (not required for the exam, but useful for consultation).
  • Jupyter notebooks.

NOT ATTENDING STUDENTS

  • Lecture slides and references to specific articles and similar material.

  • General references for some of the more technical material (not required for the exam, but useful for consultation).

  • Jupyter notebooks.

Last change 16/06/2019 20:38