Course 2021-2022 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  |  12 credits 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 tools needed for Python programming (instructions given before class starts).

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.

 

Expected Teaching Schedule

 

Class Topic
1 Python 1: variables and strings
2 Python 2: lists and sets
3 Python 3: dictionaries
4 Python 4: if-statements
5 Python 5: for-loops
6 Python 6: text classification
7 Basic Stats
8 OLS-1
9 OLS-2
10 In-class exercise
11 Non-Linear Y
12 Practice
MID TERM BREAK
13 Practice
14 In-class exercise
15 Tobit & Diff in Diff
16 Practice
17 Instrumental Variable
18 Practice
19 Final project consultation
20 Practice
21 Final project consultation
22 In-class exercise
23 Final project presentations
24 Final project presentations

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Learn how to use Python to make data-driven managerial decisions.
  • Learn how to use econometric tools in practice using large datasets.

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    
  • Group assignment (report, exercise, presentation, project work etc.)
 x    
  • Peer evaluation
 x    

ATTENDING STUDENTS

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

 

The evaluation of the attending students is based on three in-class exercises plus a final empirical project produced in groups and presented in class. You will be considered as an attending student if you complete the exercises and submit the final project. In-class attendance will NOT be taken. However, class attendance is strongly encouraged. In the past, students who show up to class have out-performed those who do not by a substantial margin. 

 

Expected grading scheme

 

Activity Points
Best 2 out of 3 in-class exercises  10
Final project report 12
Final project presentation 3
Project peer evaluation 5
Bonus: Usage of data from proprietary databases subscribed to by Bocconi Library. Examples include Compustat, Bloomberg, etc. You are allowed to use non-proprietary and publicly available databases from the internet for your project - but you will not get the bonus point. 1
Total 31

NOT ATTENDING STUDENTS

The evaluation of the non-attending students will be based on a closed book written exam consisting of questions on both the theoretical and programming aspects of the course. In the past, non-attending students have performed poorly in this course. 


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.

  • Text Books

Last change 22/08/2021 22:50