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Thinking with Data (18CBA20001)

General information

Type:

OP

Curs:

1

Period:

S semester

ECTS Credits:

3 ECTS

Teaching Staff:

Group Teacher Department Language
Ed: 1 Uri Simonsohn Operaciones, Innovación y Data Sciences ENG

Group Teacher Department Language
Ed: 2 Uri Simonsohn Operaciones, Innovación y Data Sciences ENG

COURSE CONTRIBUTION TO PROGRAM

This course focuses less on technical aspects of data analysis and more on conceptual ones. Thinking with data is what we do before and after running the statistical tools students learn in other courses. The course content is primarily based on the following three sources of information:
(1) Psychological research on people's incorrect intuitions about data and randomness,
(2) Practical tools empirical researchers have developed, mostly in medicine, economics, and political science, to make firmer causal inferences from data. These tools are useful also for everyday decision making in organizations but solve problems that are typically assumed away in traditional statistical textbooks and courses, and
(3) Foundational statistical concepts which are also useful for everyday decision making in organizations but which are usually too abstractly covered to be implemented in everyday practice (e.g., statistical power, practical vs statistical significance, concluding something does not exist vs failing to conclude it does, etc.).

Course Learning Objectives

Improve students' ability to:
1) Figure out what is the right way to ask a given question from data, and
2) Understanding what question a given analysis is actually answering.
For example, say a company rolls out a promotion to increase sales. Comparing sales before and after the promotion does not tell us if it was successful.
This course helps students better understand what question the before after comparison does answer, and how to adequately asses if the promotion worked.



CONTENT

1. Content

1) Statistical bootstrapping (to conceptually understand black-box solutions in classical stats and to carry out analyses for which formulas do not exist)
2) How to evaluate if an intervention was successful?
3) The challenge specifying the question after we see the data (that vs like-that and mutiple comparisons)
4) Experiments as information
5) Diagnosing and correcting for self-selection
6) Disentangling skill from luck in performance
7) Forensic statistics (how to detect errors, selective reporting, and fraud)
8) Learning from errors and close-calls

Methodology

Lecture discussion and hands-on analysis of data in class. We will often contrast how students solved a (practical, real-world) problem before coming to class with how the content covered suggests we cover it.

Assessment criteria

25% In class activities and quizzes
20% Preparation questions
15% Prediction project
40% Final exam

Bibliography

There is no textbook for this course. For most classes background readings will be recommended rather than required.
They will not cover the same material that's covered in class.

Timetable and sections

Group Teacher Department
Ed: 1 Uri Simonsohn Operaciones, Innovación y Data Sciences

Timetable Ed: 1

Group Teacher Department
Ed: 2 Uri Simonsohn Operaciones, Innovación y Data Sciences

Timetable Ed: 2

From 2019/4/24 to 2019/5/3:
Wednesday and Friday from 15:30 to 18:30. (Except: 2019/4/26 and 2019/5/1)

From 2019/5/8 to 2019/6/19:
Each Wednesday from 15:30 to 18:30.