Economics, Management and Data Science

Econometrics

This course covers some of the most important methodological issues arising in any field of applied economics when the main scope of the analysis is to estimate causal effects. A variety of methods will be illustrated using theory and papers drawn from the recent applied literature. The aim is to bridge the step from a technical econometrics course to doing applied research. The emphasis will be on the applications. The goal is to provide students with enough knowledge to understand when these techniques are useful and how to implement each method in their empirical research.

Convex Optimization

The course covers the basics of convex optimization methods, with an emphasis on numerical algorithms that can solve a large variety of optimization problems arising in control engineering, machine learning, mechanical engineering, statistics, economics, and finance.

The materials for the course are available at http://www.stanford.edu/~boyd/papers/cvx_short_course.html

Basic Numerical Linear Algebra

The course is aimed to recall the basic notions about vectors, matrices, vector spaces and norms, along with the basic numerical methods concerning the solution of linear systems. In particular: direct methods for square linear systems and conditioning analysis; direct methods for solving over-determined linear systems in the least square sense. The course also provides an introduction to Matlab, which is used for implementing the illustrated methods.

Banking and Finance (long seminar with optional exam)

One of the most challenging task in finance is the gap between theoretical models and the actual software implementation. Cross some different areas (derivatives evaluation, risk management, accounting issues) several problems arise: discretization, analytical approximation, montecarlo simulation vs. numerical probability, optmization and so on. After a short overview of the main financial areas, the course aims to give some insights on these topics, with a special focus on the risk management current hard problems and the related software algorithms.

Analytics and Data Science in Economics and Management I

Python Course for Data Science (M. Puliga):
- Introduction to the language: basic statements, cycles and functions
- Diving into the language: advanced types: sets and dictionaries, classes and modules, using PIP and ipython
- Scraping the web: introduction to BeautifulSoup, the regular expressions module re, the request module
- Introduction to Plotting: basic numpy, plotting overview
- Data science utilities: introduction to SQL (sqlite/mysql)

Getting, Organizing and Analyzing the Data (A. Petersen):

Scientific Writing, Dissemination and Evaluation (long seminar without exam)

In order to ensure their widest possible dissemination, research results need to be presented in academic publications and in talks. The first goal of this course is to introduce students to basic principles of academic writing and on basic techniques to plan and deliver good academic talks. In addition, the course discusses the key principles of peer review, which is what makes science reliable knowledge. In particular, the course focuses on how to write a professional referee report.

Further information is available at http://www.ru.is/kennarar/luca/IMTHOWTO/

Project Management

Project management; event management; communication and marketing; practical tools of organization; budgeting. Dealing with multiple stakeholders/ Risk management / Time management / PM tips to run an international research/Management plan concept on heritage sites / When applying for funds how do we measure project success / How we manage the output of the management plan / Flat organisations.

Philosophy of Science (long seminar without exam)

We know a lot of things ? or, at least, we think we do. Epistemology is the branch of philosophy that studies knowledge: its main features, the dynamics of its growth, as well as its claims for truth, validity, and progress. In this course ? which is designed as a series of seminars held by the students, preceded by a few introductory lectures ? we will consider some of the key contributions to the philosophical debate about the growth of scientific knowledge in the twentieth century, from Logical Positivism to Karl Popper, from Thomas Kuhn to Paul Feyerabend.

Marketing Science and Consumer Behavior

The main goals of the course are:

(1) to take economic theories and methodologies out into the world, applying them to interesting questions of individual behavior and societal outcomes;
(2) to develop a basic understanding of human psychology and social dynamics as they apply to marketing contexts;
(3) to become familiar with the major theory and research methods for analyzing consumer behavior;
(4) to develop market analytics insight into consumer actions.