Courses

This course will provide a comprehensive introduction to programming with Python, starting from the basics. Beyond confidently using Python, the class will focus on solving problems around Data Processing and Analysis. Additionally, we will discuss for what types of problems Python is the right choice and how to...
Instructor: Eszter Somos
Credits: 2.0
Banks today are primarily IT companies, at least from an operational perspective. Many of the leading European banks has been developing their IT infrastructure/applications over decades and most of them struggle with the underlying complexity and cost. Increasing demand from customers for digitization, novel...
Instructor: Szabolcs Szalay
Credits: 2.0
IBM SPSS Modeler is an extensive predictive analytics platform with an intuitive graphical interface. It provides a wide range of advanced statistical and data mining algorithms, which allows users to perform complex data analysis and to extract business value from data.This introductory course aims to give an...
Instructor: Gyorgy Kormendi
Credits: 1.0
This course is designed to help the students understand the important concepts and techniques used in Tableau to move from simple to complex visualizations and learn how to combine them in interactive dashboards.
Instructor: Ivett Kovács
Credits: 1.0
Type: core Seminars: Group 1: for full-time students primarily. Group 2: for part-time students in MSc in Business Analytics primarily. Group 3: for MSc in Finance students and all for whom the course in not offered in their program. MA in Economic Policy in Global Markets students have to choose one from Data...
Instructor: Gergely Daroczi
Credits: 2.0
Core for: MSc in Business Analytics ( Part Time...
Instructor: Gergely Daroczi
Credits: 2.0
This course is core for: MA in Economic Policy in Global Markets, MSc in Business Analytics, PhD in Business Administrationelective for : MA in Global Economic Relations, MSc in Finance Seminars:Group 1: programming in STATA, full-time schedule, for MA and PhD students primarily. Group 2: programming in R, full-time...
Instructor: Arieda Muço
Credits: 2.0
Type: core for MA in Economic Policy in Global Markets 1st yr, MSc in Business Analytics (full-time), MSc in Business Analytics (part-time)elective for: PhD in Business Administration, MA in Global Economic Relations (full-time), MA in Global Economic Relations (part-time), MSc in Finance (full-time), MSc in Finance (...
Instructor: Gábor Békés
Credits: 2.0
Type: electiveTiming: Full time: weekday Data analysis in business and policy applications is often aimed at prediction. The course introduces tools to evaluate predictions, such as loss functions or the Brier score. It emphasizes the importance of out-of-sample prediction, the role of stationarity, the dangers of...
Instructor: Gábor Békés
Credits: 2.0
Type: elective for PhD in Business Administration, MA in Economic Policy in Global Markets, MSc in Business Analytics (full-time), MSc in Finance (full-time), MSc in Finance (part-time)Timing: Full time: weekday Decisions in business and policy are often centered on specific interventions, such as changing monetary...
Instructor: Gábor Békés
Credits: 2.0
Instructor: Anikó Hannák (hannaka@ceu.edu, office hours: Tuesdays 4:00pm-5:30pm by appointment)Credits: 2 (4 ECTS)Term: Winter 2017-2018This course will provide a comprehensive introduction to programming with Python, starting from the basics. Beyond confidently using Python, the class will focus on solving problems...
Instructor: Ancsa Hannak
Credits: 2.0
The course aims showing various ways of telling stories using data visualization. First the course helps the students understand the important concepts and techniques used in Tableau to move from simple to complex visualizations and learn how to combine them in interactive dashboards. Second, this course will enable...
Instructor: Bence Arato
Credits: 2.0
Type: core for MSc in Business analyticsAfter an overview of the entire data science landscape, this course will focus on machine learning. The course will introduce the main fundamental concepts in machine learning (supervised learning, training, scoring, accuracy measures, test set, overfitting, cross-validation,...
Instructor: Zoltán Papp, Jenő Pál
Credits: 1.0
This course will build on the previous one (which introduced the basic concepts in machine learning) and will discuss state-of-the-art algorithms for supervised learning (linear models, lasso, decision trees, random forests, gradient boosting machines, neural networks, support vector machine, deep learning etc.). A...
Instructor: Zoltán Papp, Jenő Pál
Credits: 2.0
Type: electiveThis course will enable students to select the right visualization form to their data and present them in a way to engage their audience. We will learn through exercise how data visualizations work, what are the best practices and how to avoid common pitfalls. We will cover steps of the creation process...
Instructor: Krisztina Szűcs
Credits: 1.0
This is the key module aimed at discussing the importance of ethical behavior and the responsibility of modern corporations to society in the transnational business environment in which leaders and firms operate. Students will develop skills to link leadership issues to ethical problems and critically appraise the...
Instructor: Davide Torsello
Credits: 2.0
The course provides an introduction to the art and science of trading. Various timeframes, markets, instruments and analytical methods will be discussed. Success factors of automated and semi-automated trading strategies will be explored. Students will, in groups, design, backtest and forward-test their own trading...
Instructor: Ferenc Meszaros
Credits: 2.0
IMPORTANT: This course can accommodate a maximum of 30 students. Priority is given to Mathematics students (Master and PhD) and Network Science PhD students. All other students are selected based on the entry test score. Students that take the course for grade have priority over auditors. All students, both registered...
Instructor: Roberta Sinatra
Credits: 3.0