MIS403 Machine Learning for Business and Economics

Rapid advances in machine learning are transforming the way organizations analyze data and make decisions in economics, finance, and management. This course equips Management Information Systems students with a rigorous yet practical foundation in modern machine-learning methods, emphasizing both their strategic value and their methodological boundaries. We survey core techniques—classification, regression, clustering, and selected advanced topics—focusing on how each can generate insights from complex, high-dimensional business data. Through hands-on projects and critical discussions of recent applications. For More Information

MIS315/MIS 301 Data Analytics I

This course is the first part of a two-semester data analytics course that is required to extract knowledge from business data. The topics covered are: Review of Programming Software, Getting Started with Data, Causality, Learning: Supervised and Unsupervised, Data and Models, Trees, Entropy, Information Gain, Neighbor Models, and Distance as Similarity. For More Information

MIS207 Programming for Data Science

In the era of data-driven decision-making, fluency in a programming language that combines statistical power with rapid prototyping is indispensable. R, supported by the RStudio ecosystem, has become a cornerstone of modern analytics across business, economics, and the social sciences. This course introduces Management Information Systems students to the essentials of programming for data science, guiding them from first installation to applied data manipulation. Lectures emphasize live coding and follow-along exercises, reinforced by assignments that build a cumulative mini-project. Upon completion, students will possess the programming toolkit necessary to progress confidently to more advanced analytics and machine-learning courses. For More Information

MAN445 Data Analytics for Business

This course aims to introduce data-analytics for management students. Topics covered in this course include: introduction to data analytics, data-driven thinking, business problems and data science solutions, introduction to R, learning from data (supervised versus unsupervised), entropy and information gain, regression and classification trees, linear models, logistic regression, nearest neighbor methods, and model performance and clustering. For More Information

MIS302 Data Analytics II

This course is the second part of a two-semester data analytics course, which aims to introduce data analytics for business. As a background, the first part of data analytics topics, including data-driven thinking, business problems, and data science solutions, R Programming, R Data Environment, and learning from data -supervised versus unsupervised- will be covered. Similarity measures, clustering, dimension reduction, and a brief introduction to recent advancements in data analytics will be discussed. For More Information

IVA 510: İşletmeler İçin Veri Analitiği

Bu ders özellikle yüksek hacimli veri kullanılan işletmeler için gerekli veri bilimi araçlarını öğrenci ile buluşturmaktadır. Bu tekniklerin teorisi ile birlikte uygulamaları R-programı ile yapılacaktır. Dersin kapsamında entropi ve bilgi kazancı, sınıflandırma ve regresyon ağaçları, destek vektör makinesi (support vector machine), k-NN, lojistik regresyon ve kümeleme yöntemleri olmaktadır. Bu ders için öğrenciler yüksek hacimli veri kullanarak bir uygulama projesi de hazırlayacaklardır. Daha fazla bilgi için

ECO677 Finans Teorileri (Hacettepe)

Bu dersin amacı finansal ekonomi programı (Tezsiz YL) öğrencileri için temel finansal kavramlara giriş yapmak ve finansal teorileri tanıtmaktır. Daha fazla bilgi için

ECO427 Financial Markets (Hacettepe)

Introduction to the financial system and markets as well as the widely used financial instrument and their pricing models. In addition, Risk Management concept is also covered. For more information

ECO447 Machine Learning for Economists (Hacettepe)

Introduction to machine learning (ML) techniques. The course discusses the difference between traditional Econometric methods and Statistical learning. Then, introduce these learning techniques. For more information

ECO 742 Topics in Time Series Analysis (Hacettepe)

The aim of this course is to introduce current state-of-the-art methodologies in Time series. The topics include the following: discrete-continuous time series, domains: probability-time-frequency, time series, and causality, exploratory analysis of multivariate time series, similarities of time series, clustering of time series, and general approach for time series data mining. For more information

ECO 674 Finansal Ekonomi (Hacettepe)

Bu dersin amaçları; (i) finans teorilerinin temelleri ışığında öğrencilere risk ve risk yönetimini, (ii) finansal enstrümanların fiyatlama tekniklerini tanıtmak ve (iii) piyasa ve kredi risk ölçümlerindeki temel hesaplamaları tanıtmaktır. Daha fazla bilgi için

ECO665 Applied Time Series Analysis (Hacettepe)

This course aims to introduce time series analysis and its applications in economics/finance. At the end of the course, the students shall be able to create time series models for real-world time series data. The main software used in this course for statistical programming is [R](http://cran.r-project.org/ "The Comprehensive R Archive Network"). Students shall be able to use R and its related packages. The content of the course is dynamic and changing every year. For more information

ECO 450 Decision Theory (Hacettepe)

The aim of this undergraduate course is to introduce the concept of decision-making with and without uncertainty to fourth-year students. The following topics are included: Heuristics, utility theory and utility function (single-multi attribute), statistical decision theory, risk and uncertainty, decision trees, simulation, and stochastic dominance. For more information

EKO 450 Decision Theory (Hacettepe, Turkish)

Bu ders iktisat öğrencilerine karar verme analizlerinin temellerini tanıtmayı amaçlamaktadır. Dersi başarıyla tamamlayan öğrenciler, belirlilik ve belirsizlik altında [rasyonel] karar vermeye yönelik model, analiz teknikleri ve gerçek hayat uygulamalarına yönelik bazı araçların kullanımını da öğrenmiş olacaklardır. Dersin içeriği; karmaşık ortamlarda karar verme; risk ve belirsizlik; istatistiksel karar teorisi, fayda teorisi; karar ağaçları; buluşsal yöntemler; benzetim yöntemleri; Stokastik üstünlük; etkin sınır; çok amaçlı karar verme parçalarından oluşmaktadır.

EKO453 International Finance (Hacettepe)

ECO 751 Financial Economics (Hacettepe)

ECO743 Stochastic Differential Equations

This course aims to introduce stochastic differential equations and the application of continuous-time series modeling in finance and economics. At the end of the course, the students shall be able to model the real-world time series data. For more information

EKO427 Finansal Piyasalar (Hacettepe)

Finansal Piyasalar dersinde, Piyasalar, i leyi i, Risk ve Risk Yönetimi ve Finansal Enstrümalnarın Fiyatlama modelleri ele alınacaktır. Daha fazla bilgi için

ECO 634 Financial Theories (Hacettepe)

The aim of this course: (i) to introduce the fundamental theories in finance, (ii) to introduce the pricing of some financial instruments, and (iii) to introduce financial risk management, including pricing financial derivatives and instruments. For more information

EKO 450 Decision Theory (Hacettepe, in Turkish)

This is the decision theory course (ECO450) given in Turkish. For more information

ECO751 Financial Economics

Course content includes two parts, Foundations: Uncertainty-Risk-Macroeconomics; Monetary Activities, Money-Bank and Regulations, Financial Theories, Efficient Markets; Pricing and Practice: Stochastic processes and probability, Asset pricing, Options and pricing, financial innovations and recent crisis.

ECO239-ECO240 Statistics I&II (Hacettepe)

ECO137-ECO138 Mathematics for Economist I&II (Hacettepe)

Financial Management

MAN613 International Finance (Hacettepe)

SEF604 Risk Management and Actuarial Methods (Hacettepe)