DA 501 Syllabus

This course teaches the fundamental ideas to clean, manipulate, process and analyze data. The students will work on data analysis problems arising in various data-intensive applications. The course involves many in-class coding exercises where the students are expected to work on several case studies. Through these exercises, the course shall also serve as an introduction to data analytics and modern scientific computing with Python programming language.

DA 503 Syllabus

This course is an applied statistics course with an emphasis on data analysis. In this course we will study several statistical modeling techniques and discuss real-life problems over which we’ll have a chance to apply statistical tools to learn from data. We will be covering some of the fundamental statistical methods like linear regression, principal component analysis, cross-validation and p-values. The lectures are designed to help the participants apply these techniques on large sets of data using a statistical programming language such as R or Python.

DA 505 Syllabus

In this course, we will cover fundamental aspects of Data Management including traditional data management as well as new models for big data. We will start with conceptual data modelling (ER and UML models), then study relational model, and how conceptual models could be converted to relational model. We will cover SQL language for querying relational data. We will continue with more recent models such as key-value stores, document databases and graph databases. Students will do practical work on database systems such as MySQL, Cassandra, and MongoDB.

DA 507 Syllabus

The aim of this course is to introduce the concept of analytical modelling, optimization problems and the fundamental properties of an optimization problem. Students will learn basics of transforming problems into analytical/quantitative/mathematical models, and how to formulate and solve simple mathematical models that represent optimization problems. Both exact algorithms and approximate algorithms, particularly heuristic techniques will be covered in order to form an understanding of algorithms and algorithm design to solve optimization problems. Throughout the course linear, nonlinear and integer optimization problems, network flow and network design problems will be the main focus with examples from the data science and data analytics domain.

DA 512 Syllabus

This course will provide the essential background to start to develop programs that will run on Hadoop Distributed File System (HDFS). The course will also show the students the limitations of traditional programming techniques and how Hadoop addresses these problems. After learning the basics of a Hadoop Cluster and Hadoop Ecosystem, students will learn to write programs using Apache Spark framework and run these programs on a Hadoop Cluster.

DA 513 Syllabus

Time Series Forecasting is one of the most applied data science techniques across many disciplines, most notably in finance, in supply-chain management, and in production planning. This course is intended to provide a comprehensive introduction to forecasting methods used in the analysis and forecasting of Time Series. At the end of the course, students will be able to analyze and explore time series data, build and apply various Time Series models suitable for a wide range of business problems. PYthon programming language and libraries will be used in the analysis and forecasting of Time Series problems throughout the course.

DA 514 Syllabus

In this course, we will cover fundamental aspects of Machine Learning. We will start with fundamentals of machine learning, including different learning paradigms, regression and classification problems, evaluation methods, generalization and overfitting. We will then cover some of the fundamental machine learning techniques such as decision trees, Bayesian approaches, Naive Bayes classifier, and logistic regression, k-Nearest neighbor, and online learning algorithms. Besides understanding the basic theory behind the techniques, students are expected to apply them on different platforms like Weka or Matlab.

DA 517 Syllabus

In the scope of this course we will begin with data extraction, cleaning, and normalization to prepare the data for Data Mining Algorithms. We will then cover Data Mining techniques including association rule mining, sequential patterns, clustering, text mining. Students are expected to understand the fundamental theory behind each technique, as well as implementing them using an environment such as RapidMiner or Weka.

Instructor: Ahmet Onur Durahim

Recent advances in deep learning have led to groundbreaking advances in many fields, including computer vision and natural language processing. This course aims to equip students with practical skills and theoretical knowledge to leverage cutting-edge deep neural network architectures and algorithms to solve real-world challenges. Students will first gain a thorough understanding of deep learning fundamentals such as network architecture design,activation functions, loss functions, optimization algorithms, and regularization techniques that collectively enable neural networks to learn complex patterns and representations from data. Students will then gain practical knowledge on deploying deep learning models,conducting experiments, and optimizing model performance through hands-on experience with real-world datasets using the Python Programming language and the PyTorch framework.

Instructor: Özgür Asar

Causal Data Science Causal data science has recently become a sub-discipline of general data science. The aim of this area is to draw cause-effect relationships from experimental and especially observational data. With this, the possible effects of the plannned interventions will be better understood. Application areas of causal data science consist of medicine, economy and finance, marketing, political sciences, management and tech industry. The main output of this course will be that the students will be able to obtain cause-effect relationships with modern machine learning methods. The course will be taught with applications in Python.

Instructor: Ahmet Onur Durahim

This in-depth course is designed to give participants a strong grasp of Generative AI, with a special emphasis on Large Language Models (LLMs) and their real-world applications. You’ll explore core concepts, learn about LLM architectures and training methods, and develop an understanding of their strengths and limitations. By the end of the course, you’ll be ready to apply various techniques in practice, including fine-tuning LLMs for specific tasks, connecting them to external data sources using Retrieval-Augmented Generation (RAG), and creating agents and agentic workflows to handle complex tasks.

DA 522 Syllabus

Given the widespread distribution of data in today’s buısiness world, the legal and ethical issues related to the use of data have been, and will be, of critical importance in establishing a corporate policy. Within the framework of these legal and ethical issues, students will gain an understanding of the following concepts: private, confidential, anonymous and open data; private versus public data; data ownership and proprietary rights; intellectual property; overview of existing legal framework; constraints, rules and legislative procedure in access and use of data.

DA 525 Syllabus

This course is intended to provide industry insight into the world of project management and business communication. Upon completion of this course, students are expected to have a clear understanding of the tasks and challenges that are fundamental to project management requirements. The course will also cover issues on team management and other aspects of project management on schedules, risks and resources for a successful project outcome. The second part of this course will concentrate on effective communication with team members, presentation techniques for a wide range of audiences and communicating results and recommendations to upper management and clients.

IT 542 Syllabus

This course will provide the essential background to start to develop programs that will run on Hadoop Distributed File System (HDFS). The course will also show the students the limitations of traditional programming techniques and how Hadoop addresses these problems. After learning the basics of a Hadoop Cluster and Hadoop Ecosystem, students will learn to write programs using Apache Spark framework and run these programs on a Hadoop Cluster.

All graduate students pursuing a non-thesis MSc. Program are required to complete a project. The project topic and contents are based on the interest and background of the student and are approved by the faculty member serving as the Project Supervisor. At the completion of the project, the student is required to submit a final report and present the project. The final report is to be approved by the Project Supervisor.

DA 515 Syllabus

This course aims at discussing the key principles of knowledge discovery process through various case studies arising from different application areas. The students are expected to learn the main steps to traverse when they face new data analytics problems. With each case study, the tools for cleaning, processing and altering the data shall be visited. A particular attention shall be given to data inspection, feature reduction and model selection. Each case study will be completed by a thorough discussion and interpretation of the results.

DA 516 Syllabus

The Machine Learning with Graphs course explores the intersection of graph theory, machine learning, and social network analysis to model and analyze complex networks in modern applications. The course covers foundational concepts including graph representations, link analysis, centrality measures, and community detection, alongside machine learning techniques such as node embeddings and Graph Neural Networks. Throughout the course, students will gain hands-on experience using tools such as NetworkX and PyTorch Geometric, applying these techniques to real-world challenges, including analyzing large-scale networks and modeling social, technological, and biological systems.

DA 518 Syllabus

Exploratory Data Analysis (EDA) is an approach to data analysis for summarizing and visualizing the important characteristics of a data set. EDA focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, and decide how it can be investigated with more formal statistical methods. EDA is distinct from Data Visualization in that EDA is done towards the beginning of analysis and data visualization is done towards the end to communicate one’s finding. This course particularly pays attention to the applied techniques to data visualization narratives. We will draw on case studies from the business world, industry to news media.