This course will focus on methods and approaches that will assist in making decisions in an environment that has become more volatile, uncertain, complex and ambiguous with the digital transformation process. In the first part of the course, developments in neuroscience regarding learning and decision-making processes and the weaknesses of intuitive decision-making methods used by people will be shared, and it will be underlined why making decisions based on data is much more important today in the environment created by digital transformation. Within the scope of the course, various methods and tools (decision trees, multi-criteria decision solution methods, mathematical modeling and programming, heuristic/meta-heuristic methods, etc.) which enable decision making in different decision conditions, in a systematic way and based on data will be covered.

This course covers the basic mathematical concepts that will be required within the scope of Digital Transformation in Industry Non-Thesis Master's Program. The course consists of three modules: probability and statistics, linear algebra and calculus for optimization. The probability and statistics module will focus on conditional probability, Bayes’ Theorem, distributions, descriptive statistics, statistical inference, forecasting, hypothesis testing, regression, and maximum likelihood estimation. The linear algebra module will focus on vectors, matrices, linear transformations, inner product, orthogonality and eigenvectors.In the calculus for optimization, local and global optimization concepts, gradient descent/accent method and Lagrange multipliers methods will be covered.

Data Analytics aims to reveal the hidden information within the data by means of various methods, which would improve the decisions and the subsequent actions in order to create value from the data. In this process, there are various sub-processes such as business understanding, data understanding, data preparation, modeling, evaluation and deployment of the model. Within the scope of this course, metrics and methods that would be used to validate the models, supervised learning techniques (i.e., regression and classification), unsupervised learning techniques (e.g.clustering, association rule mining, principal component analysis) and feature engineering and feature subset selection methods will be discussed and various use cases in real life applications will be presented. 

This course will focus on various technologies such as the Internet of Things, Cloud/Edge Computing, Blockchain, Cybersecurity, Artificial Intelligence, Smart and Collaborative Robots, Robotic Process Automation, Augmented/Virtual/Mixed Reality, Metaverse, and Additive Manufacturing, which enable digital transformation. In the course, the history, status, and future trends of these technologies, various use cases and best practices in business and social life, and their potential in realizing possible new business models will be discussed.

Digitization is also having a transformative effect on operations in businesses and supply chains. There are radical changes in the structure and dynamics of various decision problems such as supplier selection, purchasing, warehousing, stock monitoring and inventory management, job scheduling, production and capacity planning, distribution and transportation optimization, vehicle planning and route optimization, end-to-end tracking and management of supply chains. Within the scope of this course, various use cases, best practices and potential trends in digital supply chain management, and analytical approaches and methods that can be used in decision-making processes will be discussed.

The course will first focus on sensors and data acquisition in manufacturing. Monitoring and controlling manufacturing processes, digital twins and digital factory, manufacturing optimization and data-driven manufacturing processes and applications will be the other concepts that will be covered in the scope of the course.

The main purpose of this course is to deal with project management in all aspects and introduce the fundamentals ofproject investment evaluation. The course will cover, introduction and basic definitions, the relationship of organizational strategy and projects, systems approach to projects, specification of the content and relevant data of work packages, project scheduling, project scheduling in uncertainty, resource constrained project scheduling, budgeting, risk analysis and management, project performance management, agile and hybrid project management, interest calculations and cash flow, feasibility analysis, project selection, program and portfolio management, project applications.

In this course, the digital transformation will be discussed from strategic point of view, the organizational point of view and the innovation point of view. Topics that will be covered as part of the course includes: competitive strategies on the axis of enabler technologies of digital transformation; business models; business processes; product design and manufacturing processes;organizational culture and human resources; organizational structures; agile management; innovation management and design thinking; lean startup; case studies.

The success of digital transformation will only be possible with the transformation of people. One of the primary objectives of this course is to enable students to gain an in-depth understanding of the transformative leadership competencies needed in the digital age, to realize their own strengths and to see how to strengthen their weaknesses. Other topics that will be covered in the course will be building and developing learning teams, strengthening and motivating collaborations between people, effective communication and coaching, strategy formulation and implementation under uncertainty, and change management.

Human Resources Management (HRM) has a central role in digital transformation since the transformation of the people is the key for the success of any sort of transformation in organizations and as one of the functions that is expected to make maximum use of the opportunities provided by digital transformation technologies. This course will focus on key performance metrics and their measurement, robotic process automation, human resource analytics applications and best digitalization practices in HRM. Within the scope of the course,how the information obtained from human resources data, which starts to be accumulated even before the recruitment of the employees and continues to be acquired until their last days in the organization, by means to visualization, reporting and data analytics applications, changes the role of HRM in the organization will also be discussed.

The course consists of two modules. In the first module, the interpretation, analysis and applications of the tables in which financial transactions are reflected will be discussed and financial literacy will be developed for non-financiers. In this context, topics such as general accounting and financial statements, cash flow statements, financial analysis, key performance indicators will be covered. In the second part of the course, the effects of digitalization on the financial world will be emphasized, and the current situation and future trends in Blockchain technologies, decentralized finance, token economy, cryptocurrencies, FinTechs, and similar components of the broad digital finance ecosystem will be shared with various best practices and use cases.

The primary aim of the course is to increase the competencies of companies in creating, implementing and measuring digital marketing strategies alongside with the conventional marketing activities. Within the scope of the course, topics such as the basic components of digital marketing strategies, digital media impact measurement, customer experience mapping, search engines and optimization, social media marketing, online marketing and internet analytics will be covered, various cases and best practices will be discussed and hands-on training will be provided.

The course will cover theory, concept and practice on why and how conflicts arise, how they flare up, their consequences, prevention methods and approaches, possible constructive solution methods, and how different negotiation styles and approaches at every stage of the process will affect the dynamics of the process. During the course, students will discuss past and current examples of interpersonal, inter-group, intra-firm and inter-firm conflicts, and they will have the opportunity to develop their negotiation practices and test the theory's compatibility with real life firsthand through role-playing games.

Roughly speaking, one can define innovation as “novelties that yields profit”. This definition involves three basic components: (1) “Profit making” (i.e., the market), (2) The operations that deliver these innovations to the market (i.e., the innovation practices), and (3) The process of creating new ideas (i.e., the creative process). Innovations that need coordination of these three components have different types such as product, process, business model innovations. Within the scope of the course, various concepts, approaches and tools that are both related to the innovation theory and innovation practices such as: process of creating new ideas; 4P model of creativity; creative destruction;theory of diffusion; disruptive innovations; innovation portfolio and innovation funnel; ten faces of innovations: ten types of innovation; design thinking; the lean start-up will be covered.

In the complex, dynamic and ambiguous problems that appear even more in our daily lives with digital transformation, the systems thinking aims to help people make better decisions by making them understand the subject by looking at it whole rather than by splitting it to parts. Within the scope of the course, by the virtue of systems thinking, systems modeling approaches and methods, and system dynamic analyzes, the projection of the long-term effects of the decisions that are made will be covered, and by means of systematic trials of various strategies how the complex behavior patterns of the system can be determined will be discussed.

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.