DIGITAL TRANSFORMATION AND BUSINESS STRATEGIES

Course Description

This course examines how organizations harness digital technologies, artificial intelligence, and automation to drive strategic growth and improve operational efficiency. Students will explore real-world case studies of digital transformation across various industries, gaining insights into how companies respond to technological disruption, adopt agile practices, and cultivate innovation. Key topics include the evolution of business models, digital maturity frameworks, and the role of leadership in the digital economy. Emphasis is placed on aligning digital initiatives with strategic objectives and market positioning. By the end of the course, students will be able to design strategic digital transformation roadmaps, integrating technology adoption with organizational goals. They will also develop a practical understanding of the challenges and opportunities associated with leading digital change in dynamic business environments.

DIGITAL TRANSFORMATION AND BUSINESS STRATEGIES

Evaluation Tools (Active Term)

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Course outcomes

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Course Syllabus

Reference Books & Course Materials

1. 01 Skilton, M., & Hovsepian, F. (2024). Digital transformation strategy: Harnessing our digital future (2nd ed.). Routledge.
2. 02 Johnson, G., Whittington, R., Scholes, K., Angwin, D., & Regnér, P. (2023). Exploring strategy (13th ed.). Pearson.

Learning Outcomes

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Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

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