Master of Science in Technology Management: Data Science

BU 601 focuses on understanding and managing individual and group behavior in organizations, stress management and well-being, and ethical responsibilities in business. The course explores the organizational behavior model, diversity’s role in interpersonal interactions, and job satisfaction measurement. Students learn about emotions, moods, and the relevance of intellectual and physical abilities. The curriculum covers virtual communication in modern organizations and the negotiation process. It addresses stress management at individual and organizational levels and examines group decision-making strengths and weaknesses. By integrating these topics, the course provides a comprehensive understanding of human behavior in organizational settings, equipping students with essential skills for effective management and ethical leadership in today’s dynamic business environment.

OD 655 examines the fundamental role of creativity and innovation in leadership and organizational development. The course connects these concepts to various organizational practices, including human resources programs and organizational development interventions. Students explore the significance of creativity and innovation at individual, team, and organizational levels. The curriculum emphasizes the application of creative and innovative approaches to problem-solving, human resources management, team dynamics, and diversity initiatives. By focusing on these areas, the course aims to enhance organizational effectiveness and adaptability. Students gain proficiency in relevant terminology, concepts, and analytical techniques, preparing them to foster innovation and creativity in their professional roles and contribute to organizational growth and success.

OD 688 provides a comprehensive exploration of leadership theory within the context of management and organizations. The course surveys a broad spectrum of leadership theories and the research underpinning them. Students learn to analyze various models of leadership behavior and effectiveness, understanding both their strengths and limitations. The curriculum examines how organizational structures, followers, and situations influence leadership effectiveness. It also delves into the impact of leadership on change processes within organizations. By the end of the course, students are equipped to compose their own set of guiding principles for leadership development. This approach enables students to apply theoretical knowledge practically, developing their leadership skills and understanding of organizational dynamics.

PM 672 offers a comprehensive overview of project management, focusing on its main components, project metrics, and strategies to improve project success rates. The course explores various project management approaches, including traditional IPECC, agile, and scrum methodologies. Students gain familiarity with key project metrics and learn to perform basic calculations, acquiring practical tools for use in their professional environments. The curriculum emphasizes the critical balance between hard and soft skills essential for project managers’ success. It covers interpersonal dynamics, project lifecycle planning, and effective management of project participation, teamwork, and conflict. The course also provides insights into the PMP exam, preparing students for professional certification. This practical approach equips students with the skills needed to navigate complex project environments successfully.

DS 694 DS Capstone serves as the capstone for the Master of Science in Technology Management: Data Science program, requiring students to complete a faculty-approved project demonstrating professional competence. This 16-week course integrates theoretical knowledge with practical application, allowing students to showcase skills developed throughout their master’s program and work experience. The project contributes to relevant discipline literature and is comparable to professional-level work. Students apply theory and practice in their specific discipline, develop a research proposal and literature review, and either apply the action research model or recommend actions based on case study data. This comprehensive project demonstrates students’ ability to research, analyze, and apply management theories to real-world scenarios, preparing them for advanced roles in their chosen fields.

DS 695 DS Internship offers a structured learning and work experience in a position approved by the School of Business for graduate credit. This internship course aims to bridge academic knowledge with professional practice, allowing students to apply theoretical concepts in real-world settings. The curriculum focuses on developing essential workplace skills such as problem-solving, teamwork, and effective communication. Students have the opportunity to explore various career paths, gain insights into potential job roles, and build professional networks. The internship enhances employability by providing hands-on experience and specific job-related skills. Through reflective practices and feedback, students engage in personal and professional growth, identifying strengths and areas for improvement. This practical experience prepares students for successful entry into the workforce post-graduation.

BA 631 offers a comprehensive exploration of data mining techniques and their business applications. Students learn to extract valuable insights from large datasets using statistical and machine learning algorithms. The course covers various data mining techniques such as clustering, classification, and association rule mining. Students gain proficiency in popular data mining tools like Python, R, and Weka. The curriculum emphasizes evaluating the appropriateness of different algorithms for analyzing large datasets and developing effective data mining workflows. Students learn to assess the effectiveness of data mining techniques and tools through critical analysis and evaluation of results. By the end of the course, students can analyze large datasets to find meaningful patterns that contribute to solving practical organizational problems.

CS 651 provides an in-depth exploration of cloud computing and big data analytics, focusing on designing, deploying, and managing cloud-based big data solutions. The course covers essential topics such as cloud infrastructure, big data platforms, data analysis algorithms, and data security. Students learn to describe fundamental concepts of cloud computing and big data analytics, including key terms and principles. They gain practical experience applying cloud computing technologies to set up virtual machines, storage solutions, and containerized applications. The curriculum emphasizes assessing different cloud computing models and big data processing frameworks, designing scalable solutions for big data management, and evaluating the performance of these solutions. Students also critique security and privacy considerations in cloud-based big data processing, preparing them for real-world challenges in this rapidly evolving field.

DS 600 provides a foundational understanding of data science concepts, techniques, and tools. The course covers data manipulation, analysis, and visualization, along with an overview of machine learning algorithms, data mining, and statistical methods. Students learn to work with real datasets using popular data science tools and programming languages like Python or R. The curriculum emphasizes understanding data science principles, including data manipulation, exploration, and the entire data analysis project lifecycle. Students develop proficiency in data analysis and visualization tools, assess the effectiveness of various statistical methods and machine learning algorithms, and learn to develop innovative solutions to real-world data analysis problems by combining multiple techniques and approaches.

DS 610 explores the ethical, legal, and technical challenges of protecting sensitive information and ensuring data integrity in data science applications. The course covers privacy laws, regulations, and standards globally, as well as techniques for securing data storage, transmission, and processing. Through case studies, practical exercises, and discussions, students learn to implement robust data protection strategies, perform risk assessments, and develop policies that uphold ethical standards and legal compliance in data science projects. The curriculum emphasizes understanding foundational principles of data privacy and security, mastering global privacy laws and regulations, implementing data security measures, and conducting risk assessments for data science projects.

DS 620 offers a comprehensive exploration of statistical concepts, inferential statistics, hypothesis testing, and experimental design principles. Students learn to construct practical experiments, analyze resulting data using statistical software, and interpret findings to make informed decisions. The course bridges theoretical knowledge with real-world application through practical examples and hands-on exercises. Key objectives include developing an advanced understanding of statistical principles, acquiring proficiency in experiment design and analysis, utilizing statistical software for complex data analyses, and enhancing critical thinking and decision-making skills. This course is essential for researchers, data analysts, and professionals involved in data-driven decision-making processes.

DS 630 focuses on bridging the gap between data analysis and storytelling, exploring data visualization principles, design aesthetics, and narrative techniques. Students learn to use various visualization tools and software to create impactful charts, graphs, and interactive dashboards. Through hands-on projects and case studies, the course teaches how to transform data into engaging stories that inform, persuade, and inspire diverse audiences. Key objectives include developing proficiency in data visualization techniques, gaining expertise in data storytelling, acquiring experience with visualization tools, and demonstrating the ability to deliver compelling data-driven narratives that effectively communicate insights to varied audiences.

DS 635 covers the intricacies of gathering, storing, and analyzing business data to drive strategic decisions. The course explores business intelligence (BI) fundamentals, data warehouse architecture and management, and strategic use of data analytics for solving business problems. Students learn to design and implement data warehousing solutions, utilize BI tools for data analysis, and develop strategies to extract actionable insights from large datasets. Key objectives include establishing a solid foundation in BI and data warehousing concepts, developing skills in data warehousing solution design, achieving proficiency in business data analysis, and gaining knowledge in the strategic application of data analytics for identifying business opportunities and formulating growth strategies.