Master of Science in Data Analytics

3.0 Semester Credits

In a dynamic professional world, this course empowers you to take the helm of your data analytics career using Peter Drucker's visionary principles. Discover how to channel self-awareness into a personalized leadership blueprint and resilient career strategy. Delve into the distinctions between management roles to sharpen your organizational influence. Enhance your emotional intelligence for profound professional interactions and leverage Drucker's 'Feedback Analysis' for strategic decision-making. Embrace the art of learning, unlearning, and relearning, to future-proof your career. This course isn't just about finding your path, it's about creating it.

3.0 Semester Credits

This course will introduce the theory and applications of probability and statistics. Topics include fundamental concepts of probability, conditional probability, random variables, common distributions, and statistical inference (estimation, hypothesis testing, and regression). Students will learn many practical skills such as descriptive statistics analysis, A/B testing, data visualization. The emphasis is on developing problem-solving skills and applying key results to business analysis with Excel and Tableau. Students will describe how organizations who use this data and respect human dignity at the same time through data privacy embody the best aspects of Drucker’s ideas of management as a liberal art. Prerequisite: BA501 

3.0 Semester Credits

Structured query language (SQL) is the language of databases. Whether students run reports or collect data for analysis, you need to know SQL to add, delete, edit and view records. This course provides a step-by-step overview and instructions that will help students to get started with SQL language. You will learn how to create SQL statements for data storage, data collection, data computation and reporting. Upon completion of this course, students will be able to manage, query and analyze business datasets by using relational database. Turning to Peter Drucker’s ideas of management as a liberal art, students will also explore ethical issues and learn to define ethical boundaries in analyzing data.

3.0 Semester Credits

This Application Development & Coding for Data Analytics course introduces the programming language, Python, and focuses on foundational programming concepts, data structures, and problem-solving techniques. Students will learn to write efficient code using control structures, functions, and objectoriented programming (OOP) principles to create modular and reusable applications. The course also explores file handling, error management, and database integration, enabling students to develop robust, data-driven applications. Hands-on assignments and projects emphasize real world applications including graphical user interfaces (GUIs) and data processing tools. By the end of the course, students will complete a final project demonstrating their ability to design, implement, and present a fully functional Python application.

3.0 Semester Credits

Unlock the Power of Data! Transform raw information into strategic insights with our 'Data Mining and Visualization' course. Explore Drucker's timeless principles as you journey from understanding business challenges to crafting ethical data analytics strategies. Dive into the core principles of data preparation, employing ETL processes, and ensuring impeccable data quality. Harness the capabilities of advanced analytics methodologies, from descriptive to predictive, using cutting-edge tools to solve intricate problems. Elevate your storytelling skills through the art of data visualization, crafting interactive narratives that drive data driven decisions. Join us in mastering the essentials of business analytics and become the visionary manager of tomorrow.

3.0 Semester credits

Over the past decade, artificial intelligence (AI) and machine learning have seen remarkable growth, particularly in image and speech recognition, and recommendation systems. These advancements have been fueled by improved data gathering, storage, and management capabilities, as well as the need to handle high-dimensional data and uncertainty effectively. However, despite the wealth of available information, drawing accurate conclusions remains a challenge. This course aims to address this challenge by providing a comprehensive overview of model-based and algorithmic machine learning methods. These techniques are not only grounded in theory but are also illustrated through various realworld applications and datasets, allowing you to gain practical insights. Simultaneously, the course offers a solid theoretical foundation, equipping you with the necessary knowledge and tools to navigate the intricate landscape of modern machine learning.

3.0 Semester credits

In this seven-week course, part of the Master’s in Data Analytics Capstone Project, students embark on a journey of professional growth and applied learning. You will engage in a real-world, consulting-style project, selecting a Faculty Sponsor to guide your journey. This course challenges you to integrate and apply the comprehensive data analytics skills acquired throughout your MSDA program. You'll craft a project proposal and outline, honing your ability to negotiate scope, manage deliverables, and collaborate effectively. This experience not only simulates a professional environment but also serves as a stepping stone to producing a master thesis-equivalent report, showcasing your ability to tackle complex data analysis problems in a business context.

3.0 Semester credits

In this intensive seven-week course, the second part of the Data Analytics Capstone Project, students immerse themselves in advanced research, analysis, and reporting, mirroring a professional consulting assignment. This phase challenges you to apply the comprehensive data analytics skills acquired in your MSDA program to a real world project. You'll engage in rigorous data collection and analysis, craft an interim report, and culminate with a polished final Capstone Project Report. This experience not only solidifies your understanding of data science, AI, and visualization but also fosters a collaborative dynamic with your Faculty Sponsor, culminating in a sophisticated, thesis-like deliverable that demonstrates your mastery of data analytics in a practical business context. Prerequisite: MDA691

3.0 Semester credits

The purpose of this course is to offer students an opportunity to extend their data mining and visualization skills garnered from the “Part I” course. Specifically, this elective will provide exposure to a wider set of more advanced data analytic and mining skills and techniques as well as more practice in advanced visualization and “storytelling” with data. By taking this deeper mining and visualization “Part II” course, students will apply skills from the “Part I” course in new ways to tackle more advanced managerial challenges and decisions that are more complex. Main topics of this course include best practices for data California Institute of Advanced Management │ 74 extraction, transformation and loading processes, various data analytic techniques and basic concepts of data visualization and storytelling. Prerequisite: MDA601

3.0 Semester credits

This “Part II” course extends and expands upon key frameworks, learnings, and applications from the “Part I” course. Students will be exposed to advanced/professional artificial intelligence applications—including benefits and limitations—as well as machine learning best practices for use in professional scientific, industry, and research environments. With a focus on practical applications with real results that can inform improved decision making, students will also conduct independent research and (perhaps) consulting to their firms and/or clients using course tools to drive to better managerial insights and upward reporting of the same. The course culminates in an individual project report and presentation that is suitable for employer and/or client consumptions utilizing advanced AI and ML tools but explained in a way that business leaders and owners can make quicker and better practical decisions. Not only will advanced discrete analyses be covered but so will optimization and simulation frameworks as well as cloud computing and robotics applications. Finally, societal concepts such as AI ethics and privacy, as well as machine learning data “farming” and “intrusion” will be discussed, and students will be challenged to take positions on these in light of results from their own project analyses. Prerequisite: MDA610