Master of Science in Data Science

This curriculum map illustrates the progressive development of students' competencies across the Master of Science in Information Technology Management program, showing how each course introduces (I), develops (D), or brings students to mastery (M) of the seven Program Learning Outcomes (PLOs), culminating in the capstone course where students demonstrate mastery of all outcomes.

• TECH 500: Ethical Challenges in Technology Management
• BUS 5203: Data Analytics
• BUS 5213: Processing Data for Decision-Making
• TECH 575: Big Data Analytics for IoT
• TECH 615: Applied AI: Solutions for Business
• TECH 630: Advanced AI for Business Insights and Decision-Making
• BUS 5223: Leading Data Analytics Projects
• TECH 643: Statistical Methods
• TECH 674: Quantitative Methods
• TECH 699: Data Science and Analytics Capstone Project

Core Classes

TECH 500: Ethical Challenges in Technology Management

This course focuses on preparing leaders to resolve complex ethical dilemmas in technology management. The course emphasizes Biblical values and practical solutions to contemporary challenges. Students explore ethical systems through a Christian worldview, analyze case studies, and develop skills to make sound moral judgments. By the course's end, participants will be equipped to address ethical issues in technology leadership with integrity and a faith-based perspective.

Course Student Learning Outcomes

• SLO 1: Analyze complex ethical dilemmas in technology management using various ethical frameworks, including a Christian worldview. (PLO 3, PLO 4)
• SLO 2: Evaluate the implications of emerging technologies on ethical decision-making in IT leadership roles. (PLO 3, PLO 4)
• SLO 3: Synthesize Biblical principles with contemporary ethical challenges to develop faith-based solutions in technology management. (PLO 3, PLO 5)
• SLO 4: Develop and articulate sound moral judgments for case studies in technology ethics, demonstrating critical thinking and effective communication. (PLO 1, PLO 3)
• SLO 5: Create a personal ethical framework for addressing technology management challenges that integrate professional standards with Christian values. (PLO 3, PLO 5)

BUS 5203: Data Analytics

Students will be exposed to data analytic practices in the business world such as how data is created, stored, and accessed, and how organizations utilize data and create environments that encourage analytics.

Course Student Learning Outcomes

• SLO 1: Understand the analytics mindset for business analysts. (PLO 2, PLO 4)
• SLO 2: Understand the basic concepts of statistics and data analytics. (PLO 2, PLO 4)
• SLO 3: Apply Data Analytics techniques to answer questions about the data set. (PLO 4)
• SLO 4: Analyze business decisions using data analytics techniques. (PLO 4)
• SLO 5: Evaluate ethical decisions in data analytics with faith integration. (PLO 5)
• SLO 6: Create and complete a data analytics project to answer an original question in a specific discipline. (PLO 2, PLO 4, PLO 5)

BUS 5213: Processing Data for Decision-Making

Understand how to collect and utilize data in decision-making using analytic techniques (data mining, predictive analytics, and machine learning algorithms) to find patterns of relationships between data elements. Students will learn how to gather appropriate data and analyze it to lead decision-makers to an enhanced understanding of the data and its management application.

Course Student Learning Outcomes

• SLO 1: Gain information management skills to manage data. (PLO 2, PLO 4)
• SLO 2: Gain analytics skills and tools to understand the data. (PLO 2, PLO 4)
• SLO 3: Gain an understanding of data-driven decision-making and how to deal with uncertainty. (PLO 2, PLO 4)
• SLO 4: Develop a data-oriented mindset to help businesses act on the data. (PLO 2, PLO 4)
• SLO 5: Develop skills in presenting data for decision-making. (PLO 1, PLO 2)

TECH 575: Big Data Analytics for IoT

This course introduces students to Apache Spark, a powerful big data processing framework, with a focus on its application in analyzing large-scale datasets. Students will learn to leverage Spark's capabilities using Python, emphasizing the latest Spark 2.0 DataFrame syntax. The curriculum covers advanced data manipulation techniques, machine learning applications using MLlib, and real-world problem-solving scenarios.

Student Learning Outcomes

• SLO 1: Synthesize Python programming and Apache Spark frameworks to design and implement advanced big data analysis solutions. (PLO 2, PLO 4)
• SLO 2: Evaluate and apply Spark 2.0 DataFrame syntax to optimize complex data processing tasks and improve analytical efficiency. (PLO 2, PLO 4)
• SLO 3: Create and critique sophisticated machine learning models using Spark's MLlib, including logistic regression, random forests, and gradient-boosted trees, to solve real-world classification problems. (PLO 2, PLO 4)
• SLO 4: Develop and assess innovative natural language processing applications, such as spam filters, utilizing Spark's capabilities for text analysis and classification. (PLO 1, PLO 2, and PLO 4)
• SLO 5: Formulate an ethical framework for big data analytics that integrates Christian principles of stewardship and privacy, critically examining the societal implications of large-scale data analysis techniques. (PLO 3, PLO 5)

TECH 615: Applied AI: Solutions for Business

This course provides a comprehensive introduction to Artificial Intelligence (AI), exploring its transformative impact across industries and addressing the growing global demand for AI skills. Students will delve into recent developments in Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision, and Robotics while gaining hands-on experience with modern deep learning frameworks like Keras.

Student Learning Outcomes

• SLO 1: Evaluate the impact of AI on various industries, analyzing current trends and predicting future developments in the field. (PLO 2, PLO 4)
• SLO 2: Design and implement artificial neural networks to solve complex business problems, such as customer churn prediction and stock price forecasting. (PLO 2, PLO 4)
• SLO 3: Develop advanced AI models using convolutional and recurrent neural networks for image recognition and time-series analysis in real-world business contexts. (PLO 2, PLO 4)
• SLO 4: Create and assess recommender systems and natural language processing applications, demonstrating proficiency in applying AI to enhance customer experience and business operations. (PLO 2, PLO 4)
• SLO 5: Synthesize ethical considerations in AI implementation with Christian principles of stewardship and human dignity, formulating responsible AI strategies for business applications. (PLO 3, PLO 5)

TECH 630: Advanced AI for Business Insights and Decision-Making

This course offers a transformative perspective on AI's impact in the business realm, emphasizing the critical role of AI proficiency, including generative AI like Large Language Models, in today's information-driven economy. It focuses on identifying, evaluating, and leveraging opportunities for business analytics using both proprietary and public data sources.

Student Learning Outcomes

• SLO 1: Synthesize complex data sets to create innovative business solutions, demonstrating advanced analytical capabilities in AI-driven contexts. (PLO 2, PLO 4)
• SLO 2: Evaluate current trends in AI management and application, critiquing their potential impact on various business sectors. (PLO 2, PLO 4)
• SLO 3: Design and critically assess diverse AI and data mining models, justifying their appropriateness for specific business scenarios. (PLO 2, PLO 4)
• SLO 4: Formulate collaborative strategies to translate real-world business challenges into actionable AI models, demonstrating teamwork and problem-solving skills. (PLO 2, PLO 5)
• SLO 5: Develop and defend efficient business analytics strategies, integrating AI technologies to address contemporary business issues. (PLO 2, PLO 4)
• SLO 6: Create an ethical framework for AI implementation in business that aligns with Christian principles of stewardship and social responsibility, critically examining the moral implications of AI-driven decision-making in organizational contexts. (PLO 3, PLO 5)

BUS 5223: Leading Data Analytics Projects

This course will expose students to critical components of operationalizing business intelligence and data analytics for improved decision-making and quality improvement within an organization. Specifically, students will learn how to take on the role of business intelligence consultant and apply data analytics techniques to inform business decision-making.

Student Learning Outcomes

• SLO 1: Understand key terms and concepts in the field of data analytics. (PLO 2, PLO 4)
• SLO 2: Identify key analytical skills necessary in the profession. (PLO 2, PLO 4)
• SLO 3: Present data in a graphic representational manner. (PLO 1, PLO 2)
• SLO 4: Apply concepts and techniques of business analytics. (PLO 2, PLO 4, PLO 5)

TECH 643: Statistical Methods

This course offers a comprehensive exploration of fundamental and advanced statistical techniques essential for data analysis and decision-making in various fields. This course covers descriptive statistics, probability theory, sampling distributions, hypothesis testing, and inferential statistics. Students will delve into regression analysis, including simple and multiple linear regression, as well as an introduction to logistic regression. The curriculum also encompasses analysis of variance (ANOVA), design of experiments, and non-parametric methods. Throughout the course, emphasis is placed on both theoretical understanding and practical application using statistical software such as R or SAS. Students will work with real-world datasets to develop skills in data manipulation, statistical modeling, and result interpretation. By the end of the course, participants will be equipped with a robust statistical toolkit and the ability to select and apply appropriate methods to address complex analytical challenges across diverse disciplines. Prerequisites include a basic understanding of algebra and elementary statistical concepts.

Student Learning Outcomes

• SLO 1: Demonstrate proficiency in applying fundamental and advanced statistical techniques for data analysis and decision-making across various fields. (PLO 2, PLO 4)
• SLO 2: Conduct and interpret hypothesis tests and inferential statistics accurately. (PLO 2, PLO 4)
• SLO 3: Perform regression analyses, including simple and multiple linear regression, and logistic regression. (PLO 2, PLO 4)
• SLO 4: Apply analysis of variance (ANOVA), design of experiments, and non-parametric methods to appropriate datasets. (PLO 2, PLO 4)
• SLO 5: Integrate Christian principles of ethics and stewardship in the application of statistical methods, recognizing the responsibility to use data analysis for the betterment of society and by Biblical values. (PLO 3, PLO 5)

TECH 674: Quantitative Methods

This course provides a comprehensive introduction to essential quantitative methods and statistical techniques used in modern data science. Students will develop a strong foundation in probability theory, statistical inference, and advanced analytical approaches crucial for analyzing complex datasets. Key topics include probability distributions and their applications, hypothesis testing and confidence intervals, linear and nonlinear regression analysis, time series analysis and forecasting, Bayesian statistics and inference, dimensionality reduction techniques, clustering and classification methods, resampling methods, and bootstrapping. Through a combination of lectures, hands-on exercises, and real-world case studies, students will learn to apply these quantitative methods using popular data science tools and programming languages. The course emphasizes both theoretical understanding and practical implementation, preparing students to tackle complex data analysis challenges in various industries.

Student Learning Outcomes

• SLO 1: Apply probability theory and statistical inference techniques to analyze complex datasets in data science contexts. (PLO 2, PLO 4)
• SLO 2: Develop and evaluate linear and nonlinear regression models, time series analysis, and forecasting methods for data analysis and prediction. (PLO 2, PLO 4)
• SLO 3: Utilize dimensionality reduction, clustering, and classification methods to extract meaningful patterns from high-dimensional data. (PLO 2, PLO 4)
• SLO 4: Demonstrate proficiency in using popular data science tools and programming languages to implement quantitative methods on real-world datasets. (PLO 2, PLO 4, PLO 5)
• SLO 5: Integrate Christian principles of ethical data use and interpretation, recognizing the responsibility to employ quantitative methods in ways that honor truth, promote human flourishing, and reflect good stewardship of information resources. (PLO 3, PLO 5)

TECH 699: Data Science and Analytics Capstone Project

This capstone course provides students with the opportunity to synthesize and apply the knowledge and skills acquired throughout the Master of Science in Data Science and Analytics program. Students will undertake a comprehensive, real-world data science project that addresses a significant business or societal challenge. Working individually or in small teams, students will identify a problem, collect and analyze relevant data, develop and implement appropriate data science solutions, and communicate their findings effectively. The project will encompass the entire data science lifecycle, including problem formulation, data acquisition and preprocessing, exploratory data analysis, model development and evaluation, and the presentation of results. Students will be expected to integrate advanced analytics techniques, ethical considerations, and business insights into their projects. The course will culminate in a final presentation and report, demonstrating the student's mastery of data science concepts and their ability to deliver value through data-driven solutions.

Student Learning Outcomes

• SLO 1: Design and execute a comprehensive data science project that addresses a complex real-world problem, demonstrating mastery of the data science lifecycle and advanced analytical techniques. (PLO 2, PLO 4)
• SLO 2: Effectively communicate complex data science concepts, methodologies, and results to both technical and non-technical audiences through written reports, oral presentations, and data visualizations. (PLO 1, PLO 4)
• SLO 3: Apply ethical reasoning and Christian principles in the design, implementation, and evaluation of data science solutions, addressing issues such as data privacy, bias, and societal impact. (PLO 3, PLO 5)
• SLO 4: Critically evaluate and select appropriate data science methodologies, tools, and technologies to solve specific business or societal challenges, justifying these choices based on their effectiveness and efficiency. (PLO 2, PLO 4)
• SLO 5: Collaborate effectively in diverse teams to plan, execute, and deliver a complex data science project, demonstrating leadership, project management, and cross-cultural communication skills. (PLO 1, PLO 5)
• SLO 6: Synthesize insights from data analysis to develop strategic recommendations that drive business value or address societal needs, demonstrating the ability to bridge data science with practical applications. (PLO 2, PLO 3, PLO 4)

Institutional Learning Outcomes (ILOs)

• ILO 1: Students will communicate effectively.
• ILO 2: Students will use methods of inquiry for knowledge acquisition and application.
• ILO 3: Students will address concrete problems by applying faith and ethical reasoning.
• ILO 4: Students will think creatively and critically to pursue a life of learning.
• ILO 5: Students will engage in a culturally diverse world to strengthen relationships with others.

Program Learning Outcomes (PLOs)

• PLO 1: Effectively communicate complex data science concepts and analytical results to diverse audiences, demonstrating cultural sensitivity and ethical consideration in data presentation. (ILO 1, ILO 3, ILO 5)
• PLO 2: Apply advanced statistical methods, machine learning techniques, and data mining strategies to extract meaningful insights from large-scale datasets, critically evaluating the results to solve real-world problems. (ILO 2, ILO 4)
• PLO 3: Develop and implement ethical data science solutions that integrate Christian principles of stewardship, privacy, and social responsibility, while effectively communicating the ethical implications to stakeholders. (ILO 1, ILO 3, ILO 5)
• PLO 4: Critically evaluate and synthesize current trends in data science and AI, demonstrating the ability to adapt to rapidly evolving technologies and methodologies, and effectively communicate findings to promote continuous learning. (ILO 1, ILO 2, ILO 4)
• PLO 5: Collaborate effectively in diverse teams to design and execute data science projects that address global challenges, utilizing appropriate inquiry methods and ethical reasoning to promote cross-cultural understanding through data-driven insights. (ILO 2, ILO 3, ILO 5)