General education courses are the foundation of the University’s outcomes and attributes for its graduates.

• ENG 1150 Composition I — 3 credit hours
• ENG 1200 Composition II — 3 credit hours
• MTH 1150 College Algebra or MTH 1100 Finite Math — 3 credit hours
• SPC 2050 Speech — 3 credit hours
• NSC 2100 Environmental Science or Natural Science — 3 credit hours
• HIS 2100 Foundations of the Modern World I or History Elective — 3 credit hours
• MTH 2310 Statistics I — 3 credit hours

• ACC 2410 Fundamentals of Financial Accounting — 3 credit hours
• ACC 2415 Fundamentals of Managerial Accounting — 3 credit hours
• ECN 2210 Principles of Microeconomics — 3 credit hours
• ECN 2220 Principles of Macroeconomics — 3 credit hours
• LAW 3000 Business Law I — 3 credit hours
• MGT 2300 Principles of Management — 3 credit hours
• MIS 1500 Business Productivity Software — 3 credit hours
• MKT 2080 Principles of Marketing — 3 credit hours
• FIN 3010 Financial Management — 3 credit hours
• MGT 4250 Organizational Behavior — 3 credit hours
• MGT 4800 Strategic Planning — 3 credit hours
• PHL 3100 Ethics — 3 credit hours

• ECN 4010 Economics of Public Policies — 3 credit hours
• PHL 4100 Philosophy of American Enterprise — 3 credit hours

• MIS 1250 Foundations of Data Analytics I — 3 credit hours
• OPS 1100 Intro to Operations Management — 3 credit hours
• MIS 1350 Foundations of Data Analytics II — 3 credit hours
• OPS 1200 Business Process Management — 3 credit hours
• MIS 2140 Programming I — 3 credit hours
• MIS 2100 Visual Analytics for Business Intelligence — 3 credit hours
• MIS 3200 Database Design and Implementation — 3 credit hours
• MTH 3340 Statistics II — 3 credit hours
• CS 3140 Programming for Data Analysis — 3 credit hours
• MTH 2870 Linear Algebra and Matrix Theory — 3 credit hours
• MTH 3150 Data Science for Informed Decision Making — 3 credit hours
• MTH 3450 Data Mining for Business Analytics — 3 credit hours
• MIS 3350 Adv. Database Mgt. & Visualizations for Analytics — 3 credit hours
• MTH 4500 Forecasting and Simulation Techniques for Business Enterprise — 3 credit hours
• MIS 4100 Machine Learning Techniques for Applied Predictive Modeling — 3 credit hours
• MIS 4300 Data Analytics Capstone Project — 3 credit hours
• MIS 4200 Essentials of Big Data & Cloud Computing — 3 credit hours

This course builds on the rudimentary concepts learned in BSA 1200. Students will be provided with the skillsets of a seasoned Tableau power user. They will be exposed to the professional desk tools typical of solid working experience with Tableau. Course topics include a review of creating and connecting to data sources, developing data subsets, executing Tableau calculations, performing advanced table calculations, creating and using parameters, data extraction, comparing measures, Tableau geocoding, viewing distributions, basic statistics and forecasting, story-telling using dashboards. A student who completes this course will have the intermediate Tableau skill level consistent with the fundamentals of Tableau Desktop II.

This course explores best practices for designing visualizations that viewers can easily understand and use. Upon completion of this course, students will be able to formulate strategic steps to optimize visual analytics processes, effectively deploy pre-attentive attributes in visualizations, propose design visualizations that minimize the risk of misleading consumers in hunt for data insights, and effectively use charts to answer specific questions. They will also be able to describe the process of identifying visual best practices for dashboard and story design. A student who completes this course will be prepared to complete the certification exam to qualify as a Tableau Desktop Qualified Associate. It will also provide a solid foundation for students to aspire to complete the certification exam for Tableau Desktop Certified Professional as well.

This course will guide students in a hands-on environment that facilitates a zero to hero transition in Python programming. Content ideas will advance on the fundamentals of Python programming principles learned in MIS 2140. Specific content areas for this course are working with various data formats within python to include MS Excel Worksheets, XML, HTML and JSON files, deploying the NumPy and Pandas libraries to create, structure and manipulate data arrays, as well as the Matplotlib and Seaborn modules for data plotting and visualization. The skills, tools and best practices used to effectively manage data cleaning, preparation and wrangling will be discussed.

In this class, students will explore how to combine business domain knowledge with the fundamental principles of data science in formulating a data-driven strategy to meet set objectives. The underlying conceptual processes inherent in the industry-standard CRISP-DM model, namely the systematic progression from business understanding to data understanding, preparation, modeling and deployment are examined. Real-life scenarios that differentiate between supervised and unsupervised methods for data mining and applied predictive modeling will be considered. Key characteristics associated with deployed analytic models such as generalization and overfitting are introduced. Topics related to responsible data science and ethical practice will also be covered to include transparency, explainability and fairness.

Structured Query Language (SQL) continues to rank as one of the most-used computer languages in the world. To better position careers in the business world, students taking this course will build on the SQL skills acquired in MIS 3200 to provide database solutions. Through intensive hands-on learning sessions, best practices for defining structures that hold data so that relationships in the relevant data features can be analyzed to bear on business decisions will be learned. Key MySQL ideas will be emphasized to empower students to create, modify, explore and summarize data beyond the limits imposed by Microsoft Excel and Access.

To enhance students’ preparation for the responsibility of making strategic, tactical and operational decisions, this course examines the various techniques and methodologies for mining non-random patterns in data to create business value. Highlight content areas that are covered include an overview of the data mining process, what initial data exploration entails, dimension reduction techniques, prediction and classification methods, mining relationships among records, social network analytics and text analytics. Through the lens of business case studies, students will gain perception into making choices about algorithms for a given problem statement, while balancing this process out with an analysis of the pros/cons of each mining algorithm that can be used.

This course provides a comprehensive introduction to forecasting methods used to predict data at future times from past, collected data. Among other possible use cases, students will be provided with a strong background in how to use forecasting skills to aid retail stores predict sales, energy companies forecast reserves, demand and prices, educational institutions forecast enrollment and governments forecast tax receipts and spending. Emphasis will be laid on how to uncover patterns in past, numerical data that are expected to carry into the future data by deploying quantitative forecasting techniques like exponential smoothing, regression forecasting, hierarchical forecasting and practical forecasting issues. Other vital areas that will be covered will involve learning how to combine forecasts, handle complicated seasonality patterns, dealing with hourly, daily and weekly data, forecasting count time series and other examples. Through this experience, students will sharpen their readiness for applying analytic tools that businesses optimize for future strategic tactical and operational planning and minimization of uncertainties.

To minimize uncertainties in business decisions and planning, this course studies ways that machine learning algorithms are being deployed towards optimal extraction of truths embedded in data. Case studies will be used to introduce, explain, illustrate and contextualize the utility of machine learning algorithms. Algorithms covered will center on information, similarity, probability and error-based machine learning techniques to harness and leverage the value of predictive modeling. Students will develop a deep appreciation of the three foundational pillars of data analytics, namely descriptive, predictive and prescriptive analytics as it applies to the real world of data analytics-be it customer, finance, marketing, healthcare, supply chain, human resource, government and sports analytics, to name but a few.

The capstone experience for the Data Analytics program is designed as a practitioner’s domain for synthesizing concepts learned in the program to extract actionable insights from real-life. Real-life data will be sourced from reputable data repositories. To analyze the sourced data sets, students will combine analytical methods learned during the program with stimulating ideas gleaned from completed public domains like Kaggle, GitHub and BitBucket on best practices for effectively managing an analytics project from its inception to a successful closure. Guided by the CRISP-DM model, students will synthesize database management, data visualization, machine learning, project management, consultative communication and interpersonal skills to see a data analytics project through its life cycle. Completed capstone projects will be documented in GitHub or Bitbucket as reference projects to demonstrate student’s preparedness for the corporate world.