Graduate Business Analytics Course Descriptions

DS-501. Comm. for Data Science Practitioners. 0.00 Credits.

Communication for Data Science Practitioners is intended to provide support and tailored instruction specific to multilingual graduate students in the Data Science program who speak a language other than English as a first language (L1). The course is designed to provide an intensive and focused hybrid experience for students that will effectively prepare students for discipline-specific graduate coursework delivered in English. DS-501 offers direct English-language vocabulary and advanced grammar instruction, but combines ESOL course content with a deep focus on explicitly preparing students for the tasks they must complete as both graduate students and practitioners in their field. Coursework is steeped in a content & language integrated learning approach, and the course is meant to be paired with DS-520. DS-501 is a hybrid course, with both virtual and in-person course meetings. The course is designed as a 0-credit experience, does not contribute towards visa eligibility, and is delivered as a supportive add-on for multilingual learners at the graduate level. This course is graded on a pass/fail basis, but student grades will appear on their transcripts.

DS-510. Introduction to Data Science. 3.00 Credits.

Data Science is a set of fundamental principles that guide the extraction of valuable information and knowledge from data. This course provides an overview and develops student's understanding of the data science and analytics landscape in the context of business examples and other emerging fields. It also provides students with an understanding of the most common methods used in data science. Topics covered include introduction to predictive modeling, data visualization, probability distributions, Bayes' theorem, statistical inference, clustering analysis, decision analytic thinking, data and business strategy, cloud storage and big data analytics.

DS-520. Data Analysis and Decision Modeling. 3.00 Credits.

This course will provide students with an understanding of common statistical techniques and methods used to analyze data in business. Topics covered include probability, sampling, estimation, hypothesis testing, linear regression, multivariate regression, logistic regression, analysis of variance, categorical data analysis, Bootstrap, permutation tests and nonparametric statistics. Students will learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines.

DS-530. Data Management Systems. 3.00 Credits.

This course explores foundational concepts of relational databases, data warehousing, distributed data management, structured and unstructured data, NoSQL data stores and graph databases. Various database concepts are discussed including Extract-Transform-Load, cloud-based online analytical processing (OLAP), data warehouse architecture, development and planning, physical database design, data pipelines, metadata, data provenance, trust and reuse. Students will develop practical experience using SQL. Prerequisites: DS-510 AND DS-520.

DS-533. Enterprise Design Thinking. 3.00 Credits.

Students will learn a robust framework for applying design thinking techniques to key issues facing organizations across industries. Key skills developed include shared goal setting and decision-making, processes for continuous innovation, and the alignment of multi-disciplinary teams around the real needs and experiences of users and customers. Through instruction, experiential learning and an industry-recognized methodology, students will gain practice in the successful application of design thinking techniques to address common business problems.

DS-540. Statistical Programming. 3.00 Credits.

The course gives an introduction to SAS or R programming for statistical analyses and managing, analyzing and visualizing data. Topics include numeric and non-numeric values, arithmetic and assignment operations, arrays and data frames, special values, classes and coercion. Students will learn to write functions, read/write files, use exceptions, measure execution times, perform sampling and confidence analyses, plot a linear regression. Students will explore tools for statistical simulation, large data analysis and data visualization, including interactive 3D plots.

DS-542. Python in Data Science. 3.00 Credits.

The course gives an introduction to Python programming for statistical analyses and managing, analyzing and visualizing data. Topics include numeric and non-numeric values, arithmetic and assignment operations, arrays and data frames, special values, classes and coercion. Students will learn to write functions, read/write files, use exceptions, measure execution times, perform sampling and confidence analyses, plot a linear regression. Students will explore tools for statistical simulation, large data analysis and data visualization, including interactive 3D plots. Prerequisites: DS-510, DS-520.

DS-560. Biomedical Data Analytics. 3.00 Credits.

An introduction to the biology of modern genomics and some of the tools that are used to measure it. This will include basic molecular biology, the genome, DNA and RNA sequences, and the central dogma. Students will learn techniques to analyze data from sequencing experiments. The course covers data analytic techniques to understand and analyze the biomedical data available to bioscientists and the medical profession. Prerequisites: CS-241, BI-183.

DS-570. Healthcare Data Analytics. 3.00 Credits.

An introduction to the healthcare environment and the various sources of healthcare data. How to import, clean, and refine data from these sources. Students will learn the techniques to diagnose diseases, predict prognosis and evaluate treatments. The course covers data analytic techniques to understand and analyze healthcare data. Prerequisites: CS-241, BI-183.

DS-589. Topics in Management. 3.00 Credits.

Topics vary by term. Example topics may include but are not be limited to the following: advanced project management techniques; non-profit, philanthropic, and/or faith-based management; coding fundamentals for entrepreneurs, managers, and executives; and mindfulness in the workplace.

DS-590. Data Structures and Algorithms I. 3.00 Credits.

This course explores essential topics for programmers and data scientists including the design of and implementation and analysis of efficient algorithms and their performance. Essential data structures are also reviewed, as well as searching and sorting algorithms.

DS-596. Graduate Research Assistantship. 0.00 Credits.

Graduate Research Assistantship is a robust learning experience for pre-selected students, involving scholarly research under faculty supervision. These research projects involve the development of theoretical analyses and models, gathering and analysis of data, and special projects that require substantive research. The ultimate goals for this research is academic conference presentation, publication in peer-reviewed journals and research reports, and more broadly contributing to thought leadership of the Data Science Institute.

DS-597. Applied Research Experience. 0.00 Credits.

The Applied Research Experience is a learning experience that gives Data Science Institute students the opportunity to conduct real-world consulting and research projects with businesses and organizations, that build upon the science, theory, and application of data and analysis. This non-credit course fulfills the business experience requirement for the program for those students who do not have a current work role that fulfills the requirement. For Traditional/Full-time programs. Prerequisites: DS-510 DS-520 DS-530 DS-542 DS-600 DS-620:.

DS-598. Applied Industry Experience. 0.00 Credits.

The Applied Industry Experience course is an academic component that accompanies students' industry experience in a full time role or internship. Students whose current industry role has been approved by the Academic Program Director as directly related to their program of study can register for this non-credit course each term during which they are working. Prerequisites: DS-510 DS-520 DS-530 DS-542 DS-600 DS-620.

DS-599. Research Practicum. 0.00 Credits.

The Research Practicum is a learning experience that gives the students the opportunity to conduct real-world consulting projects with businesses that build upon the science, research and application of data and analysis, extending to strategic planning and identifying relevant tactics to carry out strategies. For Professional Hybrid programs.

DS-600. Data Mining. 3.00 Credits.

Data mining refers to a set of techniques that have been designed to efficiently find important information or knowledge in large amounts of data. This course will provide students with understanding of the industry standard data mining methodologies, and with the ability of extracting information from a data set and transforming it into an understandable structure for further use. Topics covered include decision trees, classification, predictive modeling, association analysis, statistical modeling, Bayesian classification, anomaly detection and visualization. The course will be complemented with hands-on experience of using advanced data mining software to solve realistic problems based on real-world data. Prerequisites: DS-510, DS-520.

DS-605. Financial Computing and Analytics. 3.00 Credits.

This course covers the process of collecting data from a variety of sources and preparing it to allow organizations to make data-driven decisions. It builds upon the relationships within data collected electronically and applies quantitative techniques to create predictive spreadsheet models for financial decision making. Prerequisites: DS-510, DS-520.

DS-610. Big Data Analytics. 3.00 Credits.

Big Data (Structured, semi-structured, & unstructured) refers to large datasets that are challenging to store, search, share, visualize, and analyze. Gathering and analyzing these large data sets are quickly becoming a key basis of competition. This course explores several key technologies used in acquiring, organizing, storing, and analyzing big data. Topics covered include Hadoop, unstructured data concepts (key-value), Map Reduce technology, related tools that provide SQL-like access to unstructured data: Pig and Hive, NoSQL storage solutions like HBase, Cassandra, and Oracle NoSQL and analytics for big data. A part of the course is devoted to public Cloud as a resource for big data analytics. The objective of the course is for students to gain the ability to employ the latest tools, technologies and techniques required to analyze, debug, iterate and optimize the analysis to infer actionable insights from Big Data. Prerequisites: DS-510, DS-520, DS-530.

DS-620. Data Visualization. 3.00 Credits.

Visualization concerns the graphical depiction of data and information in order to communicate its contents and reveal patterns inherent in the data. It is sometimes referred to as visual data mining, or visual analytics. Data visualization has become a rapidly evolving science. This course explores the underlying theory and practical concepts in creating visual representations of large amounts of data. Topics covered include data representation, information visualization, real-time visualization, visualization toolkits including Tableau and their applications to diverse data rich contexts. At the end of the course, the student will be able to present meaningful information in the most compelling and consumable fashion. Prerequisites: DS-510, DS-520.

DS-621. Business Analytics With Power BI. 3.00 Credits.

This course will focus on building dynamic dashboard and applications in order to understand and interpret the data by using PowerBI. Course will also focus on visualization and business intelligence techniques to interpret the data as step towards Machine Learning. Prerequisites: DS-510 DS-520. Prerequisites: DS-510, DS-520.

DS-630. Machine Learning. 3.00 Credits.

Machine learning is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. Topics include decision tree learning, parametric and non-parametric learning, Support Vector Machines, statistical learning methods, unsupervised learning, reinforcement learning and the Bootstrap method. Students will have an opportunity to experiment with machine learning techniques and apply them to solve a selected problem in the context of a term project. The course will also draw from numerous case studies and applications, so that students learn how to apply learning algorithms to build machine intelligence. Prerequisites: DS-510, DS-520, DS-530, DS-542.

DS-631. Deep Learning Algorithms. 3.00 Credits.

Machine learning is the science (and art) of programming computers so they learn from data. It is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for neural networks and deep learning. Major topics neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, and implementation of deep learning in TensorFlow. Students will have an opportunity to experiment with advanced machine learning techniques (especially using Python) and apply them to solve selected problems in the context of a term project. Prerequisites: DS-630.

DS-637. Luster Analysis With Machine Learning. 3.00 Credits.

In this course, students will utilize machine learning techniques to generate business intelligence through the discovery of patterns and relationships in data. In particular, students will apply cluster analysis, or clustering this method of unsupervised learning and technique for statistical data analysis groups objects based on characteristics, such as high intra-cluster or low inter-cluster similarities. Pre-requisites: DS-542 and DS-630 Prerequisites: DS-542 DS-630.

DS-640. Predictive Analytic & Financial Modeling. 3.00 Credits.

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. This course will provide predictive analytics foundational theory and methodologies as well as teach students how to build predictive models for practical financial and business applications and verify model effectiveness. Topics covered are linear modeling and regression, nonlinear modeling, time series analysis and forecasting, segmentation and tree models, support vector machine, clustering, neural networks and association rules. Prerequisites: DS-510, DS-520.

DS-642. Advance Python in Data Science. 3.00 Credits.

This course explores essential advanced Python topics for programmers & data scientists including working with databases using Python, writing web services, exploring unit-testing frameworks, understanding multithreading concepts in Python, performing advanced statistical analysis using Python libraries and learning industry standards for writing and organizing large Python programs. Prerequisites: DS-510, DS-520, DS-542.

DS-650. Data Ethics and Artificial Intelligence. 3.00 Credits.

The increasing use of big data and artificial intelligence in our society raises legal and ethical questions. This course explores the issues of privacy, data protection, non-discrimination, equality of opportunities and due process in the context of data-rich environments. It analyzes ethical and intellectual property issues related to data analytics with the use of artificial intelligence. Students will also learn the legal obligations in collecting, sharing and using data, as well as the impact of algorithmic profiling, industrial personalization and government. This course also provides an understanding of the important capabilities of business with the technologies that enable them and the management of artificial intelligence. Prerequisites: DS-510, DS-520. Prerequisites: DS-510, DS-520.

DS-660. Business Analytics. 3.00 Credits.

Business analytics is the process of generating and delivering the information acquired that enables and supports an improved and timely decision process. The aim of this course is to provide the student with an understanding of a broad range of decision analysis techniques and tools and facilitate the application of these methodologies to analyze real-world business problems and arrive at a rational solution. Topics covered include foundations of business analytics, descriptive analytics, predictive analytics, prescriptive analytics, and the use of computer software for statistical applications. The course work will provide case studies in Business Analytics and present real applications of business analytics. Students will work in groups to develop analytic solutions to these problems. Prerequisites: DS-510, DS-520 OR MS-500:.

DS-665. Advanced Machine Learning. 3.00 Credits.

Machine learning is the science (and art) of programming computers so they learn from data. It is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for neural networks and deep learning. Major topics neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, and implementation of deep learning in TensorFlow. Students will have an opportunity to experiment with advanced machine learning techniques (especially using Python) and apply them to solve selected problems in the context of a term project. Prerequisites: DS-510, DS-520 AND DS-630.

DS-670. Capstone: Big Data & Data Science. 3.00 Credits.

This course is structured as a capstone research practicum where students have an opportunity to apply the knowledge acquired in data science to interdisciplinary problems from a variety of industry sectors. Students work in teams to define and carry out an analytics project from data collection, processing and modeling to designing the best method for solving the problem. The problems and datasets used in this practicum will be selected from real world industry or government settings. At the end of the class students will write a report that presents their project, the approach and techniques used to design a solution, followed by results and conclusion. Students are encouraged to present their capstone research at conferences. Prerequisites: DS-620, DS-630; Course Type(s): Capstone.

DS-671. Capstone: Business Analytics. 3.00 Credits.

This course is structured as a capstone research practicum where students have an opportunity to apply the knowledge acquired in data science to interdisciplinary problems from a variety of industry sectors. Students work in teams to define and carry out an analytics project from data collection, processing and modeling to designing the best method for solving the problem. The problems and datasets used in this practicum will be selected from real world industry or government settings. At the end of the class students will write a report that presents their project, the approach and techniques used to design a solution, followed by results and conclusion. Students are encouraged to present their capstone research at conferences. Prerequisites: DS-520, DS-620 Prerequisites: DS-520, DS-620; Course Type(s): Capstone.

DS-680. Marketing Analytics & Operation Research. 3.00 Credits.

Organizations need to interpret data about consumer choices, their browsing and buying patterns and to match supply with demand in various business settings. This course examines the best practices for using data to prescribe more effective business strategies. Topics covered include marketing resource allocation, metrics for measuring brand assets, customer lifetime value, and using data analytics to evaluate and optimize marketing campaigns. Students learn how data is used to describe, explain, and predict customer behavior, and meet customer needs. Students also learn to model future demand uncertainties, predict the outcomes of competing policy choices and take optimal operation decisions in high and low risk scenarios. Prerequisites: DS-510, DS-520.

DS-684. Data Engineering Using Cloud Computing. 3.00 Credits.

This course presents the fundamentals of cloud computing with a focus on data and analytics. Students will gain insights on how to analyze large datasets in the cloud using Microsoft Azure platform, from basic cloud tools to the big data distributed technologies like Spark, SQL and Python. With the exponential growth in data, organizations rely on the robust computing, storage, and analytical power of Azure, AWS and other cloud tools to scale, stream, predict, create visualizations and make data informed decisions. Course topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, data visualization and creating dashboards. Prerequisites: DS-542.

DS-687. Artificial Intelligence Fundamentals. 3.00 Credits.

This comprehensive course provides an introduction to Artificial Intelligence concepts. At the end of this class students will be able to describe what is AI, its applications, use cases, and how it is transforming our lives. Students will be able to explain and understand how the terms like machine learning, deep learning, and neural networks work. Hands on experience will be practiced with IBM Watson platform by using computer vision techniques and develop custom image classification models and deploy them to the Cloud. The class will also tackle the UpToDate topics of ethical concerns surrounding AI. Prerequisites: DS-510, DS-520.

DS-688. Natural Language Processing With Ai. 3.00 Credits.

This course explores the fundamental concepts of NLP and its role in current and emerging technologies. Students will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information. By mastering cutting-edge approaches, they will gain the skills to move from word representation and syntactic processing to designing and implementing complex deep learning models and other language understanding tasks. Prerequisites: DS-510, DS-520, DS-530, DS-542.

DS-690. Data Science and Health. 3.00 Credits.

Students will be introduced to the types of data commonly used in public health, biomedical and clinical settings. Students will acquire the knowledge and skills to use these data for understanding and improving the quality of health outcomes. Through lectures and class data analysis projects, students will explore, analyze and create graphical visualization of data from a variety of healthcare sources. Students will also be exposed to selective topics on real time analytics, clinical informatics, and machine learning for biomedical applications. Prerequisites: DS-510, DS-520.

DS-698. Exploring Industry & Technology Overseas. 3.00 Credits.

This travel course is tailored specifically for students in Data Science, Business Analytics, or MBA Business Analytics. Through instruction, industry visits, and cultural excursions students will gain a comprehensive knowledge of data-driven decision-making processes and business analytics practices within Germany and Belgium. Course Type(s): International (Travel).

DS-700. Independent Study in Data Science. 3.00 Credits.

In this course, students will work with a faculty member to explore a topic in depth or conduct independent research. Requirements for completion include submission of a research report. Course Type(s): Independent Study.

DS-702. Practicum in Data Science. 3.00 Credits.

Practicum is a learning experience that gives the students the opportunity to conduct real-world consulting projects with businesses that build upon the science, research and application of data and analysis, extending to strategic planning and identifying relevant tactics to carry out strategies. Prerequisites: DS-630, DS-631.

DS-703. Practicum in Statistics. 3.00 Credits.

Practicum is a teaching experience for doctoral students that gives the students the opportunity to conduct real-world consulting projects with businesses that build upon the large datasets, by working on statistical correlations while practicing teaching. Prerequisites: DS-520, DS-600.

DS-770. Topics in Data Science. 3.00 Credits.

Students will explore emerging, innovative, alternative and/or advanced subject matter in the field of data science. Topics vary by term.

DS-780. Practicum in Teaching Data Science. 3.00 Credits.

Recognizing that teaching data science at the college level requires more than just subject matter expertise, students in this course will devise, implement, assess, revise and reevaluate undergraduate and/or graduate data science lessons. Pre-service professors will develop and present student-centered lessons that engage classroom or virtual learners interactively and collaboratively by utilizing appropriate teaching and learning techniques and technologies. Classroom coaching and constructive feedback from mentors and peers will help students improve their teaching. Current instructors in data science and/or related disciplines are encouraged to enroll for professional development purposes.

DS-790. Practicum in Teaching Statistics. 3.00 Credits.

Recognizing that teaching statistical analysis and probability at the college level requires more than just subject matter expertise, students in this course will devise, implement, assess, revise and reevaluate undergraduate and/or graduate statistics lessons. Pre-service professors will develop and present student-centered lessons that engage classroom or virtual learners interactively and collaboratively by utilizing appropriate teaching and learning techniques and technologies. Classroom coaching and constructive feedback from mentors and peers will help students improve their teaching. Current instructors in statistics and/or related disciplines are encouraged to enroll for professional development purposes.

DS-800. Forecasting Methods Business Decisions. 3.00 Credits.

This course will prepare leaders for different forecasting methods and analytical tool to get them prepared for the business decisions. Forecasting methods will be evaluated according to the conditions such as under uncertainty, under risk and so on. Prerequisites: DS-801.

DS-801. Advanced Data Structures & Algorithms. 3.00 Credits.

This course explores core data structures and algorithms used in everyday applications, the trade-offs involved with choosing each data structure, along with traversal, retrieval, and update algorithms. It will be covered linked lists, stacks, queues, binary trees, and hash tables. Prerequisites: DS-630.

DS-802. Advanced Natural Language Processing. 3.00 Credits.

Students will explore the fundamental concepts of NLP and its role in current and emerging technologies. Students will develop a comprehensive working knowledge of modern neural network algorithms in order to process of linguistic information. By mastering cutting-edge approaches, students will gain the skills to advance from word representation and syntactic processing to designing and implementing complex deep learning models and other language understanding tasks. Prerequisites: DS-510 AND DS-520.

DS-803. Optimization Computational Lin. Algebra. 3.00 Credits.

In this course, students will learn about the theory and practical aspects of many fundamental tools from matrix computations, numerical linear algebra and optimization. In addition to classical applications, most examples will particularly focus on modern large-scale machine learning problems. Implementations will be done using MATLAB/Python. Prerequisites: DS-510 AND DS-520.

DS-804. Advanced Optimization. 3.00 Credits.

The course covers mathematical programming and combinatorial optimization from the perspective of convex optimization, which is a central tool for solving large-scale problems. The course is dedicated to the theory of convex optimization and its direct applications. Besides, it focuses on advanced techniques in combinatorial optimization. Prerequisites: DS-803.

DS-805. Research Seminar in Forecasting. 3.00 Credits.

In a research seminar format, students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest vary from term to term. Prerequisites: DS-510, DS-520.

DS-806. Research Seminar in Unstructured Data. 3.00 Credits.

In a research seminar format, students will work with faculty to develop research proposals, perform analyses, and create reports, culminating in presentations. Topics will emphasize Unstructured Data analysis, and may vary by term. Prerequisites: DS-510, DS-520.

DS-871. Development and Initiation. 4.00 Credits.

This course is the first in a series of four courses designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will focus on laying the foundation for their research by developing Chapters 1 and 2 of their dissertation. They will learn about the essential elements of a research proposal, including problem formulation, dataset research (if needed), literature review, research questions, and hypotheses. Additionally, students will begin collecting and analyzing data related to their research topic. Emphasis will be placed on individual student work with their Mentor and Dissertation Committee members. Prerequisites: DS-801, DS-802, DS-803, DS-804, DS-805, DS-806.

DS-872. IRB Approval and Data Collection. 4.00 Credits.

Dissertation Seminar 2 is the second part of a four course series designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will delve into the critical aspects of obtaining Institutional Review Board (IRB) approval for their research and initiating the data collection process. They will gain a comprehensive understanding of ethical considerations, data collection methods, and data management. Emphasis will be placed on individual student work with their Mentor and Dissertation Committee members. Prerequisites: DS-871.

DS-873. Data Analysis and Interpretation. 4.00 Credits.

Dissertation Seminar III is the third part of a four-course series designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will focus on the critical phases of data analysis, interpretation, and drawing meaningful conclusions from their research data. They will learn various data analysis techniques, visualization methods, and how to effectively communicate their findings. Prerequisites: DS-872.

DS-874. Finalization and Dissertation Defense. 4.00 Credits.

Dissertation Seminar IV is the final part of a four-course series designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will focus on finalizing their dissertation, including editing and polishing, preparing for the defense, and taking the necessary steps to successfully complete their doctoral journey. Students must maintain continuous enrollment in this course until they have successfully completed and defended their dissertation. Students must have their dissertation proposal approved by the Doctoral Committee for Research Involving Human Subjects prior to registering for this course. Prerequisites: DS-873.