Data Science Analytics, BS and Data Analytics, MS Combined Degree
Data Science Analytics, BS and Data Analytics, MS Combined Degree (DSAN BS/MS)
138 Credits
BACHELOR OF SCIENCE/MASTER OF SCIENCE • 138 CREDITS • DSAN BS/MS
This program provides students the opportunity to earn a BS degree in Data Science and Analytics and an MS degree in Data Analytics in a five-year period. Students in this program will take graduate courses which will satisfy undergraduate content and credit hour requirements. Four graduate courses in the graduate Data Analytics program will enhance the undergraduate degree with higher-level learning. Upon completion, a student will have 120 credits required for the BS degree, and will have earned 12 graduate credits toward their MS degree. Students can complete their MS degree in Data Analytics by completing an additional 18 graduate credits.
Admissions requirements:
- Apply for secondary admission after 30 credits (sophomore status)
- Must have completed DATA 275 with B minimum grade
- Cumulative 2.75 GPA
Student may submit a graduation application to award the BS degree after completion of all undergraduate credits and MS Data Analytics core courses: DATA 610, DATA 625, DATA 667, DATA 710 (120 credits).
Which class should I take? When should I take it?
See our Recommended Program Sequences:
- Data Science and Analytics, BS Data Analytics, MS(web)
- Data Science and Analytics, BS Data Analytics, MS (pdf)
Foundations of Excellence |
37 Credits | |
|---|---|---|
| ACES100 | This course presents the skills needed for university success and initiates students to career planning and development. Students evaluate their abilities and interests in order to develop career goals and align these goals with an appropriate course of study. Through a career investigation project, students are introduced to research techniques. Students also improve on academic skills necessary to successfully complete university work, such as critical thinking, study techniques, and test taking strategies. In addition, students are introduced to important dynamics of interpersonal communication and conflict resolution. The course also orients students to the University, to the Davenport University Excellence System, and to other elements of the Davenport curriculum. (This course is required for all new business, health, and technology students, except those transferring with 30 or more semester credits.) Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/h | |
| BITS211 | Students create and manipulate spreadsheets with MS Excel to solve business applications. It is expected that students have a familiarity with spreadsheet software, as the course quickly progresses to advanced features, including data validation, linked workbooks, pivot tables, lookup functions, solver, and scenario manager. By the end of the semester, students will have the prerequisite skills to take applicable certification testing. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Recommended Prerequisite(s): MATH120 or MATH125 | |
| COMM120 | This course introduces and applies the theories and principles of effective communication. Students learn to organize and present clear, logical messages to specific audiences. They develop confidence in public speaking and increase their ability to inform and persuade listeners. They also implement critical thinking and listening skills. Finally, students exhibit the skills and tools necessary to construct, organize, and deliver effective speeches. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. | |
| ENGL109 | This course introduces students to expository and persuasive writing. Employing critical thinking and the writing process, students will compose academic essays utilizing a variety of modes. They will also analyze and respond to a variety of academic and professional readings. Students will evaluate information and audience to improve form and content. Students are also introduced to the research process, including finding, evaluating, and documenting sources, to complete a short research project using the American Psychological Association Style. Note: ENGL109L is a 0 credit hour lab utilized in the Accelerated Learning Program (ALP). Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): Appropriate test scores or successful completion of ENGL021. Student must also complete a diagnostic writing sample. | |
| ENGL110 | This course further develops the skills in expository and persuasive writing that were introduced in ENGL109, English Composition. Students develop critical thinking through the creation of essays and documents that use argumentation and persuasion. As a team, students collaborate to present a required assignment to the class. Students learn to research, evaluate, and incorporate information from both primary and secondary sources, to document secondary sources using APA format, and to analyze information and audiences to improve form and content. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): ENGL109 | |
| ENGL311 | This course develops the written and presentation skills necessary for success in professional, supervisory, or managerial positions. Emphasis on communication in both on-paper and digital media is included. Students also learn to use a variety of formats, styles, and delivery systems to achieve the clear, concise, and professional communication required to communicate in global markets. To stress the importance of workplace communication, students create a major professional document as a team. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): ENGL110 and COMM120 | |
| MATH150 | This course is designed to prepare students for the traditional calculus sequence. Topics include: brief review of algebra, solving equations and inequalities, systems of linear and nonlinear equations, the properties and graphs of relations and functions (including polynomial, radical, rational, logarithmic, exponential, and trigonometric), zeros of polynomial functions, trigonometry, conic sections, polar coordinates. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): Completion of MATH120 or MATH125 with a C or above | |
| SOSC201 | This course introduces students to the complex issues surrounding diversity in U.S. society and to the need for understanding difference in an increasingly globalized world. Students will explore the social-historical context of multiple experiences on individual, cultural and institutional levels. They will analyze the complex interactions regarding diversity in organizations. Students will also evaluate their own thoughts, attitudes, and behaviors in order to understand their roles in a diverse society. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. | |
| ECON200 | This course introduces students to economics. Students learn the basics of supply and demand; the market economy; elasticity; the foundation of consumer demand; the theory of the business firm and costs of production; the market structures of perfect competition, monopoly, oligopoly, and monopolistic competition; theories of labor unions and wages; antitrust policy; and the microeconomic view of international business. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): ENGL109 and MATH120 or MATH125 | |
| ECON201 | This course introduces students to economics, the schools of economic thought, and international economics. Students learn the methodology, concepts, and terminology of macroeconomics, including principles, theories, and tools. They also study banking, money, the Federal Reserve System, and monetary theory. In addition, macroeconomic problems such as inflation, unemployment, economic growth, and globalization are discussed. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): ENGL109 and MATH120 or MATH125 | |
| MATH120 | Students in this course will explore and apply college-level mathematical concepts so as to enhance their critical and creative thinking skills. This course aims to increase students' appreciation of the utility and application of mathematics. Topics will include i) problem solving, ii) set theory and real numbers, iii) linear, quadratic, exponential, and logarithmic functions, and iv) probability. Other topics of interest will be selected from graph theory, prime numbers, logic, number representation, and voting theory. Fees: Additional course fees apply. Prerequisite(s): Appropriate test scores, placement criteria, or successful completion of MATH 030 | |
| STAT220 | This is the basic statistics course in which students learn to collect, analyze, present and interpret data. Descriptive and inferential statistical methods are applied in problem-solving and decision-making situations. Analysis of large, real-world data sets will be performed using statistical software. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): MATH120 or MATH125 |
Foundations of Math and Statistics |
14 Credits | |
|---|---|---|
| MATH205 | This course introduces the fundamentals of linear algebra (i.e., the notation and algebra of vector spaces and matrices). Because these items have the ability to handle masses of data as a single unit with relative ease, they are of particular interest to those in computer science. Those applications to programming (e.g., 3-D game design, simulation, and biometric security) will serve as context throughout the course. Topics include matrix operations, linear transformations, vector spaces, and 3D geometry. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): MATH135 or MATH150 | |
| MATH215 | This course covers differential calculus and an introduction to integral calculus. Topics include: limits and continuity, the definition of the derivative, rules and techniques of differentiation, applications of the derivative (including motion, L’Hôpital’s Rule, curve sketching, optimization, and related rates), antiderivatives, Riemann sums, the definition of the definite integral, the Fundamental Theorem of Calculus, and elementary methods and applications of integration. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Note: A grade of C or above is required to take MATH216, MATH317 and MATH350. Prerequisite(s): MATH150 | |
| MATH250 | This course applies fundamental ideas in discrete structures and mathematical reasoning. Topics include elementary logic and set theory, functions and relations, induction and recursion, elementary algorithm analysis, counting techniques, and introduction to computability. Fundamental techniques include graph theory, Boolean algebra, and trees. Techniques and topics will form the foundation for subsequent programming language courses. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): CISP111 and MATH130, MATH135 or MATH150 | |
| STAT322 | This course introduces students to the advanced methods of data analysis. Particular focus will be given to techniques commonly used in the decision-making processes of those in management and marketing research, as well as those pursuing other careers requiring the interpretation of statistics-based research. Analysis of large, real-world data sets will be performed using statistical software. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): STAT219 or STAT220 |
Foundations of Business |
6 Credits | |
|---|---|---|
| MKTG211 | This course explores the role of marketing in society and in the success of an organization. Students learn and apply the strategies, tactics and terminology used by market-oriented businesses. Through critical thinking exercises and case analysis, students become familiar with the primary tools of marketing including market segmentation, product, pricing, marketing communication, research, and marketing channel strategies. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Co-requisite(s): ENGL109 | |
| MKTG412 | Students will conduct, prepare, and present an actual situation analysis report for a firm using appropriate primary and secondary sources. The course reviews the nature, procedures, terminology, and application of research in solving marketing problems. Students learn the steps of marketing research, including problem definition, research design, sampling procedures, data collection methods, data analysis and interpretation, and the research report. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Recommended Prerequisite(s): FINC211 Prerequisite(s): BITS211, ENGL311, MKTG211, STAT220, and achieved senior status |
Foundations of Computer Science |
15 Credits | |
|---|---|---|
| CISP253 | The Python programming language is cross platform in nature and can be used on Windows, Linux/Unix and Mac OS systems. This broad-based capability makes the Python Scripting languages highly useful in the field of technology. The language is highly capable in stream editing of data, data manipulation and parsing, which are required in IT and Forensics. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. | |
| CISP309 | This course covers the use of a relational database management system (RDBMS) in the design and development of database systems. Topics include the use of SQL, DDL, stored procedures, indexes, constraints, triggers, user management, query optimization, and administrative tasks. Recommended Prerequisite(s): CISP 247 | |
| CISP446 | The design and implementation of data warehouses (including data marts and operational data stores) are studied using current database technologies. Topics include data modeling for warehouses, data warehousing infrastructure and tool selection, data exploration, data synthesis and reduction, organizational metadata, data warehouse administration, and other contemporary issues. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): STAT322 | |
| CSCI280 | This course will present an introduction to the field of Artificial Intelligence. Topics will include problem solving, search techniques (including game playing), inductive learning, decision trees, reasoning, and natural language understanding. Prerequisite(s): MATH 250 and CSCI 231 or CISP 253 or DATA 288 | |
| CSCI325 | This course will introduce the student to the theory and application of deep learning. Machine learning concepts will be covered such as hyperparameters, validation sets, overfitting, under-fitting, bias and variance. Methods for regularization of deep learning methods will be discussed as well as the optimization and application of deep learning algorithms to real world problems. Other concepts that may be discussed could include convolutional networks and autoencoders. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): CSCI280 |
Major |
21 Credits | |
|---|---|---|
| DATA275 | The basics of data analytics are introduced including descriptive, predictive and prescriptive statistics, regression analysis, and data visualization. The instructional approach is an application-based introduction to data analytics practices such as data cleaning, data organization for analysis, and exploratory data analysis. A key component of instruction is an emphasis on hands-on practice with data analysis projects and presentation of results to multiple audiences. Techniques examined emphasize applicability in multiple organizational sectors including business, healthcare, and technology. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): STAT220 | |
| DATA288 | This course introduces students to the R statistical programming language. Includes installation and configuration of software necessary for the statistical programming and data analysis. Covers practical problems such as statistical computing, reading and manipulating data, using R packages, writing R functions, debugging, and organizing R programs. | |
| DATA356 | This course introduces students to regression-based modeling. It covers supervised versus unsupervised learning, bias-variance tradeoff, cross-validation, simple and multiple linear Least Squares regression, variable selection methods, ridge regression, and Lasso. Emphasis is placed on model creation and validation rather than traditional inference methods. Students get hands-on practice by conducting a data analytics project using real world data sets. Prerequisite(s): DATA 275 | |
| DATA374 | This course provides the foundational knowledge to use classification models to create business insights and other real-world problems. It teaches a systematic approach for building classification models from an input data set. Examples include decision tree classifiers, logistic regression, KNN, support vector machines, and naive Bayes classifiers. The course design purposely makes it easy to learn commonly used classification algorithms and how to use those algorithms to solve business problems. Upon completing this course, students will be able to clearly define a classification problem, extract and prepare data, explore data using univariate and bivariate visualization, and build and evaluate classification models using five basic and advanced algorithms. Prerequisite(s): DATA 275, DATA 342 | |
| DATA495 | Students will apply all of their theoretical and practical experience to design and execute an analytics project on a chosen topic as a culmination of their analytics program, thereby demonstrating competency of program learning outcomes. Students will select the techniques to be used in the study, collect and analyze data for the purpose of drawing conclusions and making recommendations to the decision makers of an organization. Prerequisite(s): Students must achieve senior status to take this course. | |
| DATA490 | Registration Internship/Practicum Note: Attend Mandatory Internship Workshop at least two semesters prior to your desired internship course semester. The required internship workshop and approval process can be found at: https://my.davenport.edu/internships. This undergraduate internship is the integration of previous classroom instruction with new learning acquired through on-the-job work experience. The experience should be related as closely as possible to the student's major field and individual interest. In this course, the student integrates data analytics skills acquired through classroom instruction with on-the-job learning via work experience. Emphasis is placed on extensive hands-on experience in one or more of the following focus areas: organizing and exploring data, building dashboards, mining data, or conducting predictive analysis using industry standard software. 150 hours of career-related work time shall be required for the 3 credit course pursued. Internship hours wil | |
| SPMG370 | Students will learn how to use historical data to predict trends or inform sport decisions. The class will cover the theory, development, and application of sport data and analytics for the purpose of outperforming opponents. Specific topics in analytics include sport organization management, ticket sales, in-game strategy, and sport fantasy league applications. This course is designed for students of all majors who have an interest in sport analytics. Prerequisite(s): STAT 220 |
Open Electives |
15 Credits |
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Core Courses |
12 Credits | |
|---|---|---|
| DATA610 | Essentials of big data and data analytics are introduced and include descriptive, predictive and prescriptive statistics, regression analysis, optimization techniques and data visualization. The instructional approach in this course focuses on application-based reinforcement of concepts to include the use of simulations. A key component of instruction is an emphasis on analytical report writing and other ways to effectively present data analytic results. Techniques examined emphasize applicability in multiple organizational sectors to include business, finance, human resources, healthcare, manufacturing, sport management, social services, education, non-profit, and government entities. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. | |
| DATA625 | This course introduces students to data mining methods and applications. It covers basic concepts and tools for data mining, including data sources, data cleaning tools and methods, mainstream algorithms for data mining, statistical modeling, popular tools for mining structured data and unstructured data. Students will also learn how data mining can be effectively used in various application areas to drive decisions and actions. Students get hands-on practice by conducting a data analytics project using real world data sets. | |
| DATA667 | Data visualization and communication skills are taught using industry standard software. The instructional approach in this course focuses on application using hands-on projects to create reports and dashboards with high-impact visualizations of common data analyses to help in decision making. A key element of instruction is an emphasis on communicating the practical implications of data analytics results to a non-technical audience in a timely manner. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. | |
| DATA710 | R programming language concepts are covered within the context of how they are implemented in practice when conducting high-level statistical analysis. The instructional approach in this course focuses on application-based programming concepts such as reading data into R, accessing analysis tool boxes in R, writing R functions, debugging, and organizing and commenting in R code. Data mining and analysis projects will be used to provide working examples. Upon completing this course, students will be able to employ advanced modeling techniques to write R code to conduct data analysis with strong reusability. |
Advanced Courses |
15 Credits | |
|---|---|---|
| DATA728 | This course will be a more advanced treatment of data mining and predictive analytics concepts introduced in DATA625 with a focus on customer relationship management (CRM). Using customized variations of the industry-standard CRISP-DM methodology, it will provide an experiential learning opportunity to explore all six phases of the model. This includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Industry standard tools and techniques are utilized to prepare students with the knowledge to be successful in current organizations. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): DATA625 | |
| DATA742 | Students will be introduced to the concept of the data warehouse and the role it plays in an organization’s overall business intelligence and analytics strategy. This course will cover the two predominate warehouse design strategies, as well as hybrid designs that combine best practices from both areas, including the requirements of a data warehouse, selecting the proper design strategy, choosing the proper tools to support that design, selecting metrics for monitoring performance, data quality, and planning future enhancements. Students will be able to build a high-level plan for implementing a data warehouse in their organization or planning future changes to an existing warehouse if present. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. | |
| DATA758 | This course introduces the essentials of cloud computing and various service models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). These cloud service models are also reviewed in terms of their role in delivering on-demand computing resources to customers. The risks and benefits of cloud deployment models as public, private, hybrid, and community, are discussed together with the underlying infrastructure and operational considerations related to security and privacy. In addition, various cloud vendor platforms are explored to learn how cloud computing is implemented in practice. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. | |
| DATA772 | This course covers statistical procedures used in data analytics with emphasis on hands-on practice. Industry standard software is used to import and prepare data for model development as well as for developing various types of regression models. Assessment of model performance and methods for model selection are also covered. Emphasis is also placed on parameter estimation, variable selection, and diagnostic checking of these models and their use for statistical inference and prediction. Both numerical and graphical techniques are used for diagnostics and reporting. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): DATA710 | |
| DATA785 | This course covers statistical modeling in the use of statistical methods to develop models that can be used for predicting future numerical or categorical outcomes in processes for disciplines ranging from business to science. The philosophy of modeling as well as common modeling methods and model adequacy assessment procedures are covered. Industry standard software is used to prepare data, develop and assess models, obtain predictions, and present results. The main thrust of the course is on the application of predictive modeling rather than the theory behind it. Selected projects will be used to provide hands-on experience with the various steps involved in modeling and predicting. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): DATA772 |
Capstone Project |
3 Credits | |
|---|---|---|
| DATA792 | Students will apply all of their theoretical and practical experience to design and execute an analytics project on a chosen topic as a culmination of their analytics program, thereby demonstrating competency of program learning outcomes. Students will select the techniques to be used in the study, collect and analyze data for the purpose of drawing conclusions and making recommendations to the decision makers of an organization. Note: A grade of B or better must be earned to pass this course successfully. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees. Prerequisite(s): Course may only be elected in the final semester of the program. |