Master of Science in Data Analytics

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.

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.

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.

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.

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

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 Essentials of Cloud Computing
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.

DATA790 Data Analytics Internship
Contact Regional Internship Manager at least 1 semester prior to enrolling.
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. Further, to ensure adequate practice, a minimum of 150 hours of career related work are required at the internship site as well as weekly progress reports, a written internship report and an oral presentation. Note: Any unexcused non-attendance or dismissal from an internship, practicum, clinical or fieldwork experience will result in a grade of F. A grade of B or better is required in order to earn a passing grade in this course. A criminal background check and drug screening may be required by the internship site. Additional course fee(s) apply.
Prerequisite(s): DATA610, DATA667, DATA710, DATA728

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

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

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.