Master of Science in Data Analytics (DATANLYTC MS)
MASTER OF SCIENCE IN DATA ANALYTICS • 30 CREDITS • DATANLYTC MS
Data Analytics is used to analyze vast databases that must be examined using complex algorithms and artificial intelligence to identify previously unidentified useful sets of relationships and trends. All aspects of the business and medical communities, as well as government agencies and non-profit organizations, rely on data analytics, yet are hampered by a growing shortage of data analysts. Davenport’s 30 credit hour Master of Science in Data Analytics responds to this need. The degree is delivered jointly by the College of Arts and Sciences in partnership with the Colleges of Technology, Business and Health Professions. The program is online and prepares individuals to conduct sophisticated analysis of existing data and create new data systems and methodologies. It is also designed to enable these individuals to make recommendations that increase effective use of data to help organizations meet specific goals and respond to new opportunities. The program uses industry standard software in practical applications directly related to current trends and issues that impact organizations across a broad spectrum. Course progression and content is carefully formulated to build competency in data analysis for students from a broad range of disciplines and experiences, including those who are new to the field.
• Completion of a bachelor’s degree from a regionally accredited university.
• Student must have earned a minimum undergraduate GPA of 2.75 or a graduate GPA of 3.0.
• Strong analytical skills with interest in applying sophisticated analytical methods using cutting edge software.
DATA courses are only offered in a 15-week online format.
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.
The benefits of using data to optimize the decision-making process, including understanding the differences between various types of data, data formats, data warehouses and data marts. Students will develop usable extraction, transformation and loading (ETL) techniques associated with data analysis and be introduced to data modeling and data mash-up techniques. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees.
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.
The basics of R programming are introduced including software installation and configuration necessary for effective data analysis. Generic programming language concepts are introduced and 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 introduction of 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. Applicable Course Fees can be found at https://my.davenport.edu/financial-aid/how-much-does-du-cost/tuition-and-fees.
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.
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.
DATA790: 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. In addition, students are required to contact The Office of Experiential Learning at least one semester prior to enrolling. Note: Any unexcused non-attendance or dismissal from an internship, practicum, clinical or fieldwork experience will result in a grade of F. A $30.00 insurance fee and a $30.00 internship management fee are charged in this course. 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 maybe required by the internship site. 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.
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.
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.