Predictive Analytics and Data Visualization (PREDVIL GRC)
12 Credits
GRADUATE CERTIFICATE • 12 CREDITS • PREDVIL GRC
Data Analytics analyzes vast databases that must be examined using complex algorithms and artificial intelligence to identify previously unidentified useful sets of relationships and trends. The Predictive Analytics and Data Visualization Graduate Certificate is offered online and prepares individuals to build predictive and forecasting models, generate data visualization products, and build data dashboards and automated reports. The Graduate Certificate 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 predictive modeling, forecasting and data visualization for students from a broad range of disciplines and experiences, including those who are new to the field. Credit from this certificate program can be transferred to the Master of Science in Data Analytics Degree program.
DATA courses are only offered in a 15-week online format.
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 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