Predictive Analytics and Data Visualization (PREDVIL GRC)
12-15 Credits
GRADUATE CERTIFICATE • 12-15 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.
Recommended Prerequisite: DATA610 Essentials of Business Analytics (3 cr) is a recommended prerequisite that should be completed prior to taking the following courses in the Graduate Certificate in Predictive Analytics and Data Visualization.
*DATA courses are only offered in a 15-week online format.
Which class should I take? When should I take it?
See our Recommended Program Sequences:
- Predicitve Analytics and Data Visualization, GRC (web)
- Predicitve Analytics and Data Visualization, GRC (pdf)
Major |
12 Credits | |
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DATA667 | In this course, students learn data visualization and communication skills using industry standard software. Emphasis is placed on communicating practical implications of data analytics results to a non-technical audience to facilitate decision making. Students apply their learning through hands-on projects, creating reports, and dashboards with high-impact visualizations. | |
DATA710 | In this course, students learn R programming language concepts in the context of conducting high-level statistical analysis through application-based programming 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 provide students working examples through which to apply various analysis strategies. Ultimately, students employ advanced modeling techniques to write R code to conduct data analysis with strong reusability. | |
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 |