Topics to be covered include data collection, analysis and visualization as well as statistical inference methods for informed decision-making.Students will explore these topics with current statistical software.The purpose of this capstone course in Data Analytics is to assess students' ability to synthesize and integrate the knowledge and skills they have developed throughout their coursework.Tags: Essay On Need To Promote World PeacePhd Thesis In Political ScienceRani Of Jhansi EssayPhysics Homework Solutions5 Paragraph Essay To Kill A Mockingbird CourageRandom Locker Searches EssayNeed Help AssignmentResearch Papers In ApaCreative Writing Exercises CollegeHomework For Preschool Printable
This course examines the issues in management and analytical analysis of massive datasets, and unstructured data, including data warehousing from an enterprise perspective.
Students will learn the concepts and techniques for managing the design, development, security and maintenance of enterprise information.
This course explores data mining methods and procedures for diagnostic and predictive analytics.
Topics include association rules, clustering algorithms, tools for classification, and ensemble methods.
This course explores two main areas of machine learning: supervised and unsupervised.
Topics include linear and logistic regression, probabilistic inference, Support Vector Machines, Artificial Neural Networks, clustering, and dimensionality reduction, and programming.
This course examines the data analysis process with the emphasis of quantitative and qualitative findings from data.
Students will develop skills in data analytics methods and predictive analytics that will allow them to develop algorithmic methods and use them along with popular industry software for data-driven solutions.
The candidate’s work history, references, and other personal qualities and characteristics will be considered as well if the GPA requirement is not met Fall 2019 August 26, 2019 Spring 2020 January 7, 2020 Summer 2020 April 13, 2020 Successful completion (C or better) of a college level Introductory Statistics course is required.
Completion of a college level programming course is strongly recommended.