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Course Criteria
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3.00 Credits
This course provides both the concepts and practical applications of predictive analytics using data mining techniques of classification and prediction. Techniques learned in data mining - neural net models, machine learning, logit/probit regressions, along with advanced time series methods, text mining/analytics will be discussed. Real business cases will be used to demonstrate the application of these data mining methods using tools such as XLMiner, SAS Enterprise Miner, and SQL Server BI tools along with the R programming language. Prerequisite: DA 540.
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3.00 Credits
This course covers methods to store and analyze large datasets ('Big Data'). Particular focus will be on Hadoop, and MapReduce technology. Further, the course covers No SQL, Key-value, concepts for handling unstructured data. There will be select topics for analytics on 'Big Data'. An integral part of this course is the application of database knowledge learned in the prior courses in the program. All data in this course will be stored in an appropriate relational (SQL) or document oriented (NoSQL) database. Students will then query the database for the data they will use in their analyses.
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3.00 Credits
This course is an opportunity for students to apply the concepts, tools, and techniques learned during the Master's Program in Applied Data Analytics. In this course, students will take up real life data, explore and ask the right questions using the data, and derive insights to empower decision-making.
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3.00 Credits
This course provides students with an introduction to the need for and methods for data cleaning. The course presents methods for locating and handling invalid values, out-of-range values, and missing values along with methods for managing datasets. The course uses SAS software.
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3.00 Credits
Traditional Business Intelligence (BI) tools are unable to handle the Big Data challenge due to exponential growth of data volume, velocity and variety. To cope up with this new demand, organizations are embracing new techniques like data visualization which involves data discovery and exploration. Technology giants like Amazon, Facebook, Google, Netflix use powerful data visualization tools to gain customer insights on their choices and apply them into their service offerings. Organizations are able to ask better questions and derive better decisions. This introductory course will teach students how organizations can harness the power of Big Data through data visualization. Students will learn how to capture data in visual format for better decisions using data viz tools like SAS, Tableau. Prerequisite: DA 555.
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3.00 Credits
This course covers an introduction to big data analysis tools. The course provides an overview of SAS, Hadoop and other big data tools. The course covers the structure and framework of data analytic tools and covers the use of these tools to perform various analyses.
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3.00 Credits
This course covers methods to store and analyze large datasets ('Big Data'). Particular focus will be on Hadoop, and MapReduce technology. Further, the course covers No SQL, Key-value, concepts for handling unstructured data. There will be select topics for analytics on 'Big Data'. An integral part of this course is the application of database knowledge learned in the prior courses in the program. All data in this course will be stored in an appropriate relational (SQL) or document oriented (NoSQL) database. Students will then query the database for the data they will use in their analyses. Prerequisite: DA 560.
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3.00 Credits
This capstone course in the Masters in Data Analytics program incorporates skills learned throughout the program into real-world analytics project. An integral part of this course is the application of database knowledge learned in the prior courses in the program. All data in this course will be stored in an appropriate relational (SQL) or document oriented (NoSQL) database. Students will then query the database for the data they will use in their analyses. Prerequisite: DA 570.
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3.00 Credits
This capstone course in the Masters in Data Analytics program incorporates skills learned throughout the program into real-world analytics project. An integral part of this course is the application of database knowledge learned in the prior courses in the program. All data in this course will be stored in an appropriate relational (SQL) or document oriented (NoSQL) database. Students will then query the database for the data they will use in their analyses. Prerequisite: DA 570.
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3.00 Credits
This online course provides teachers with the opportunity to review recent research and theory concerning advanced child growth and development. The course examines the nature and process of child development with a focus on infancy and early childhood years. The primary goal of the course is the integration of information generated from empirical research, both classic and current, into explanatory systems (theories) of child development. The emphasis in this course is less on learning the facts of child development and more on learning why child development research is conducted (theories), how it is conducted (methodology), what it means for the field (conclusions) and how research is evaluated (critical thinking).
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