New Courses (Effective 201830):

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New Courses (Effective 201910):

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DASH 2000, 1 credit

E-Portfolio

New course as of Fall Semester 2018

Students design an e-portfolio documenting the achievements, knowledge, and skills acquired throughout the program. The aim of the e-portfolio is to showcase students’ work tailored to their career goals and professional identity. Consultation with the instructor to review the status of the portfolio will be scheduled 2-3 times over the semester.

Prerequisite(s): PHIL 1199

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JOUR 1242, 1.5 credits

Advanced Copy Editing

New course as of Fall Semester 2018

This course builds on the skills developed in JOUR 1142: Basics of Copy Editing. Students learn how to undertake more challenging structural changes to stories, refining these stories to improve their flow. Students also learn how to assess stories for gaps and omissions in information, fairness, good taste, libel, editorializing, and other issues.

Prerequisite(s): JOUR 1142

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STAT 3225, 4 credits

Statistical Methods for Biological and Health Sciences

New Course as of Fall Semester 2018

Students learn how to use statistical methods for analyzing data from the biological and health sciences. The programming language R and R commander is used for statistical computing including data manipulation, data analysis, and graphical display of data. Topics covered in this course include: observational and experimental studies, parametric and nonparametric statistical methods, analysis of contingency tables, analysis of variance, multiple linear regression, and logistic regression. Students are required to complete a term data analysis project using statistical methods and software presented in this course.

Prerequisite(s): STAT 1181, STAT 2281

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CPSC 4800, 3 credits

Computing for Data Analytics

New course as of Spring Semester 2019

Computers provide the power and platform for any significant work in data analytics. Students learn about an organization's information systems and business processes as well as its multiple data sources. Students issue database commands to examine the data's structure and organization and retrieve appropriate sized datasets. Students also learn programming using the Python programming language.

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CPSC 4810, 3 credits

Transformations for Data Analytics

New course as of Spring Semester 2019

Data analysts need to integrate heterogeneous data from a variety of sources and in a variety of formats to provide workable homogeneous data sets for further analysis or processing. Students write programs using a scripting language to extract, transform, merge, and clean data to generate datasets that can be loaded into an appropriate analysis or visualization tool.

Prerequisite(s): CPSC 4800

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CPSC 4820, 3 credits

Visualization for Data Analytics

New course as of Spring Semester 2019

Data visualization is the third and final part in the data science workflow. This is where all of the collecting, transforming, and analysis culminates in a form of communication to others. Students need to convert that qualitative and quantitative results into visual formats that others can understand and benefit from.

Prerequisite(s): DANA 4800 and CPSC 4800.

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DANA 4800, 3 credits

Data Analysis and Statistical Inference

New course as of Spring Semester 2019

Statistical inference is the process of drawing conclusions from data. Students gain a foundation in probability, descriptive statistics, sampling methods, normal distributions, Poisson distributions, sampling distributions, one-sample and two-sample statistical inference procedures on both proportions and means (including z and t).

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DANA 4810, 3 credits

Predictive Analytics - Quantitative Data

New course as of Spring Semester 2019

Predictive Analytics is a process of using and applying statistical analysis techniques for estimation and forecasting. Students learn standard methodology for analyzing quantitative data, including analysis of variance, design of experiments, simple regression, multiple regression, data transformation, and generalized linear models.

Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or MATH 4801 and DANA 4800.

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DANA 4820, 3 credits

Predictive Analytics - Qualitative Data

New course as of Spring Semester 2019

Predictive Analytics is a process of using and applying statistical analysis techniques for estimation and forecasting. Students learn standard methodology for analyzing categorical data including chi-square tests for a two-way and multi-way contingency tables, logistic regression, and Poisson regression.

Prerequisite(s): A passing mark from Data Analytics Math Assessment Test or MATH 4801 and DANA 4800.

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MATH 4801, 1 credit

Mathematics for Data Analytics

New course as of Spring Semester 2019

Students require a solid foundation in pre-calculus algebra and linear algebra to succeed in the PDD in Data Analytics. Topics include linear equations, systems of equations, matrix operations, quadratic forms, power functions, square root functions, exponential functions, logarithmic function, and reciprocal functions.

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NURS 4161, 3 credits

Nursing Knowledge VIII: Advanced Healing Modalities for Complex Care

New course as of Spring Semester 2019

Students examine concepts related to complex alterations in health and essential nursing care of adults in complex health care settings. Students learn to integrate knowledge through the use of a decision-making framework (DMF) in understanding clients' and families' experiences with complex health challenges. Students also explore nursing theory, humanities, and health sciences for advanced physical assessment for pharmacological and diagnostic investigation. There is a focus on inter-professional practice and continuity of care as well as teaching of clients, families, and groups.

Prerequisite(s): A minimum 'C+' grade in NURS 3309, NURS 3330, and NURS 3361; and an 'S' grade in NURS 3363.

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