Comprehensive Exam Track: Total Credit Hours Required to Finish the Degree ( 36 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 151096000 | RESEARCH METHODS | This course provides students an overview of research methodologies used in human resource management. Special emphasis will be placed on the role of human resource management systems that can be utilized to help answer research questions. | 3 | - | 3 |
- |
| 151096100 | INTRODUCTION TO DATA SCIENCE | This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. | 3 | - | 3 |
- |
| 151096110 | DATA MANAGEMENT | This course is introduction to database management systems and provides a foundation for data modeling, mining and analytics. Topics covered include data models, query languages, query optimization and database design. | 3 | - | 3 |
- |
| 151096120 | DATA VISUALIZATION | This course is introduction to the theory and concepts of data visualization and the techniques used to create visual representation of large amounts of data. Students will learn to analyze and present visual data for better decision | 3 | - | 3 |
151096100 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. |
| 151096130 | MACHINE LEARNING | This course provides an overview of the study of machine learning and its role in the field of artificial intelligence. Topics covered include Bayesian Learning, decision trees, genetic algorithms and neural networks. | 3 | - | 3 |
151096100 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. 151096150 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include problem solving, planning, machine learning and nature language processing. |
| 151096140 | PRINCIPLES OF MARKETING | This course provides an overview of the principles of marketing management and strategy and incorporates the skills learned in data science. Students will analyze customer data to make recommendations on customer segmentation, purchasing behavior and targeted marketing and pricing. | 3 | - | 3 |
- |
| 151096150 | FOUNDATIONS OF ARTIFICIAL INTELLIGENCE | This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include problem solving, planning, machine learning and nature language processing. | 3 | - | 3 |
- |
| 151096190 | BUSINESS STATISTICS | This course is an overview of the range of statistical techniques necessary for decision making. Topics include descriptive statistics, probability distribution, sampling theory, statistical inference, hypothesis testing, experimental design and analysis of variance and linear and multiple regressions. | 3 | - | 3 |
- |
| 151096200 | BUSINESS ANALYTICS | This course is introduction to the development, implementation, and utilization of computer-based business models. Students will learn to use quantitative methods to formulate a regression model and to use and interpret the information a model produces to analyze business questions. | 3 | - | 3 |
151096190 BUSINESS STATISTICS This course is an overview of the range of statistical techniques necessary for decision making. Topics include descriptive statistics, probability distribution, sampling theory, statistical inference, hypothesis testing, experimental design and analysis of variance and linear and multiple regressions. |
| 151096210 | DECISION ANALYSIS | This course examines the techniques that successful business managers use to successfully plan, frame, and research business decisions before implementing them. Students will learn how to apply systematic decision-making processes in order to reduce risk and choose the best course of action for the organization. | 3 | - | 3 |
- |
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 151096160 | TEXT MINING | This course is introduction to the field of text mining and will examine techniques used to analyze large amounts of data from traditional sources as well as the web and social media. This course will build on the foundation of Machine Learning and Natural Language Processing. | 3 | - | 3 |
151096180 DATA MINING This course builds on the foundation provided in Introduction to Data Science and provides students with an overview of the field of data mining. Students will learn the process and techniques to discover patterns in large data sets. 151096220 NATURAL LANGUAGE PROCESSING This course provides an overview of the ways that computers can process interpret written and spoken language and the role of natural language processing in artificial learning. |
| 151096180 | DATA MINING | This course builds on the foundation provided in Introduction to Data Science and provides students with an overview of the field of data mining. Students will learn the process and techniques to discover patterns in large data sets. | 3 | - | 3 |
151096150 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include problem solving, planning, machine learning and nature language processing. |
| 151096220 | NATURAL LANGUAGE PROCESSING | This course provides an overview of the ways that computers can process interpret written and spoken language and the role of natural language processing in artificial learning. | 3 | - | 3 |
151096100 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. |
| 151096310 | DATA SCIENCE FOR BUSINESS | This course builds on the foundations of Business Analytics and Data Mining and provides students with the concepts and quantitative techniques to extract business knowledge and value from data for competitive advantage. | 3 | - | 3 |
151096180 DATA MINING This course builds on the foundation provided in Introduction to Data Science and provides students with an overview of the field of data mining. Students will learn the process and techniques to discover patterns in large data sets. 151096200 BUSINESS ANALYTICS This course is introduction to the development, implementation, and utilization of computer-based business models. Students will learn to use quantitative methods to formulate a regression model and to use and interpret the information a model produces to analyze business questions. |
| 151096320 | DESIGN OF ALGORITHMS | This course is introduction to algorithm design and serves as a foundation for future courses in data science. Topics covered include common algorithms designs such as greedy optimization, dive and conquer, dynamic programming, network flows, reduction and randomized algorithms. | 3 | - | 3 |
151096100 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. 151096190 BUSINESS STATISTICS This course is an overview of the range of statistical techniques necessary for decision making. Topics include descriptive statistics, probability distribution, sampling theory, statistical inference, hypothesis testing, experimental design and analysis of variance and linear and multiple regressions. |
| 151096330 | MARKETING ANALYTICS | This course provides an overview of the field of marketing analytics and how data analysis and mining are used to make strategic marketing decisions. The emphasis will be on using customer insights to make decisions that increase the value of a business. | 3 | - | 3 |
151096140 PRINCIPLES OF MARKETING This course provides an overview of the principles of marketing management and strategy and incorporates the skills learned in data science. Students will analyze customer data to make recommendations on customer segmentation, purchasing behavior and targeted marketing and pricing. |
Thesis\Treatise Track: Total Credit Hours Required to Finish the Degree ( 36 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses plus ( 6 ) credit hours for the Thesis
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 151096000 | RESEARCH METHODS | This course provides students an overview of research methodologies used in human resource management. Special emphasis will be placed on the role of human resource management systems that can be utilized to help answer research questions. | 3 | - | 3 |
- |
| 151096100 | INTRODUCTION TO DATA SCIENCE | This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. | 3 | - | 3 |
- |
| 151096110 | DATA MANAGEMENT | This course is introduction to database management systems and provides a foundation for data modeling, mining and analytics. Topics covered include data models, query languages, query optimization and database design. | 3 | - | 3 |
- |
| 151096130 | MACHINE LEARNING | This course provides an overview of the study of machine learning and its role in the field of artificial intelligence. Topics covered include Bayesian Learning, decision trees, genetic algorithms and neural networks. | 3 | - | 3 |
151096100 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. 151096150 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include problem solving, planning, machine learning and nature language processing. |
| 151096140 | PRINCIPLES OF MARKETING | This course provides an overview of the principles of marketing management and strategy and incorporates the skills learned in data science. Students will analyze customer data to make recommendations on customer segmentation, purchasing behavior and targeted marketing and pricing. | 3 | - | 3 |
- |
| 151096150 | FOUNDATIONS OF ARTIFICIAL INTELLIGENCE | This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include problem solving, planning, machine learning and nature language processing. | 3 | - | 3 |
- |
| 151096190 | BUSINESS STATISTICS | This course is an overview of the range of statistical techniques necessary for decision making. Topics include descriptive statistics, probability distribution, sampling theory, statistical inference, hypothesis testing, experimental design and analysis of variance and linear and multiple regressions. | 3 | - | 3 |
- |
| 151096200 | BUSINESS ANALYTICS | This course is introduction to the development, implementation, and utilization of computer-based business models. Students will learn to use quantitative methods to formulate a regression model and to use and interpret the information a model produces to analyze business questions. | 3 | - | 3 |
151096190 BUSINESS STATISTICS This course is an overview of the range of statistical techniques necessary for decision making. Topics include descriptive statistics, probability distribution, sampling theory, statistical inference, hypothesis testing, experimental design and analysis of variance and linear and multiple regressions. |
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 151096120 | DATA VISUALIZATION | This course is introduction to the theory and concepts of data visualization and the techniques used to create visual representation of large amounts of data. Students will learn to analyze and present visual data for better decision | 3 | - | 3 |
151096100 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. |
| 151096160 | TEXT MINING | This course is introduction to the field of text mining and will examine techniques used to analyze large amounts of data from traditional sources as well as the web and social media. This course will build on the foundation of Machine Learning and Natural Language Processing. | 3 | - | 3 |
151096180 DATA MINING This course builds on the foundation provided in Introduction to Data Science and provides students with an overview of the field of data mining. Students will learn the process and techniques to discover patterns in large data sets. 151096220 NATURAL LANGUAGE PROCESSING This course provides an overview of the ways that computers can process interpret written and spoken language and the role of natural language processing in artificial learning. |
| 151096180 | DATA MINING | This course builds on the foundation provided in Introduction to Data Science and provides students with an overview of the field of data mining. Students will learn the process and techniques to discover patterns in large data sets. | 3 | - | 3 |
151096150 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include problem solving, planning, machine learning and nature language processing. |
| 151096210 | DECISION ANALYSIS | This course examines the techniques that successful business managers use to successfully plan, frame, and research business decisions before implementing them. Students will learn how to apply systematic decision-making processes in order to reduce risk and choose the best course of action for the organization. | 3 | - | 3 |
- |
| 151096220 | NATURAL LANGUAGE PROCESSING | This course provides an overview of the ways that computers can process interpret written and spoken language and the role of natural language processing in artificial learning. | 3 | - | 3 |
151096150 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include problem solving, planning, machine learning and nature language processing. |
| 151096310 | DATA SCIENCE FOR BUSINESS | This course builds on the foundations of Business Analytics and Data Mining and provides students with the concepts and quantitative techniques to extract business knowledge and value from data for competitive advantage. | 3 | - | 3 |
151096180 DATA MINING This course builds on the foundation provided in Introduction to Data Science and provides students with an overview of the field of data mining. Students will learn the process and techniques to discover patterns in large data sets. 151096200 BUSINESS ANALYTICS This course is introduction to the development, implementation, and utilization of computer-based business models. Students will learn to use quantitative methods to formulate a regression model and to use and interpret the information a model produces to analyze business questions. |
| 151096320 | DESIGN OF ALGORITHMS | This course is introduction to algorithm design and serves as a foundation for future courses in data science. Topics covered include common algorithms designs such as greedy optimization, dive and conquer, dynamic programming, network flows, reduction and randomized algorithms. | 3 | - | 3 |
151096100 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science. Topics covered include the evolution of data science, its relation to machine learning and data infrastructure uses. 151096190 BUSINESS STATISTICS This course is an overview of the range of statistical techniques necessary for decision making. Topics include descriptive statistics, probability distribution, sampling theory, statistical inference, hypothesis testing, experimental design and analysis of variance and linear and multiple regressions. |
| 151096330 | MARKETING ANALYTICS | This course provides an overview of the field of marketing analytics and how data analysis and mining are used to make strategic marketing decisions. The emphasis will be on using customer insights to make decisions that increase the value of a business. | 3 | - | 3 |
151096140 PRINCIPLES OF MARKETING This course provides an overview of the principles of marketing management and strategy and incorporates the skills learned in data science. Students will analyze customer data to make recommendations on customer segmentation, purchasing behavior and targeted marketing and pricing. |
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