University of Texas Arlington

The world is awash in data and companies are now trying to discern patterns and predict behaviors of both consumers and competitors to gain and sustain a competitive advantage. The unstructured nature of data as well as the myriad sources they come from make it particularly challenging for companies to systematically capture, cleanse, store, and analyze the data. Python is a simple yet powerful language that has a rich ecosystem to facilitate the analysis of such complex data. The aim of this course is to acquaint students with aspects of the Python language that are necessary to effectively function as a data scientist. Upon successful completion of the course, students will be familiar with data structures and programming constructs in the Python language, accessing data from files and databases, Market-Basket Analysis, Text Analytics, and Map-Reduce.

Prerequisite: INSY 5336 (Python) and INSY 5339 (Data Mining)

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Learning Objectives: The aim of this course is to acquaint students with aspects of the Python language that are necessary to effectively function as a “data scientist”. Upon successful completion of the course, students will be familiar with:

a.         Data structures and programming constructs in the Python language. Specifically, students will have a good grasp of lists, tuples, dictionaries, classes, selection (e.g., if ..else), and iteration (e.g., while and for loops).

b.         Accessing data from files (e.g., text, csv, JSON, etc.) and databases.

c.         Market-Basket Analysis using R

d.         Text Analytics in Python & R, including topic modeling and sentiment analysis

e.         Machine learning algorithms using Scikit-learn.

f.          Basics of Social Network Analysis using Networkx

This course introduces you to the world of IS & Analytics research.  The first and foremost objective of this course is to nurture you into a first-rate scholar for the IS field.  We will provide a stimulating environment for the first phase of your development and growth as a scholar.  We will foster the ability to critically think and constructively criticize research papers in the IS field, as well as begin to form the foundation for building your own schema for the field.  In addition, we will provide each student an opportunity to conduct a research study.  Our goal is to help you publish in top IS journals while still in the doctoral program.

Specifically, this course will provide:

An overview of key articles pertaining to the area of Information Systems and Business Analytics, and introduce key theoretical perspectives that allow IS phenomena to be examined from different vantage points.  The underlying theories include transaction cost economics, theory of reasoned action and planned behaviors, resource-based view of a firm, and distributed cognition theory, to name but a few.

A broad survey of research studies related to IS/IT management that illustrate the applications of the various theoretical perspectives.  Topics for the course include, but may not be limited to:

business analytics

knowledge management

IS use and success

Business value of IT

Agile software development

IS strategy and management

Business Analytics

Social Networks

A broad but in-depth coverage of the research methodologies and the current theoretical frontier in the IS field.

The course covers a broad range of concepts and analytical techniques used in Social Network Analysis (SNA). Networks are engendered by relationships and/or connections between actors (such as humans, hospitals, and organizations). The proliferation of social media has increased the number and variety of networks. Organizations are increasingly relying on SNA and other analytical techniques to gain insights from such networks. This course will equip students with the skills necessary to help firms in their endeavor to compete on analytics.

An introductory programming course that teaches students how to solve business problems using the scripting language, Python. Students will be exposed to object-oriented programming concepts, file handling, database access, and graphical user interfaces. 

This course addresses the concepts and principles of Big Data and how Big Data can be used in the Enterprise. The course starts with an overview of the fundamental principles of Big Data and its role in making better decisions and predictions in the organization. Following the Fundamentals of Big Data, we address the Technology, Infrastructure and Applications of Big Data. The Software and Application requirements of Big Data are discussed and a number of case studies of Big Data Applications are studied. 

The world is awash in data and companies are now trying to discern patterns and predict behaviors of both consumers and competitors to gain and sustain a competitive advantage. The unstructured nature of data as well as the myriad sources they come from make it particularly challenging for companies to systematically capture, cleanse, store, and analyze the data. Python is a simple yet powerful language that has a rich ecosystem to facilitate the analysis of such complex data. The aim of this course is to acquaint students with aspects of the Python language that are necessary to effectively function as a data scientist. Upon successful completion of the course, students will be familiar with data structures and programming constructs in the Python language, accessing data from files and databases, Market-Basket Analysis, Text Analytics, and Map-Reduce. 

This course addresses the concepts and principles of Big Data and how Big Data can be used in the Enterprise. The course starts with an overview of the fundamental principles of Big Data and its role in making better decisions and predictions in the organization. Following the Fundamentals of Big Data, we address the Technology, Infrastructure and Applications of Big Data. The Software and Application requirements of Big Data are discussed and a number of case studies of Big Data Applications are studied. 

The course covers a broad range of concepts and analytical techniques used in Social Network Analysis (SNA). Networks are engendered by relationships and/or connections between actors (such as humans, hospitals, and organizations). The proliferation of social media has increased the number and variety of networks. Organizations are increasingly relying on SNA and other analytical techniques to gain insights from such networks. This course will equip students with the skills necessary to help firms in their endeavor to compete on analytics.

This course addresses the concepts and principles of Big Data and how Big Data can be used in the Enterprise. The course starts with an overview of the fundamental principles of Big Data and its role in making better decisions and predictions in the organization. Following the Fundamentals of Big Data, we address the Technology, Infrastructure and Applications of Big Data. The Software and Application requirements of Big Data are discussed and a number of case studies of Big Data Applications are studied. 

This course covers topics on the management of information technologies (IT) from the view point of senior managers. Subjects discussed include the strategic role of IT to gain competitive advantage, Internet-based business models, building a lean and agile organization through IT, managing IT security and reliability, evolving models of IT service delivery, such as cloud computing and open source, management of outsourcing, IT governance, and ethical issues in the digital era. In addition to classroom lectures, the course relies heavily on case analysis and discussion to provide a real world perspective of issues related to IT management.

This course covers topics on the management of information technologies (IT) from the view point of senior managers. Subjects discussed include the strategic role of IT to gain competitive advantage, Internet-based business models, building a lean and agile organization through IT, managing IT security and reliability, evolving models of IT service delivery, such as cloud computing and open source, management of outsourcing, IT governance, and ethical issues in the digital era. In addition to classroom lectures, the course relies heavily on case analysis and discussion to provide a real world perspective of issues related to IT management.

This course addresses the concepts and principles of Big Data and how Big Data can be used in the Enterprise. The course starts with an overview of the fundamental principles of Big Data and its role in making better decisions and predictions in the organization. Following the Fundamentals of Big Data, we address the Technology, Infrastructure and Applications of Big Data. The Software and Application requirements of Big Data are discussed and a number of case studies of Big Data Applications are studied. 

This course introduces you to the world of IS research.  The first and foremost objective of this course is to nurture you into a first-rate scholar for the IS field.  We will provide a stimulating environment for the first phase of your development and growth as a scholar.  We will foster the ability to critically think and constructively criticize research papers in the IS field, as well as begin to form the foundation for building your own schema for the field.  In addition, we will provide each student an opportunity to conduct a research study.  Our goal is to help you publish in top IS journals while still in the doctoral program. 

An introductory programming course that teaches students how to solve business problems using the scripting language, Python. Students will be exposed to object-oriented programming concepts, file handling, database access, and graphical user interfaces. 

The world is awash in data and companies are now trying to discern patterns and predict behaviors of both consumers and competitors to gain and sustain a competitive advantage. The unstructured nature of data as well as the myriad sources they come from make it particularly challenging for companies to systematically capture, cleanse, store, and analyze the data. Python is a simple yet powerful language that has a rich ecosystem to facilitate the analysis of such complex data. The aim of this course is to acquaint students with aspects of the Python language that are necessary to effectively function as a data scientist. Upon successful completion of the course, students will be familiar with data structures and programming constructs in the Python language, accessing data from files and databases, Market-Basket Analysis, Text Analytics, and Map-Reduce. 

An introductory programming course that teaches students how to solve business problems using the scripting language, Python. Students will be exposed to object-oriented programming concepts, file handling, database access, and graphical user interfaces.

An introductory programming course that teaches students how to solve business problems using the scripting language, Python. Students will be exposed to object-oriented programming concepts, file handling, database access, and graphical user interfaces.

This course introduces students to the concepts and practices of project management and their importance to improving the success of information technology projects. Distinct aspects or characteristics of IT projects which cause these projects to behave differently in the corporate world than do other, non-technical, projects will be discussed.

The primary objective of this course is to expose students to research issues in software development, including object-oriented analysis & design and agile methodologies. The articles used in the course are drawn from premier publication outlets such as MISQ, ISR, and IEEE Transactions on Software Engineering. In addition, the readings include a number of articles from small-group research that deal with group dynamics, team cognition/mental models, transactive memories and creativity in teams.  Research in the nascent area of analytics will be introduced as well.

This course introduces students to the concepts and practices of project management and their importance to improving the success of information technology projects. Distinct aspects or characteristics of IT projects which cause these projects to behave differently in the corporate world than do other, non-technical, projects will be discussed.

This course studies the concepts, issues and approaches important in effectively managing projects.  Topics include project selection, organizational structures, project planning, estimation, scheduling, resource allocation, risk management, crashing, earned value analysis and agile project management.  Topics are viewed from a managerial perspective.

This course covers concepts, tools, and technologies associated with the analysis and design of information systems using the object-oriented (OO) paradigm.  OO concepts are discussed and OO systems development life cycle is introduced.  OO analysis and design techniques using the UML (Unified Modeling Language) are discussed.  Students get hands on training in analysis and design through a group project. 

This course studies the concepts, issues and approaches important in effectively managing projects.  Topics include project selection, organizational structures, project planning, estimation, scheduling, resource allocation, risk management, crashing, earned value analysis and agile project management.  Topics are viewed from a managerial perspective.

This course studies the concepts, issues and approaches important in effectively managing projects.  Topics include project selection, organizational structures, project planning, estimation, scheduling, resource allocation, risk management, crashing, earned value analysis and agile project management.  Topics are viewed from a managerial perspective.

This course studies the concepts, issues and approaches important in effectively managing projects.  Topics include project selection, organizational structures, project planning, estimation, scheduling, resource allocation, risk management, crashing, earned value analysis and agile project management.  Topics are viewed from a managerial perspective.

The world is awash in data and companies are now trying to discern patterns and predict behaviors – of both consumers and competitors – to gain and sustain a competitive advantage. The unstructured nature of data as well as the myriad sources they come from make it particularly challenging for companies to systematically capture, cleanse, store, and analyze the data. Python is a simple yet powerful language that has a rich ecosystem to facilitate the analysis of such complex data. The aim of this course is to acquaint students with aspects of the Python language that are necessary to effectively function as a “data scientist”. Upon successful completion of the course, students will be familiar with:

a.         Data structures and programming constructs in the Python language. Specifically, students will have a good grasp of lists, tuples, dictionaries, classes, selection (e.g., if ..else), and iteration (e.g., while and for loops).

b.         Accessing data from files (e.g., text, csv, JSON, etc.) and databases. Students will also be exposed to the fundamentals of SQL.

c.         Map-Reduce, the abstraction from Google that inspired Hadoop. Specifically, we will use a lightweight Python implementation of map-reduce.

d.         Social Network analysis using the Networkx module.

e.         Machine learning algorithms using Scikit-learn.

f.          Basics of Python-R interface.

The world is awash in data and companies are now trying to discern patterns and predict behaviors – of both consumers and competitors – to gain and sustain a competitive advantage. The unstructured nature of data as well as the myriad sources they come from make it particularly challenging for companies to systematically capture, cleanse, store, and analyze the data. Python is a simple yet powerful language that has a rich ecosystem to facilitate the analysis of such complex data. The aim of this course is to acquaint students with aspects of the Python language that are necessary to effectively function as a “data scientist”. Upon successful completion of the course, students will be familiar with:

a.         Data structures and programming constructs in the Python language. Specifically, students will have a good grasp of lists, tuples, dictionaries, classes, selection (e.g., if ..else), and iteration (e.g., while and for loops).

b.         Accessing data from files (e.g., text, csv, JSON, etc.) and databases. Students will also be exposed to the fundamentals of SQL.

c.         Map-Reduce, the abstraction from Google that inspired Hadoop. Specifically, we will use a lightweight Python implementation of map-reduce.

d.         Social Network analysis using the Networkx module.

e.         Machine learning algorithms using Scikit-learn.

f.          Basics of Python-R interface.

The course is designed to provide a broad managerial view of management and deployment of IT resources.  Information technology (IT) has dramatically altered the way organizations conduct business and compete in a global marketplace.  The commercialization of the Internet has created new electronic market places, and new channels of supply and distribution.  New business models are continuing to emerge and challenge our notion of how best to organize a business.  This course aims to discuss the challenges of managing a business in a global networked economy.  It provides an understanding of the influence of IT on business decisions from a senior management perspective.  The implications of emerging trends such as cloud computing, social media, mobile computing, and business analytics will also be discussed.

Analysis and design phase of systems development life cycle. Topics include systems survey, functional specification, interface specification, data design, program design, system testing, and implementation. 

This course provides a solid treatment of foundation principles but in the context of all the new tools, technologies, and approaches to systems development.

Analysis and design phase of systems development life cycle. Topics include systems survey, functional specification, interface specification, data design, program design, system testing, and implementation. 

This course provides a solid treatment of foundation principles but in the context of all the new tools, technologies, and approaches to systems development.

The course is designed to provide INSY majors, who already have a strong technical background, a broad managerial view of deployment and management of IT resources.  Information technology (IT) has dramatically altered the way organizations conduct business and compete in a global marketplace.  The commercialization of the Internet has created new electronic market places, and new channels of supply and distribution.  New business models are continuing to emerge and challenge our notion of how best to organize a business.  This course aims to discuss the challenges of managing a business in a global networked economy.  It provides an understanding of the influence of IT on business decisions from a senior management perspective.  

This course studies the concepts, issues and approaches important in effectively managing projects.Topics include project selection, project planning, negotiation, budgeting, scheduling, resource allocation, project control, project auditing, and project termination.Topics are viewed from a managerial perspective.

This course studies the concepts, issues and approaches important in effectively managing projects.Topics include project selection, project planning, negotiation, budgeting, scheduling, resource allocation, project control, project auditing, and project termination.Topics are viewed from a managerial perspective.

The course is designed to provide INSY majors, who already have a strong technical background, a broad managerial view of deployment and management of IT resources.Information technology (IT) has dramatically altered the way organizations conduct business and compete in a global marketplace.The commercialization of the Internet has created new electronic market places, and new channels of supply and distribution.New business models are continuing to emerge and challenge our notion of how best to organize a business.This course aims to discuss the challenges of managing a business in a global networked economy.It provides an understanding of the influence of IT on business decisions from a senior management perspective.

The objectives of the course are to:

1.Provide a process-oriented view of organizations.

2.Provide an understanding of how end-to-end business processes work in organizations.

3.Facilitate an understanding of the role of ERP (enterprise resource planning) systems.

4.Give students a hands-on experience with a real-world ERP system.

5.Expose students to emerging trends in enterprise software development/deployment/architectures, including SOA (service-oriented architecture), cloud-computing, and business intelligence.