Curriculum

This course is an introduction to financial management fundamentals. It examines the roles of financial management in creating value and to present the analytic framework used in the study of finance.

This is a project-based course exploring all aspects of the IoT product life-cycle. These aspects include, (i) interfacing with the devices (sensors/actuators) that collect data and affect the environment, (ii) exploring network protocols for communication with these constrained devices, (iii) programming the back-end services that host, manipulate and disseminate the collected data and (iv) developing apps that facilitate human interaction with these devices and analysis of the data they produce. Security, privacy and performance considerations specific to IoT will also be explored.  

This course builds skills in generating business and social insights from user generated content (e.g., text, images, video, etc.) by the use of text analytics, sentiment analysis, visualization techniques, etc.  

Data driven thinking is at the critical frontier in business operations and decision making. This course introduces basic concepts, methodology, algorithms, and technology used in business analytics and decision making. We introduce fundamental concepts from probabilistic modeling, analysis and experimental design. We also cover the basics of modern regression and classification, clustering, visualization, dimensionality reduction, A/B Testing and an introduction to deep learning. 

This is a practicum course in which students apply their learning in the MSITM program to develop real-life business and social solutions using emerging information technologies. Industry partners will provide business context and ideas for IT capstone projects. Students can also propose their own IT Capstone project ideas.

The last decade has brought remarkable advances in Machine Learning algorithms, platforms and computing architectures. The frontiers what data science and AI can accomplish is currently progressing much faster than what was previously considered possible. Even for those not on the front lines of research, a basic working knowledge of the latest algorithms, business applications and limitations 
can can provide a fundamental advantage. 
 
This course will explore precisely this, through hands on, project-based coverage of Deep Learning and Artificial Neural Networks. The course will use Python and Tensorflow, among other tools. Covered material includes convolutional neural networks, recurrent neural networks and generative adversarial networks.
Applications include computer vision, image and time series modeling as well as computational aspects of deep learning over big datasets. 
 
Students are expected to have a basic knowledge of machine learning and data science, as well as a basic knowledge of Python.

This course develops skills in strategic management of new IT-embedded product and service innovations and their incorporation into the digital business ecosystems of organizations.

This course introduces students to the role of Information Technology in managing Supply Chains.  Students will learn about the IT capabilities needed by firms to co-ordinate their operations, collaborate with business partners and manage uncertainty.  The course will illustrate the role of technologies and tools like ERP platform, ABAP programming, XML, web services, distributed computing and machine learning to improve the performance of supply chains. 

This course builds skills for the prevention and mitigation of data security and privacy risks in newly designed digital artifacts by covering IT governance, risk, and control frameworks and relevant laws, regulations, and industry standards. 

In this course we will study a variety of data mining and machine techniques for descriptive, predictive and prescriptive analytics. Particular emphasis will be given to approaches that are (i) scalable to very large data sets, (ii) relatively robust when faced with a large number of predictors, (iii) can be applied to heterogeneous or streaming data. Approaches to analyze different types of information from the Web (web structure, content, usage) will also be described. Connections to relevant business problems shall be made via example studies. The central goal of this course is to convey an understanding of the pros and cons of different predictive modeling techniques, so that you can make an informed decision on what approaches to consider when faced with real-life problems, and can apply models properly on real datasets in order to make valid conclusions. This goal will be reinforced through both theory and hands-on experience involving python based libraries such as sci-kit learn and Spark MLlib.

This course is an introduction to financial management fundamentals. It examines the roles of financial management in creating value and to present the analytic framework used in the study of finance.

This is a project-based course exploring all aspects of the IoT product life-cycle. These aspects include, (i) interfacing with the devices (sensors/actuators) that collect data and affect the environment, (ii) exploring network protocols for communication with these constrained devices, (iii) programming the back-end services that host, manipulate and disseminate the collected data and (iv) developing apps that facilitate human interaction with these devices and analysis of the data they produce. Security, privacy and performance considerations specific to IoT will also be explored.  

This course builds skills in generating business and social insights from user generated content (e.g., text, images, video, etc.) by the use of text analytics, sentiment analysis, visualization techniques, etc.  

Data driven thinking is at the critical frontier in business operations and decision making. This course introduces basic concepts, methodology, algorithms, and technology used in business analytics and decision making. We introduce fundamental concepts from probabilistic modeling, analysis and experimental design. We also cover the basics of modern regression and classification, clustering, visualization, dimensionality reduction, A/B Testing and an introduction to deep learning. 

This is a practicum course in which students apply their learning in the MSITM program to develop real-life business and social solutions using emerging information technologies. Industry partners will provide business context and ideas for IT capstone projects. Students can also propose their own IT Capstone project ideas.

The last decade has brought remarkable advances in Machine Learning algorithms, platforms and computing architectures. The frontiers what data science and AI can accomplish is currently progressing much faster than what was previously considered possible. Even for those not on the front lines of research, a basic working knowledge of the latest algorithms, business applications and limitations 
can can provide a fundamental advantage. 
 
This course will explore precisely this, through hands on, project-based coverage of Deep Learning and Artificial Neural Networks. The course will use Python and Tensorflow, among other tools. Covered material includes convolutional neural networks, recurrent neural networks and generative adversarial networks.
Applications include computer vision, image and time series modeling as well as computational aspects of deep learning over big datasets. 
 
Students are expected to have a basic knowledge of machine learning and data science, as well as a basic knowledge of Python.

This course develops skills in strategic management of new IT-embedded product and service innovations and their incorporation into the digital business ecosystems of organizations.

This course introduces students to the role of Information Technology in managing Supply Chains.  Students will learn about the IT capabilities needed by firms to co-ordinate their operations, collaborate with business partners and manage uncertainty.  The course will illustrate the role of technologies and tools like ERP platform, ABAP programming, XML, web services, distributed computing and machine learning to improve the performance of supply chains. 

This course builds skills for the prevention and mitigation of data security and privacy risks in newly designed digital artifacts by covering IT governance, risk, and control frameworks and relevant laws, regulations, and industry standards. 

In this course we will study a variety of data mining and machine techniques for descriptive, predictive and prescriptive analytics. Particular emphasis will be given to approaches that are (i) scalable to very large data sets, (ii) relatively robust when faced with a large number of predictors, (iii) can be applied to heterogeneous or streaming data. Approaches to analyze different types of information from the Web (web structure, content, usage) will also be described. Connections to relevant business problems shall be made via example studies. The central goal of this course is to convey an understanding of the pros and cons of different predictive modeling techniques, so that you can make an informed decision on what approaches to consider when faced with real-life problems, and can apply models properly on real datasets in order to make valid conclusions. This goal will be reinforced through both theory and hands-on experience involving python based libraries such as sci-kit learn and Spark MLlib.