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 emerging technologies in managing and innovating business processes.  Students will learn about the IT capabilities needed by firms to co-ordinate their activities, collaborate with business partners and make decisions.  The course will illustrate the role of technologies like IoT, Block Chain and AI and how these technologies interface with enterprise technologies such as ERP and CRM to improve business performance. Students will also learn about collaboration technologies such as XML, web services, and microservices.

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. 

Prerequisite: Emerging Technologies II (Applications of Blockchain)

The purpose of this graduate course is to provide students a theoretical as well as hand-on practical understanding of smart contracts as used in Blockchain applications. While this course will briefly review core Blockchain concepts, the students are expected to have an understanding of Blockchain technology components. Smart contracts give Blockchain the potential to create enterprise applications by codifying the agreements between the multiple parties involved in a transaction. This course will provide students an in-depth understanding for implementing smart contracts within Enterprise Ethereum and HyperLedger. Students will be expected to implement prototype smart contract based business applications using both Ethereum and Hyperledger.

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 focused on three main FinTech areas in which technology is driving changes in the way financial services are provided: (i) Lending/Banking services, (ii) wealth management (iii) Trading. The course is going to provide specific coverage and examples of developments from (1) payments (2) peer-to-peer lending (3) robo-advising (4) algo/quantitative trading. In each of these areas, we start by analyzing the marketing place, the incumbents, and the business case and strategies of the incoming technology players. We then turn to understand the role data and analytics plays in driving the technology-based services. Guest speakers augment the discussion by offering their perspective on future trends in each of these areas. 

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.

Prerequisite: Emerging Technologies II (Applications of Blockchain)

The purpose of this graduate course is to provide students a theoretical as well as hand-on practical understanding of smart contracts as used in Blockchain applications. While this course will briefly review core Blockchain concepts, the students are expected to have an understanding of Blockchain technology components. Smart contracts give Blockchain the potential to create enterprise applications by codifying the agreements between the multiple parties involved in a transaction. This course will provide students an in-depth understanding for implementing smart contracts within Enterprise Ethereum and HyperLedger. Students will be expected to implement prototype smart contract based business applications using both Ethereum and Hyperledger.

This course introduces students to the role of emerging technologies in managing and innovating business processes.  Students will learn about the IT capabilities needed by firms to co-ordinate their activities, collaborate with business partners and make decisions.  The course will illustrate the role of technologies like IoT, Block Chain and AI and how these technologies interface with enterprise technologies such as ERP and CRM to improve business performance. Students will also learn about collaboration technologies such as XML, web services, and microservices.

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 focused on three main FinTech areas in which technology is driving changes in the way financial services are provided: (i) Lending/Banking services, (ii) wealth management (iii) Trading. The course is going to provide specific coverage and examples of developments from (1) payments (2) peer-to-peer lending (3) robo-advising (4) algo/quantitative trading. In each of these areas, we start by analyzing the marketing place, the incumbents, and the business case and strategies of the incoming technology players. We then turn to understand the role data and analytics plays in driving the technology-based services. Guest speakers augment the discussion by offering their perspective on future trends in each of these areas. 

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 emerging technologies in managing and innovating business processes.  Students will learn about the IT capabilities needed by firms to co-ordinate their activities, collaborate with business partners and make decisions.  The course will illustrate the role of technologies like IoT, Block Chain and AI and how these technologies interface with enterprise technologies such as ERP and CRM to improve business performance. Students will also learn about collaboration technologies such as XML, web services, and microservices.

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. 

Prerequisite: Emerging Technologies II (Applications of Blockchain)

The purpose of this graduate course is to provide students a theoretical as well as hand-on practical understanding of smart contracts as used in Blockchain applications. While this course will briefly review core Blockchain concepts, the students are expected to have an understanding of Blockchain technology components. Smart contracts give Blockchain the potential to create enterprise applications by codifying the agreements between the multiple parties involved in a transaction. This course will provide students an in-depth understanding for implementing smart contracts within Enterprise Ethereum and HyperLedger. Students will be expected to implement prototype smart contract based business applications using both Ethereum and Hyperledger.

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 focused on three main FinTech areas in which technology is driving changes in the way financial services are provided: (i) Lending/Banking services, (ii) wealth management (iii) Trading. The course is going to provide specific coverage and examples of developments from (1) payments (2) peer-to-peer lending (3) robo-advising (4) algo/quantitative trading. In each of these areas, we start by analyzing the marketing place, the incumbents, and the business case and strategies of the incoming technology players. We then turn to understand the role data and analytics plays in driving the technology-based services. Guest speakers augment the discussion by offering their perspective on future trends in each of these areas. 

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.

Prerequisite: Emerging Technologies II (Applications of Blockchain)

The purpose of this graduate course is to provide students a theoretical as well as hand-on practical understanding of smart contracts as used in Blockchain applications. While this course will briefly review core Blockchain concepts, the students are expected to have an understanding of Blockchain technology components. Smart contracts give Blockchain the potential to create enterprise applications by codifying the agreements between the multiple parties involved in a transaction. This course will provide students an in-depth understanding for implementing smart contracts within Enterprise Ethereum and HyperLedger. Students will be expected to implement prototype smart contract based business applications using both Ethereum and Hyperledger.

This course introduces students to the role of emerging technologies in managing and innovating business processes.  Students will learn about the IT capabilities needed by firms to co-ordinate their activities, collaborate with business partners and make decisions.  The course will illustrate the role of technologies like IoT, Block Chain and AI and how these technologies interface with enterprise technologies such as ERP and CRM to improve business performance. Students will also learn about collaboration technologies such as XML, web services, and microservices.

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 focused on three main FinTech areas in which technology is driving changes in the way financial services are provided: (i) Lending/Banking services, (ii) wealth management (iii) Trading. The course is going to provide specific coverage and examples of developments from (1) payments (2) peer-to-peer lending (3) robo-advising (4) algo/quantitative trading. In each of these areas, we start by analyzing the marketing place, the incumbents, and the business case and strategies of the incoming technology players. We then turn to understand the role data and analytics plays in driving the technology-based services. Guest speakers augment the discussion by offering their perspective on future trends in each of these areas.