Curriculum

To provide a comprehensive background in the mathematical topics required for learning Quantitative Finance (QF) and Business Analytics and Data Science (BADS). The mathematical topics covered include Calculus, Linear Algebra and Probability Theory. Applications of these topics in a variety of business contexts will be included.

This course enables you to transform data into persuasive dashboards that effectively inform and guide management actions. Dashboards are persuasive if they motivate actions in an intended audience. Dashboards are effective if they offer comprehensive and reliable information. This course introduces and discusses the fundamental design principles and technology of dashboards and allows you to design, implement, and critique dashboards. (FNCE 2402 & MSIS 2403)

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Data analytics involves the application of scientific methodologies to extract, understand, and make predictions based on data sets from a broad range of sources. Data analytics requires knowledge and skills from three areas: (i) programming, (ii) math/statistics, and (iii) domain specific expertise.

This course introduces participants to quantitative techniques and algorithms that are based on big and small data (numerical and textual). We also analyze theoretical models of big systems for prediction and optimization that are currently being used widely in business. It introduces topics that are often qualitative but that are now amenable to quantitative treatment. The course will prepare participants for more rigorous analysis of large data sets as well as introduce machine learning models and data analytics for business intelligence.

Covers the basic conceptual foundations and tools of econometrics and apply them to case studies with real-world data. The key statistical technique used in this course is multiple linear regression and R-programming.

Course presents technical and managerial approaches to the analysis, design, and management of business data, databases, and database management systems. The topics include structured and unstructured data management, a comparison of relational and object-oriented databases, relational database conceptual and logical design, and database implementation and administration.

This course is designed to provide a comprehensive introduction to forecasting methods used in Time Series Analysis. The class covers a range of topics in time series forecasting. The class will provide you with a language to describe time series data and ultimately cover modeling techniques such as ARIMA, SARIMA, and GARCH to produce forecasts

This course covers key issues in panel data analysis, with an emphasis on their applications in empirical research, especially empirical corporate finance. The course aims to introduce various econometric methods for analyzing panel data and develop core techniques to identify casual relations in the data. We will begin with the standard linear regressions, and extend to pooled, fixed effect, and random effect regression models, instrumental variables, differences-in-differences, selection models, and regression discontinuity. Students will be exposed to a broad range of applications in finance through reading academic papers and conducting their own empirical analysis.

This course is about big data and its role in carrying out modern business intelligence for actionable insight to address new business needs. This course is a lab-led and open source software rooted course. Students will learn the fundamentals of MapReduce, Spark framework, NoSQL databases, PySpark, and Amazon Athena. The class will focus on the storage, processing, and analysis aspects of big data. Students will use Spark cluster and MapReduce fundamentals to solve big data problems. Prerequisites: MSIS 2506, 2507 and 2503

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This course teaches students the fundamentals of Natural Language Processing (NLP). NLP has recently found several applications in business. There is now a foundation of content that students who wish to work in this field need to know and this course is aimed at providing students with a conceptual understanding of the field and its business applications, and a technical toolkit to implement NLP models.

Introduction to the topic of Deep Learning Neural Networks (DLNs), Linear Learning models using Logistic Regression, and adding hidden layers to create Deep Feed Forward Neural Networks. Detailed algorithms are used to train these networks using Stochastic Gradient Descent and the resulting algorithm called Backprop. Training processes of these networks are used with Tensor Flow tool and the MNIST and CIFAR-10 image data-sets. Some specialized DLN architectures including the following: (a) Convolutional Neural Networks (ConvNets), (b) Recurrent Neural Networks (RNNs), (c) Reinforcement Learning. Model parameter initialization, underfitting and overfitting are discussed as well as techniques such as Regularization. Issues such as the Vanishing Gradient problem that often cause problems during training are also discussed.

Reinforcement Learning is introduced as a way to do optimal control in cases when a system model is not available and information about the Value Function is obtained by analyzing its sample paths. RL Algorithms, Temporal Difference Learning, Q-Learning, on-policy and off-policy learning, policy exploration vs exploitation, Deep Learning Neural Networks as function approximators for RL systems, The Deep Q Network (DQN) algorithm, Policy Gradient methods such as the REINFORCE algorithm in combination with Value Function and Policy Gradient methods are explored. Applications of these concepts in the areas of Game Playing systems, Finance and Robotics are also discussed.

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The widespread adoption of hardware virtualization and the availability of low-cost computers and storage devices with high-capacity networks together with service-oriented architecture has led to growth in cloud computing. This course will study what technologies make Cloud Computing possible and how IT leverage these technologies to make the enterprise computing environment more efficient. There are three parts to this course. The first part will study how hardware virtualization is made possible through computer architecture advancement. The second part will discuss the two main solutions in the virtualization layer which are hypervisor-based virtualization and container-based virtualization. The third part of the course will study the microservices and the containers workflow orchestration. This course includes hands-on labs in virtual machines creation based on different technologies like hypervisors (VMware) and container (Docker). We will also explore different workflow orchestration tools like Docker Swarm and/or Google Kubernetes. (4 units)

This course enables you to explore data, identify insights, and develop evidence-based arguments using data visualization techniques. Completing this course equips you with a moderate level of data literacy, the ability to interpret, construct and convey arguments through the functional and truthful visual presentation of data. You will wrangle data, customize data visualization technologies, and programmatically develop data visualizations. (2 units)