Financial Analytics

In this course students learn to differentiate relational and non-relational databases, and to design a database to an organization's requirements. Preparing raw data for import into a database, using queries to manipulate data, and developing reports to meet stakeholder needs, and database integrity and security are also covered.

Students learn to create and use scripts to access, acquire, prepare, and transform data, including structured and semi-structured data, and to define and detect patterns in unstructured data to demonstrate how real world problems can be answered with data.

In this course students learn about data types and how to perform pre-processing activities including cleaning, integration, reduction, and transformation. Use of appropriate techniques to analyze and model data, such as classification, decision trees, association rules, and clustering, and the causes and methods of detecting outliers are also covered. Students propose additional applications for data mining based on current trends/research.

In this course students learn about deep learning, machine learning, and artificial intelligence, and compare supervised and unsupervised learning. Machine learning workflows and the challenges associated with machine learning, such as bias and heteroscedasticity, are considered. Students evaluate models, and examine uses for machine learning and artificial intelligence. Natural Language Processing is also addressed.

In this course, students will delve into the ethical considerations, regulatory landscape, and risk management strategies crucial for financial professionals. Students will also address real-world ethical dilemmas, understand key financial regulations, and apply principles of risk management to financial cases and scenarios.

In this course, students will use advanced techniques for creating robust financial models and making effective financial forecasts. The course emphasizes the practical application of financial tools used in forecasting and the utilization of real-world scenarios.

In this course, students will delve into the application of Time Series Analysis in finance, and focus on the statistical techniques and models essential for analyzing financial data, forecasting market trends, and making informed investment decisions. The course blends advanced financial concepts with practical applications, using real-world financial data to illustrate the nuances of observing a variable's outcomes over different time periods.

In this course, students will apply sophisticated statistical methods and leverage machine learning techniques tailored towards making well-informed decisions in the finance sector. This course focuses on practical, real-world application and hands-on experience that mirrors the complexities and dynamics of financial analytics across industries.

In this course, students will explore the art and science of presenting financial data in a visual format. Using modern data visualization tools and technologies, students will learn how to represent complex financial data sets in an understandable and interactive manner, tailoring their visualizations to diverse audiences, from financial experts to the general public.

Students will integrate learning and competencies from the Business Analytics program into a single data-based project.? Given a data set, students will ask such questions as why is the data organized as it is, what are the benefits and limitations of the data set, what appropriate visualization elements should be developed, and what business recommendations should be made.