Math plays an integral part in building the type of sound understanding of risk assessment you will be expected to develop as a SAIS student. During the Math Review for Risk Assessment we will cover the following topics: Fundamentals of Algebra; Graphs and Functions; Systems of Equations; Limits and Derivatives; Lagrange Multipliers; and Introduction to Probability Distributions.

This course is designed to furnish students with the fundamental tools of statistical analysis, including analysis of descriptive statistics, probability distributions, statistical inference and related tests, correlation and conditional expectation. Aim of the course is to introduce the basic statistical tools required to conduct and evaluate empirical research in economics and the social sciences. Special attention will be given to the application of these statistical tools to the analysis of real phenomena.

Today's world relies a lot on the accumulation, presentation, and interpretation of large quantities of information. Statistics and Econometrics are tools that enable us to organize our data in an efficient manner and provide us with methods that help to understand the relationships that occur in our data and our increasingly complex world. In this course, we will draw examples from multiple disciplines, such as political science and economics, to demonstrate how to search for and evaluate patterns in large amounts of data, as well as to interpret what these patterns tell us about the world. The course develops tools for estimating functional relationships and critically reading empirical studies that use different econometric techniques.

Nowadays datasets that have relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, bar-code readers, and intelligent machines. Such datasets are often stored in data warehouses specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. Many successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, stock market investments, and so on. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods and Machine Learning algorithms that have emerged from both fields and proven to be of value in recognizing patterns and making predictions. We will survey applications and provide an opportunity for hands-on experimentation with Machine Learning algorithms for data mining using Python. Prerequisite: Statistics for Data Analysis.