Introduction to Data Science (Fall 2016): A project-based course that brings together methods, concepts and current practices in the growing field of data science, including statistical inference, financial modeling, data visualization, social networks and data engineering. Emphasis is on the ethical dilemmas involved in gathering, storing, analyzing and disseminating information from large databases. Learn: Statistical Thinking in the Age of Big Data, Exploratory Data Analysis (EDA), Kernel Density Estimation, Advanced Regression, Social Networks and Data Journalism, Financial Modeling, Reproducible Research and Sharing Your Work, Ethics and Privacy. APPLY: R Studio and Shiny, Python,GitHub
Experimental Statistics I (Fall 2016): This course gives an overview of statistical methods from an experimental design perspective. Students will review statistical sampling, T-tests, Analysis of Variance, Linear Regression and other skills. Rather than calculations, the course focuses on interpretation, analysis and communication of the results and ethics of statistical analysis. LEARN: Experimental Design, Statistical Sampling, T-tests, Analysis of Variance, Linear Regression, Diagnostics and Checks for Statistical Methods, Interpretation and Communication of Results (both oral and written), Ethics of Statistical Analysis APPLY: SAS, R.
File Organization & Database Management (Spring 2017): This course surveys current database approaches and systems, as well as the principles of design and the use of these systems. Students learn database query language design and implementation constraints as well as applications of large databases, including a survey of file structures and access techniques, such as NoSQL databases. Students will use a relational database management system to implement a database design project. LEARN: Database Queries, Relational Database Design, NoSQL Database APPLY: SQL, MySQL, MongoDB, XML, Python.
Data and Network Security: This course builds on Experimental Statistics I with attention to the analysis of multivariate data. Basic machine learning methods, such as linear discriminant analysis, logistic regression and principal components analysis, are discussed. Emphasis is on interpretation of the analysis rather than calculations. LEARN: Multiple Linear Regression and Variable Selection, Multivariate Analysis of Variance (MANOVA), Linear and Quadratic Discriminant Analysis, Unsupervised Learning (Clustering), Methods for Categorical Variables (Explanatory and Response), Autoregressive Models for Time Series Data, Basic Bootstrap. APPLY: SAS, R