Summer short courses are designed to enhance the elective offerings available to IAD students, and for students in related programs at UB.
These ‘bite-size’ courses are designed to:
Students can ‘stack’ these 1-2 credit offerings to fulfill IAD degree requirements or enhance knowledge in critical areas that will make them more competitive in the marketplace. These electives are approved for students in both data science programs in IAD, to satisfy either the elective requirement or to replace the project course to finish the graduation requirements.
Non-IAD masters students interested in taking these courses for elective credit towards their degree should seek approval from their department before requesting registration.
Please scroll to the bottom of the webpage for registration information.
These summer courses run on non-standard class dates. The financial liability and add/drop dates vary for each individual course and students are responsible for reviewing these dates prior to enrollment via the student accounts website.
This course will cover data structures and algorithms that advanced Python programmers need to write code that runs faster and more efficiently. Topics will include Big O notation, linked-lists, searching, sorting, greedy algorithms, hashes, stacks, queues, and graphs. The course is designed to meet the demands of coding interviews.
This course gives an overview of Bayesian Networks with application in R. The focus will be Bayesian network modeling, from structural learning to parameter learning and inference. Classic discrete, Gaussian, and conditional Gaussian networks will be described. Applications will showcase the wealth of R packages dedicated to learning and inference.
Networks are everywhere. From the internet to our social interactions and even in our brain, it has become clear that we are surrounded by complex systems of interconnected elements. In this course, we will explore how one can better understand the world around us by thinking about systems as networks and examining the specific structure of how network elements are connected. What do social networks and brain networks have in common? Why is that important? How would we design a power grid so that it’s less likely to fail? We will initially focus on learning and applying network statistics that can measure and quantify static network structure and then introduce temporal and multilayer frameworks that can capture richer features of systems. An undergraduate level understanding of linear algebra will be expected.
This course covers:
This course covers:
This course covers:
Analysis of Variance: 1-way ANOVA, Multiple Range Tests, Kruskal-Wallis Test, Welch’s ANOVA, 2-Way ANOVA, ANOVA with Interaction, Model Adequacy Checking
Regression Analysis: Correlation, Simple Linear Regression, Quadratic Regression, Transformations in Regression, Multiple Regression
Storytelling Through Data Visualization is designed to introduce students to the principles and best practices of data visualization. Students will learn how to effectively communicate data and information through visual storytelling. The course will cover topics such as data preparation, chart selection, design principles, and interactive visualization, through hands-on exposure to industry-standard data visualization software platforms.
Time series are ordered series of data points collected over time. This course is an introduction to the analysis of time series using R software. The main topics covered in this course include the following: basic characteristics and visualization of time series, autocorrelation, stationarity, ARIMA models, time series regression, seasonality, and forecasting.
This course will build on the topics explored in TSR1. It will start with additional time domain topics such as unit root testing, long-memory, and modeling volatility with autoregressive conditionally heteroskedastic (ARCH) specifications. These fundamentals will then be extended to state space models (also called dynamic linear models). This is a very general class of models that subsume many special cases of interest. Here we will explore prediction, filtering, smoothing and several special topics. Time permitting, we will discuss the frequency domain approach to time series analysis.
IAD Master's students should fill out the summer formstack registration in order to enroll in the CDA courses.
Non-IAD masters students should submit a request via the force registration portal.
Email cdsedept@buffalo.edu for questions or assistance with class registration.
