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.
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.
In the rapidly evolving field of computational biology, the integration of artificial intelligence (AI) and machine learning (ML) is opening new frontiers in research and applications. This advanced course, designed for graduate-level students, delves into the transformative impact of AI and ML on computational biology, exploring both theoretical foundations and practical applications.
The utility of designed experiments has been recognized in the world
of business and marketing as a tool to increase conversion, strengthen
customer retention, and improve the bottom line. Companies like
Google, Amazon, Meta, Netflix, Airbnb and Lyft have all adopted
experimentation and A/B testing for these purposes. As such, data
science practitioners and professionals find experimentation a
foundational tenet of the field. This course provides an in-depth look
into statistical and computational techniques for designing and
analyzing experiments that are regularly used in tech and data science
companies. Concepts that will be covered include:
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.
This course covers:
This course covers:
This course aims to provide students with a foundational understanding of image processing using Python. Topics covered include: Basics of working with images, Intensity transformation, Spatial filtering, Frequency domain filtering, Morphological image processing, Color image processing, Image segmentation, Feature extraction, Image pattern classification. By the end of this course, students will have a comprehensive grasp of image processing techniques in Python.
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.
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.
IAD Master's students should fill out the summer formstack registration in order to enroll in the CDA courses.
Non-IAD masters students should confirm with their department if these classes can be used for their degree requirements. When ready to register, please submit a request via the force registration portal.
Email cdsedept@buffalo.edu for questions or assistance with class registration.