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.
This course focuses on the essential data structures and algorithms that Python programmers need to write efficient and optimized code. Key topics include Big O notation, data structures such as linked lists, hash tables, stacks, queues, graphs, and tries, as well as algorithms for sorting and data structure-specific operations. The course places a strong emphasis on solving LeetCode-style problems, helping students develop problem-solving skills and effectively prepare for coding interviews.
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: hypothesis testing under two and multiple conditions, randomization and factorial experimental design, A/B and A/B/C testing, modern experimentation in industry, the relationship between power, effective sample size and level of confidence and metrics for interpreting the effectiveness of an experiment.
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 introduces Network Analysis, focusing on Artificial Intelligence and Data Science applications. Topics include network modeling, community detection, structural learning, parameter estimation, and inference. Students will explore various types of networks, including discrete, Gaussian, and conditional Gaussian networks. Practical applications will highlight the use of tools and packages in R, showcasing their role in solving complex problems in data-driven fields.
This intermediate-level course in probability covers key concepts necessary for statistical analysis and advanced probability topics. It begins with an introduction to random variables, their properties, and distributions, including discrete random variables (Binomial, Poisson, Geometric, Negative Binomial) and continuous random variables (Exponential, Normal). Students will learn how to calculate mean, variance, and cumulative distribution functions (CDF). Advanced topics such as sums of random variables, covariance, independence, and transformations will also be explored. The course includes hands-on practice using Excel to calculate probabilities, with worksheets provided, so no prior Excel knowledge is required. Basic understanding of unions and intersections is assumed.
This course introduces statistical methods for analyzing experimental data to make informed decisions. Topics covered include one-sample analysis (confidence intervals and hypothesis testing for means, variances, and proportions), tests for normality, and two-sample analysis (two-sample means, paired t-tests, variances, and proportions). Additional focus is given to Chi-Square tests such as goodness of fit and tests for association. The course emphasizes the use of menu-driven statistical software for analysis, interpretation of results, and ensuring model assumptions are met, with no prior programming or formula knowledge required.
This course is a continuation of Analysis of Experimental Data 1, focusing on more complex data analysis and the design of experiments for efficient data extraction. Topics include Analysis of Variance (1-way ANOVA, Multiple Range Tests, Kruskal-Wallis, Welch’s ANOVA, 2-Way ANOVA, and ANOVA with Interaction), as well as Regression Analysis (correlation, simple and quadratic regression, multiple regression, and transformations). The course also covers advanced methods for checking model assumptions. Emphasis is placed on interpreting results, making predictions, and performing analyses using free statistical software.
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 .
Email cdsedept@buffalo.edu for questions or assistance with class registration.