STUDENTS PLEASE DO NOT REGISTER UNTIL YOU HAVE BEEN ADVISED. YOU CAN EMAIL THE CHAIR OF DSA JOAQUIN CARBONARA, THE ASSOCIATE CHAIR, WENDE MIX OR THE PROGRAM COORDINATOR, HEATHER CAMPBELL.
COURSE | FORMAT | TIME | CRN | FACULTY |
MAT 616 | In-person | TR 4:30 - 5:45 | 3856 | Dr. Joaquin Carbonara |
MAT 646 | In-person | TR 4:30 - 5:45 | 2335 | Dr. Chaitali Ghosh |
PSM 602 | In-person |
W 4:30 - 7:10 |
1695 | David Jacome |
DSA 601 | In-person | W 4:30 - 7:10 | 2552 | Sumanlata Ghosh and Matt Nagowski |
DSA 610 | In-person | M 4:30 - 7:10 | 2635 | Betsy McCall |
DSA 650 | In-person | T 6:00 - 8:40 | 2509 | Angela Horton & Jessica Copeland |
GEG 584 | In-person | R 7:30 - 8:55 | 2091 | Dr. Wende Mix |
GEG 585 | In-person | TR 4:30 - 5:55 | 2261 | Dr. Wende Mix |
HEA 730 | Online Asynchronous | NA | 3655 | Dr. Patrick McDonald |
BIO 670 | In-person | T 4:30 - 7:15 | 2570 | Dr. Robert Waren |
ENT 581 | In-person | MW 4:30 - 6:45 | 3920 | Dr. Nick Gilewski |
DSA 690 | Online Asynchronous | NA | 2802 | Dr. Wende Mix |
If you have any questions or issues with registration please contact the Program Coordinator Heather Camppbell at campbehm@buffalostate.edu.
For instruction on how to search for classes, register or withdraw click this link and look under the GET HELP section on the right hand side.
Prerequisites: Instructor Permission
Introductory topics in calculus, optimization, linear algebra and discrete mathematics useful for data scientists. Networking concepts relevant to data analytics approached from a mathematical point of view. Mathematical programming to implement a variety of numerical methods.
Prerequisite: Instructor permission.
Descriptive statistics, probability concepts, discrete and continuous probability distributions, sampling distributions, interval estimation and hypothesis testing of one and two population means, proportions and variances, non-parametric tests, simple linear regression and correlation, one-way analysis of variance. Offered every fall semester.
Prerequisites: Graduate-level standing.
Intend to develop strategic thinking about communication of quantitative information and improve writing, presentation, and interpersonal communication skills for mathematicians and scientists in a variety of settings (i.e. industrial, managerial, academic, research). Includes a review of “best practices” or guidelines that have been derived from both research and experience. Students will put those guidelines into practice, using a workshop format that will rely heavily on discussion and in-class exercises.
Prerequisites: MAT 126, MAT 311, CIS 512, or Instructor permission
Applied introduction to building predictive, machine-learning models for real-world problems; learning Python computing environment, basic data analysis, management; data visualization and reporting using machine learning methods, including k-nearest neighbor, linear models, naive Bayesian models, decision trees, random forests, and neural networks. Sample data sets from across industry professions.
Prerequisite: Graduate status.
Introduction to a “big picture” understanding of data flow for strategic, data-driven decision making, including data storage, data organization, data gathering and preparation, exploratory data analysis, and meaningful visualizations and communication. Includes hands-on practice. Offered occasionally, beginning spring 2020.
Prerequisites: Instructor permission
Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security.
Prerequisites: Instructor permission, programming experience required
Introduction to Python programming focusing on the development of Python scripts and custom tools for processing and analysis of geospatial data. Automating geoprocessing workflows, creating custom analysis tool, and customizing user interfaces.
Prerequisites: GEG 425, GEG 525, or equivalent introductory GIS course.
Introduction to interactive and Web-based mapping. Different approaches to communicating with maps on the Internet; how to create Web-based mapping applications.
This course will cover the fundamentals of effective data-driven storytelling. Students will learn how to analyze data, detect stories within datasets and communicate findings in oral, written, and interactive visual delivery modes for various audiences.
Prerequisites: Graduate status.
This course will have 3 components; internship, professional lab, and seminar. The internship will acquaint the student with the specialized resources of various external organizations and will assist the student in understanding the nature of employment activities in offices/agencies that employ data scientists. The professional lab will provide the student with an opportunity to apply the skills and knowledge gained in the classroom to actual problems and tasks while still in an academic environment.
Prerequisite: One course in statistics.
Experimental design and statistical analysis of biological data; applications of computers to biological investigations. Designed for students in the initial stages of planning their research.
Prerequisite: ENT 621, or instructor permission
Cover broad guidelines and best practices regarding obtaining and treatment of data in materials science and device physics related directly to Clean Energy. Feature engineering, model training, validation, evaluation and comparison. Include interactive Jupyter notebooks with example Python code to demonstrate important concepts, workflows, and best practices in the field.
Prerequisites: ENT 331 Electric Circuits or equivalent, and ENT 671 Power Systems Analysis I or equivalent, or instructor’s permission.
This course introduces renewable and efficient electric power systems. It encourages self-teaching by providing numerous practical examples requiring quantitative analysis. Topics include historical, regulatory, and utility industry perspectives of the electric system as well as most of the electricity, thermodynamics, and engineering economics background needed to understand new power technologies.
Interdisciplinary Unit in Data Science & Analytics
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