New Data Science and Analytics MS
Internship in Genomics
We are excited to announce that starting fall 2021 the DSA program is offering a new Internship in Genomics. This pathway will allow students to gain a deep understanding of data science and genomics, preparing them to be successful in a career in academia, government, healthcare, or biotechnology.
Analyze and interrogate human genomic data.
Manage genomic data including organization, storage, cleaning, and manipulation using appropriate software and programming languages (Python, SQL, SAS, Excel, R, SPSS).
Weigh the social, ethical, and legal issues associated with genomic data science.
Visualize and effectively communicate results using dashboards, charts, graphs, written and oral reports.
We have partnered with Roswell Park Comprehensive Cancer Center to bring students hands-on experiential learning opportunities including the opportunity to complete an internship and real-world projects at Roswell Park.
As a part of this new unternship in genomics DSA program, in collaboration with Roswell Park, has submitted a NIH R25 grant, 'Exploring genomic data using Data Science and Analytics: A Buffalo State- Roswell Park Partnership' to support this addition to our program.
The PACM & DSA programs are excited to welcome the new Dean of the recently combined School of Arts and Sciences, Dean Brian Cronk.
DSA 621, Data Science Tools in Energy Engineering, was approved by Buffalo State. It will be a new elective suggested to students interested in clean energy analytics.
Each newsletter a faculty member shares a relevant article, dashboard, visualization or video. This newsletter's faculty pick is Saquib Ahmed, assistant professor in the Buffalo State Engineering Technology Department and DSA Clean Energy focus adjunct professor.
The article provides a wonderful guide for materials scientists interested in performing machine learning-centered research. First - to define materials science: it is a tremendously interdisciplinary field, concerned with the design, discovery, and innovation of materials for application in every arena and discipline under the sun (from biomedical devices to space applications, and everything in between; and in tackling each and every challenge facing humanity). The article provides broad guidelines and best practices regarding obtaining and treating data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model, architecture sharing, and finally publication. Data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing researchers from all levels to greatly benefit.
This is one of those rare publications which is hefty in impact while being easy to read and understand at the same time. With the defacto long reach of materials science, in the scientific community anyway, the impact of introducing the nexus of data analytics and machine learning into the mix has tremendous implications for research at all levels. This article is a must read for students, faculty, and researchers at large. It is important to note here that my group's first research work in machine learning (in collaboration with Joaquin Carbonara and Harry Efstathiadis from SUNY Poly) was just submitted in manuscript form last week to the journal 'SOLAR ENERGY'. Additionally, I have just submitted today an abstract of the same work for the MRS conference in the Fall (Boston). The article provided great context and overview for my research team in their overall efforts.
Industry Advisory Board member Karen Moronski-Chapman, CIO for Buffalo News, and Bill Chapman, Founder and Principal Consultant at 28 Protons LLC, chatting with DSA student Joe Skowronski and his wife during the Summer DSA picnic in Wilson, next to Ontario lake.
with Mary Odachowski (Gorman) '13
Mary is a Lead Quantitative Analyst at M&T bank where she guides the development and analysis of quantitative behavioral models used for credit risk and capital planning.
How did the PACM program contribute to your success?
Coming from a quantitative background, I already had fairly strong mathematical skills entering the program. The PACM program helped me learn to combine my technical skills with communication and leadership skills needed to succeed in a business setting.
What advice would you give current students?
Take advantage of ANY opportunity that pops up whether it be internships/electives/etc. I passed up a modeling elective that revolved around banking data because I thought "I will never go into banking" and now look where I am!
Read her complete profile on the PACM website here.
DSA and PACM alumni, if you are interested in sharing information for a profile please complete this survey.
The DSA Coding Challenge
The DSA Coding Challenge is released the third Thursday of each month. The first challenge was released on May 20th and closed June 3rd. We had 3 student successfully complete the first challenge, Patrick Doughtery, Joseph Fedyna, and Jillian Goodwyn. Congratulations!
The second challenge, created by DSA Alumni Mohammad Haque, was released June 17th and will close today July 1st. Submit your answers by midnight tonight!
Data science in the everyday
Visualizing Weather over Time
Check out these graphics from the New York Times showing changes in the normal temperature and precipitation from 1900 to today.
"'What we’re trying to do with climate normals is put today’s weather in the proper context,' said Michael Palecki."
Rembrandt’s “The Night Watch” was trimmed in the 1700's and the edges were lost. A museum’s senior scientist, Robert Erdmann, trained a computer using convolutional neural networks to recreate the missing pieces in Rembrandt's style based off of another artist's replica that contained the missing pieces.
The Robot Training Academy is a program that offers micro-credentials in Data Processing and Data Understanding. The programs are offered by rel8ed.to Analytics.
In these programs, participants will be trained in data processing, data science, and data understanding. Participants in the academy will also receive:
Certification in two areas: Data Processing Technology and/or Data Understanding.
Hands-on experience contributing to real world data projects (resume building work). Access to expert-led workshops.
The Academy runs from July 5th to August 13th.
If you are interested in participating in this opportunity email Joaquin Carbonara at email@example.com. Students may use this as the Professional Lab component of DSA 690.
In honor of Pride last month (June) we are sharing a collection of links containing, datasets, visualizations, and resources of LGBTQIA+ data.
GAYta Science - Use data science techniques to capture, combine, and extract insight of LGBTQ+ people to expanded outlooks, understanding, and support more inclusive policies.
US Census - Same sex couple data, visualizations and publications from the current population survey.
Destination Pride - Data-driven search platform that reimagines the Pride flag as a dynamic bar graph, then uses it to visualize the world's LGBTQ+ laws, rights and social sentiment.
LGBTdata.com - Collection of data sources including sexual orientation data with the goals of, 1) supporting the collection of sexual orientation data by survey administrators who have yet to collect this information, and 2) encouraging the analysis of these data sets.
Buffalo State Data Talk Summer
Mini-Series All About Internships
This Summer the Buffalo State Data Talk podcast is taking a break from it's regular episodes to bring you a mini-series all about internships. These episodes will guide listeners through everything you need to know about internships from why you might want to get one, to how to make the most out of the experience.
The topics include:
Episode 1 - Internship basics: Where do I start?
Episode 2 - Internship Networking and LinkedIn: Building digital relationships
Episode 3 - Applying for Internships: Resume, cover letters and interviews
Episode 4 - The Internship: You got one! Now what do you do?
Cloud computing is increasingly becoming ubiquitous and relevant to data science. We have organized a series of workshops in collaboration with Amazon Web Services (AWS) as an introduction to cloud computing with a specific focus on data lakes.