Category Archives: Data Science
Thousands of new cosmic explosions discovered by Young … – Pennsylvania State University
It is the largest multi-band data release of nearby supernovae ever slightly fewer than 2,000 objects and is the first to use photometric classification and photometric redshifts the increase in wavelength that astronomers observe when objects in space are moving away from us extensively. This is critical for the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time, a planned 10-year survey of the southern sky, for where there are too many objects to get spectra, said the researchers.
Much of the time-domain universe is uncharted, said Gautham Narayan, deputy director of CAPS and assistant professor at UIUC. We still do not know the progenitor systems of many of the most common classes of transients, such as type Ia supernovae, while still using these sources to try and understand the expansion history of our universe. Weve also seen one electromagnetic counterpart to a binary neutron star merger. There are many kinds of transients that are theoretically predicted, but have never been seen at all.
Narayan stated that With high-redshift experiments such as the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope about to begin operations, we saw an opportunity to establish our Young Supernova Experiment to be a low-redshift anchor. We can probe time-scales that these newer experiments cannot and find lots of transients in the nearby universe to compare to their samples in the distant universe. In particular, with this data release, we made extensive use of AI and machine learning techniques to classify the data release sample techniques that will be crucial for Rubin and Roman.
This groundbreaking effort could not have succeeded without the collective partnership between the University of Hawaii, UCSC, DARK, NCSA, UIUC, and Penn State. Using Hawaiis Pan-STARRS1 telescope to collect the images, DARKs enhanced processing of the data on its computing cluster, UCSCs organization of the survey and data hosting, and NCSA and UIUCs analysis it is an outstanding achievement for multi-institution research.
This survey is a discovery portal, said Ryan Foley, assistant professor of astronomy and astrophysics at UCSC, who led the organization of the YSE survey project. We are finding thousands of interesting objects, which we can then follow and study with additional observations to understand what were seeing.
Mark Huber, a senior researcher at University of Hawaiis Institute for Astronomy, and deeply involved in the YSE project said, "Pan-STARRS produces a steady stream of transient discoveries, observing large areas of the sky every clear night with two telescopes. With over a decade of observations, Pan-STARRS operates one of the best calibrated systems in astronomy with a detailed reference image of the static sky visible from Haleakal. This enables rapid discovery and follow up of supernovae and other transient events well suited for programs like YSE to build up the sample required for analysis and this significant data release."
Director of Pan-STARRS Observatories Ken Chambers added this collaboration with the Young Supernova Experiment makes exceptional use of Pan-STARRS ability to routinely survey the sky for transient phenomena and moving objects. We have provided an unprecedented sample of young supernovae discovered before their peak luminosity that will be an important resource for supernova researchers and cosmologists for many years. Looking ahead, Pan-STARRS will remain a crucial resource in the Northern Hemisphere to complement the Rubin Observatory in the Southern Hemisphere.
In addition to Villar, the research team at Penn State includes postdoctoral scholar Conor Ransome and graduate students Kaylee De Soto and S. Karthik Yadavalli.
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Thousands of new cosmic explosions discovered by Young ... - Pennsylvania State University
Where the sidewalk ends – EurekAlert
Its easier than ever to view maps of any place youd like to go by car, that is. By foot is another matter. Most cities and towns in the U.S. do not have sidewalk maps, and pedestrians are usually left to fend for themselves: Can you walk from your hotel to the restaurants on the other side of the highway? Is there a shortcut from downtown to the sports arena? And how do you get to that bus stop, anyway?
Now MIT researchers, along with colleagues from multiple other universities, have developed an open-source tool that uses aerial imagery and image-recognition to create complete maps of sidewalks and crosswalks. The tool can help planners, policymakers, and urbanists who want to expand pedestrian infrastructure.
In the urban planning and urban policy fields, this is a huge gap, says Andres Sevtsuk, an associate professor at MIT and a co-author of a new paper detailing the tools capabilities. Most U.S. city governments know very little about their sidewalk networks. There is no data on it. The private sector hasnt taken on the task of mapping it. It seemed like a really important technology to develop, especially in an open-source way that can be used by other places.
The tool, called TILE2NET, has been developed using a few U.S. areas as initial sources of data, but it can be refined and adapted for use anywhere.
We thought we needed a method that can be scalable and used in different cities, says Maryam Hosseini, a postdoc in MITs City Form Lab in the Department of Urban Studies and Planning (DUSP), whose research has focused extensively on the development of the tool.
The paper, Mapping the Walk: A Scalable Computer Vision Approach for Generating Sidewalk Network Datasets from Aerial Imagery, appears online in the journal Computers, Environment and Urban Systems. The authors are Hosseini; Sevtsuk, who is the Charles and Ann Spaulding Career Development Associate Professor of Urban Science and Planning in DUSP and head of MITs City Form Lab; Fabio Miranda, an assistant professor of computer science at the University of Illinois at Chicago; Roberto M. Cesar, a professor of computer science at the University of Sao Paulo; and Claudio T. Silva, Institute Professor of Computer Science and Engineering at New York University (NYU) Tandon School of Engineering, and professor of data science at the NYU Center for Data Science.
Significant research for the project was conducted at NYU when Hosseini was a student there, working with Silva as a co-advisor.
There are multiple ways to attempt to map sidewalks and other pedestrian pathways in cities and towns. Planners could make maps manually, which is accurate but time-consuming; or they could use roads and make assumptions about the extent of sidewalks, which would reduce accuracy; or they could try tracking pedestrians, which probably would be limited in showing the full reach of walking networks.
Instead, the research team used computerized image-recognition techniques to build a tool that will visually recognize sidewalks, crosswalks, and footpaths. To do that, the researchers first used 20,000 aerial images from Boston, Cambridge, New York City, and Washington places where comprehensive pedestrian maps already existed. By training the image-recognition model on such clearly defined objects and using portions of those cities as a starting point, they were able to see how well TILE2NET would work elsewhere in those cities.
Ultimately the tool worked well, recognizing 90 percent or more of all sidewalks and crosswalks in Boston and Cambridge, for instance. Having been trained visually on those cities, the tool can be applied to other metro areas; people elsewhere can now plug their aerial imagery into TILE2NET as well.
We wanted to make it easier for cities in different parts of the world to do such a thing without needing to do the heavy lifting of training [the tool], says Hosseini. Collaboratively we will make it better and better, hopefully, as we go along.
The need for such a tool is vast, emphasizes Sevtsuk, whose research centers on pedestrian and nonmotorized movement in cities, and who has developed multiple kinds of pedestrian-mapping tools in his career. Most cities have wildly incomplete networks of sidewalks and paths for pedestrians, he notes. And yet it is hard to expand those networks efficiently without mapping them.
Imagine that we had the same gaps in car networks that pedestrians have in their networks, Sevtsuk says. You would drive to an intersection and then the road just ends. Or you cant take a right turn since there is no road. Thats what [pedestrians] are constantly up against, and we dont realize how important continuity is for [pedestrian] networks.
In the still larger picture, Sevtsuk observes, the continuation of climate change means that cities will have to expand their infrastructure for pedestrians and cyclists, among other measures; transportation remains a huge source of carbon dioxide emissions.
When cities talk about cutting carbon emissions, theres no other way to make a big dent than to address transportation, Sevtsuk says. The whole world of urban data for public transit and pedestrians and bicycles is really far behind [vehicle data] in quality. Analyzing how cities can be operational without a car requires this kind of data.
On the bright side, Sevtsuk suggests, adding pedestrian and bike infrastructure is being done more aggressively than in many decades in the past. In the 20th century, it was the other way around, we would take away sidewalks to make space for vehicular roads. Were now seeing the opposite trend. To make best use of pedestrian infrastructure, its important that cities have the network data about it. Now you can truly tell how somebody can get to a bus stop.
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Written by Peter Dizikes, MIT News Office
Additional background
Paper: Mapping the Walk: A Scalable Computer Vision Approach for Generating Sidewalk Network Datasets from Aerial Imagery
https://www.sciencedirect.com/science/article/pii/S0198971523000133
Computers Environment and Urban Systems
Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery
22-Feb-2023
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.
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The Japanese venture capital star bagging 35% returns mining … – The Japan Times
Japan isnt known for its startup culture. Tomotaka Goji, a bureaucrat-turned-technology guru, is working hard to change that.
The 50-year-old runs a low-profile venture fund in Tokyo that has quietly built a track record that would make Silicon Valleys finest envious. Hes done it by blending his experiences at Stanford University and his connection to the prestigious University of Tokyo.
His firm, University of Tokyo Edge Capital Partners, concentrates on turning academic research into commercial businesses. With a doctorate in data science, Goji uses big data and artificial intelligence to uncover promising research results in fields like material sciences and chemistry. The result is 35% yearly returns at one of his funds since its founding in 2018, the best in Japan for any fund of more than 10 billion ($75 million) according to one survey.
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The Japanese venture capital star bagging 35% returns mining ... - The Japan Times
Utica University Data-Based Research Uncovers New … – PR Newswire
FinThrive Provides Novel Data for Study Sponsored by the Robert Wood Johnson Foundation
ALPHARETTA, Ga., March 15, 2023 /PRNewswire/ -- Utica University research examining communities in New York state has uncovered new socioeconomic insights that can be correlated to clinical outcomes. The findings can be leveraged by providers across healthcare to make formal recommendations to economic and data science teams, in an effort to create a more equitable healthcare experience.
The Robert Wood Johnson Foundation's Health Data for Action (HD4A) program funded the research, with spatial analysis provided by FinThrive, Inc., a healthcare revenue management software-as-a-service (SaaS) provider. The study used FinThrive data to evaluate the demographics and family structures of New York households and identify specific neighborhoods that could benefit from more informed healthcare interventions. The insights enable researchers to observe specific factors including household arrangements that would impact a patient's ability to prioritize their health.
The findings demonstrate that demographics such as race and ethnicity, specifically Black and Hispanic/Latino Americans, as well as single-parent households have a direct relation to generational poverty. Generational poverty occurs when two or more generations living in a home or community have not advanced socioeconomic status, which affects all areas of life: financial, social, physical and emotional. Unlike traditional research using census or claims data, this project uses FinThrive socioeconomic data that provides a three-dimensional view, at both the individual and household level, helping to inform intervention programs to positively impact outcomes and reduce unnecessary healthcare spending. The study also found that government stimulus alone will not be enough to aid individuals who suffer from generational poverty to eventually achieve upward economic mobility.
Potential recommendations include stratifying the health and social services needs of their population with considerations for race, ethnicity and family structure. In addition, community programs should target "cold spots," defined as difficult-to-reach areas that treat populations generationally, within a given geography.
Other valuable use cases for these findings include:
Upstate Family Health Center in Utica, NY is an example of a provider that is benefiting from such data and research. "During the pandemic, we evolved as a community health center," said John Milligan, Chief Executive Officer of UFHC. "We saw a lot of behavioral and substance abuse issues, and it became clear that we needed specialized teams to address those areas." Using data and screenings as a guide, Milligan and his staff were able to implement patient-centric programs such as food drives to ensure that socioeconomic factors correlated to care delivery were addressed. "You can do all you want on the clinical side, but until you find the root of the problem you aren't going to get anywhere."
"Spearheading this research was a true honor," said Michael McCarthy, PhD, Assistant Professor of Data Science at Utica University. "I am so proud of the amazing team whose dedication, focus and unwavering humanistic approach allowed us to gain insight into the significant role generational poverty plays in health equity and the need to prioritize a patient's economic status in a meaningful way within healthcare."
"Census and claims data can be helpful in certain instances, butthe value is undermined by out-of-date, incomplete or biased perspectives of a person's life," said John Yount, Chief Innovation Officer at FinThrive. "The FinThrive data shows all the activity happening outside of the healthcare setting. Layer that into a clinical workflow or apply to a housing/food distribution program for a diabetic Medicaid population, for example, and you can create opportunities to improve overall experience and reduce total medical costs. Our goal is to uncover insights that have a positive impact on US Healthcare, helping to support and drive health equity."
Continuing to utilize this data, the Utica University research team is expanding their analysis to include North Carolina, Arizona, California, Virginia and Texas. Results from these studies are forthcoming in 2023. FinThrive and Utica University will continue to publish findings as they become available.
About FinThriveFinThrive provides one of healthcare's most comprehensive revenue cycle management SaaS platforms, offering patient access, charge integrity, claims management, contract management, machine learning & robotic process automation, data & analytics, and education software solutions to 3,200+ healthcare providers. FinThrive's end-to-end software platform helps healthcare organizations increase revenue, reduce costs, expand cash collections, and ensure regulatory compliance across the entire revenue cycle continuum. For more information on the FinThrive story, visit http://www.FinThrive.com
About Upstate Family Health CenterUpstate Family Health Center, Inc. is a 501(c)(3), not for profit, federally qualified health center, offering family health care services to individuals of all ages at various locations including Utica and Rome throughout the Mohawk Valley. The experienced and dedicated staff provide the highest level of care, while ensuring that the patient's needs come first. Upstate Family Health Center is building bridges to better healthcare.
About the Robert Wood Johnson FoundationThe Robert Wood Johnson Foundation is the nation's largest philanthropy dedicated solely to health. Since their founding in 1972, RWJF has worked to improve health and healthcare in the United States. RWJF supports efforts to build a national Culture of Health rooted in equity that provides every individual with a fair and just opportunity for health and well-being, no matter who they are, where they live, or how much money they have. They do this by supporting research, programs, policies and practices aimed at bringing about meaningful change and improving the lives of everyone in our nation now and for generations to come. To learn more about the Robert Wood Johnson Foundation visit https://www.rwjf.org.
About Utica UniversityUtica University is a private university in Utica, New York founded in 1946. They offer numerous accredited and recognized programs including their Master of Social Work program, and their master's degree program in nursing(Family Nurse Practitioner [FNP], Leadership and Education), and post-graduate APRN FNP certificate programsare accredited by the Commission on Collegiate Nursing. To learn more about Utica University, visit https://www.utica.edu.
Media Contact:Audra MurphyVP, Strategic Communications, FinThrive(717) 476-4864[emailprotected]
SOURCE FinThrive
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Utica University Data-Based Research Uncovers New ... - PR Newswire
The business value of curated model collections – MIT Sloan News
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Its common practice for museums and galleries to have curators who are responsible for acquiring, caring for, and developing collections so they remain key assets. Engineering organizations can take a page from the art world and embrace curation concepts to better manage their own models.
Companies building complex systems such as naval battleships, jetliners, and cars are switching from traditional, paper-based processes to digital and model-centric engineering workflows as part of digital transformation. Product-related models, simulations, and data are used to digitally represent design concepts, mimic system behavior, and test and optimize product performance under varying conditions.
While model-based practices are now a staple in product design and engineering workflows, models are seldom managed as an enterprise collection, limiting their value, according to Donna Rhodes, a principal research scientist at the MIT Sociotechnical Systems Research Center. By establishing new curator roles and curation workflows, companies can maximize the utility of models as a true enterprise asset, enabling greater reuse, improving design efficiencies, and seeding more innovation.
Were talking about management control and preservation as you would in an art museum, but also about the active enhancement of models in the collection, Rhodes said during a recent MIT SDM Systems Thinking Webinar Series event. Models are becoming so valuable to enterprises that ultimately they may have greater value than the physical assets themselves that is, if they can be repurposed.
Not every model belongs in a curated collection. For example, the myriad models used in early-stage product development arent necessarily applicable elsewhere. However, mature models that function as a digital twin a digital representation of a physical asset or system are valuable additions to any curated repository, Rhodes said. In addition, models that can be easily repurposed for functions beyond their initial intended use have a place in a collection.
A curated model collection requires some foundational building blocks. Governance is key and should include policies that specify access controls and permissions, and a board or committee that makes key decisions. A governance board specifies what models are allowed to come into a collection (a process known as accession), what models need to be removed (known as deaccession), the valuation of key assets, and the strategic road map for evolving the curated model collection. Establishing the proper cybersecurity controls is another requisite, as is adopting technologies that enhance usability in areas like search and model discovery.
While curated-model use cases are still evolving, one example with broad applicability is what Rhodes referred to as a digital demonstrator a capability that could allow a company to demonstrate an offering to potential customers as part of a competitive bid process. Imagine reaching into a model collection, pulling out some models, and putting them together in such a way that you could actually demonstrate what a solution looked like, Rhodes said.
As organizations advance on this journey, several factors are central to establishing a collection of curated models and ensuring that it delivers desired benefits, Rhodes said.
Issues to consider include the following:
Establishing trust in the models. For models to be useful, potential consumers must trust that they are credible, especially when theyve been developed by someone else. Model verification and validation practices can help build that trust. Its also important to create mechanisms that provide visibility into model origins, establish transparency into how a model was created and how it might have been repurposed, and context for related decisions.
Consumers that have more experience and expertise working with models are more likely to view them as credible, as are those who have a higher propensity for trust in general, Rhodes said. Model credibility is also influenced by an individuals trust in the model developer and how easily models can be discovered and retrieved from a repository. Well increasingly find ways that we can curate models for specific consumer needs, and the ability to do so will be very important in whether this idea of a shared model repository would be useful to an enterprise, she said.
Addressing governance challenges. Creating strong governance standards is one way to help establish trust in curated model collections. Organizations should establish a board of standing members, domain experts among them, that is tasked with making decisions about what models come into a collection as well as what models are removed. Rhodes cited work supported by the U.S. Department of Defense Systems Engineering Research Center to help establish criteria to determine whether a model is a candidate for curation. Pertinent attributes include relevance to the enterprise, completeness of the metadata and documentation accompanying a model, completeness of the model pedigree, the potential for reusability, the uniqueness of the model, and the economic business case.
Governance efforts also need to recognize that people wont want to give up their own models and often prefer to use them on an individual or localized program level. As a result, companies may want to consider a federated approach, which keeps some models local as opposed to centralizing them in one enterprise repository. Think of it as a system of systems of models in repositories, Rhodes said. There are many things we are thinking about in terms of the composition of governance.
Considering technology as an enabler. The entire concept of a curated collection hinges on users ability to discover and access models. Emerging technologies in areas like data science, visual analytics, and machine learning can help consumers discover pertinent models and aid in the curation and reuse processes, Rhodes said. For example, augmented intelligence could provide context for how a model could be repurposed for an entirely different context, facilitating human decision-making, she explained.
We know if were going to achieve this idea of enterprise model collections, the value of these collections needs to outweigh the investment it takes to create and maintain them, Rhodes said. But I think they can be very powerful, especially when we get into this world where every system has a digital twin.
Watch the webinar Investigating the Future of Curated Model Collections
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The business value of curated model collections - MIT Sloan News
What is Data Science? How to be a Data Scientist …
The data scientist builds and trains prescriptive or descriptive models, then tests and evaluates the model to make sure it answers the question or addresses the business problem. At its simplest, a model is a piece of code that takes an input and produces output. Creating a machine learning model involves selecting an algorithm, providing it with data, and tuning hyperparameters. Hyperparameters are adjustable parameters that let data scientists control the model training process. For example, with neural networks, the data scientist decides the number of hidden layers and the number of nodes in each layer. Hyperparameter tuning, also called hyperparameter optimization, is the process of finding the configuration of hyperparameters that result in the best performance.
A common question is "Which machine learning algorithm should I use?" A machine learning algorithm turns a dataset into a model. The algorithm the data scientist selects depends primarily on two different aspects of the data science scenario:
To help answer these questions, Azure Machine Learning provides a comprehensive portfolio of algorithms, such as Multiclass Decision Forest, Recommendation systems, Neural Network Regression, Multiclass Neural Network, and K-Means Clustering. Each algorithm is designed to address a different type of machine learning problem. In addition, The Azure Machine Learning Algorithm Cheat Sheet helps data scientists choose the right algorithm to answer the business question.
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What is Data Science? – Data Science Explained – AWS
Data science is an all-encompassing term for other data-related roles and fields. Lets look at some of them here:
While the terms may be used interchangeably, data analytics is a subset of data science. Data science is an umbrella term for all aspects of data processingfrom the collection to modeling to insights. On the other hand, data analytics is mainly concerned with statistics, mathematics, and statistical analysis. It focuses on only data analysis, while data science is related to the bigger picture around organizational data.In most workplaces, data scientists and data analysts work together towards common business goals. A data analyst may spend more time on routine analysis, providing regular reports. A data scientist may design the way data is stored, manipulated, and analyzed. Simply put, a data analyst makes sense out of existing data, whereas a data scientist creates new methods and tools to process data for use by analysts.
While there is an overlap between data science and business analytics, the key difference is the use of technology in each field. Data scientists work more closely with data technology than business analysts.Business analysts bridge the gap between business and IT. They define business cases, collect information from stakeholders, or validate solutions. Data scientists, on the other hand, use technology to work with business data. They may write programs, apply machine learning techniques to create models, and develop new algorithms. Data scientists not only understand the problem but can also build a tool that provides solutions to the problem.Its not unusual to find business analysts and data scientists working on the same team. Business analysts take the output from data scientists and use it to tell a story that the broader business can understand.
Data engineers build and maintain the systems that allow data scientists to access and interpret data. They work more closely with underlying technology than a data scientist. The role generally involves creating data models, building data pipelines, and overseeing extract, transform, load (ETL). Depending on organization setup and size, the data engineer may also manage related infrastructure like big-data storage, streaming, and processing platforms like Amazon S3.Data scientists use the data that data engineers have processed to build and train predictive models. Data scientists may then hand over the results to the analysts for further decision making.
learning?Machine learning is the science of training machines to analyze and learn from data the way humans do. It is one of the methods used in data science projects to gain automated insights from data. Machine learning engineers specialize in computing, algorithms, and coding skills specific to machine learning methods. Data scientists might use machine learning methods as a tool or work closely with other machine learning engineers to process data.
Statistics is a mathematically-based field that seeks to collect and interpret quantitative data. In contrast, data science is a multidisciplinary field that uses scientific methods, processes, and systems to extract knowledge from data in various forms. Data scientists use methods from many disciplines, including statistics. However, the fields differ in their processes and the problems they study.
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Computational Data Science MS | Temple University
Extract knowledge from big data with the Master of Science in Computational Data Science in Temples College of Science and Technology. You can enroll in the 30-credit-hour degree program as a part- or full-time student. Once you graduate, youll be prepared to fill the growing demand for data scientists in a range of industries from education to government. The program will also ready you for further graduate studies, research positions or teaching careers.
As a student, youll dig deep into computational analytics and explore techniques and theories steeped in computer science, mathematics and statistics. Throughout the program, youll learn how to analyze large quantities of data and discover the knowledge necessary to fuel cutting-edge developments. Data science is used to make complex decisions in a wide range of data-rich domains, such as biomedical science, defense and security, education, engineering, geoscience, physical science, and social science.
Youll gain a strong foundation in algorithmic, computational and statistical thinking as well as the inner-workings of computer systems. Toward the end of the program, you will have the opportunity to pursue topics related to your academic and professional interests with the three-credit Masters Project or the more advanced six-credit Masters Thesis.
The Computational Data Science MS program is a 30-credit degree culminating in the Masters Project. Required courses you will take during your studies include
Learn more about Computational Data Science MS courses.
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Data Science with Concentration in Computational Analytics, B.S.
Learn more about the Bachelor of Science in Data Science.
Data Science is an interdisciplinary field of study about methods and systems to extract knowledge or insights from large quantities of data coming in various forms. Temple's B.S. in Data Science is designed for students interested in developing expertise in data science. The Computational Analytics concentration provides a strong background in mathematics, algorithmic and computational thinking, computer systems, and data analysis, and will enable students to analyze large quantities of data to discover new knowledge and facilitate decision making.
Undergraduate Contact Information:
Dr. Jamie Payton, ChairScience Education and Research Center, Room 304215-204-8450
Dr. Gene Kwatny, Vice ChairScience Education and Research Center, Room 304215-204-8450
Dr. Anthony Hughes, Faculty AdvisorScience Education and Research Center, Room 344215-204-7910anthony.hughes@temple.edu
Students must complete all University requirements including those listed below.
All Temple students must take a minimum of two writing-intensive courses at Temple as part of their major.The specific writing-intensive course options for this major are:
45 Upper Level (2000+) credits within the College of Science & Technology (CST), the College of Liberal Arts (CLA), or the College of Engineering (ENG).
90 credits within the College of Science & Technology (CST), the College of Liberal Arts (CLA), or the College of Engineering (ENG).
Courses listed under the major requirements for the degree will be included in the calculation of the major GPA. Courses that could not apply toward the major as an elective or a required course are not counted in the calculation of the major GPA.
To graduate with Distinction in Major, students are required to have a 3.50 or higher grade point average (GPA) both in the major and overall, as well as be recommended by the department of Computer & Information Sciences.
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Data Science with Concentration in Computational Analytics, B.S.
Data Science Institute | Fox School of Business
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Role: Analyst Revenue Strategy & OperationsComputeach LLCCozen OConnorCTS Consulting GroupCustomers Bank Data Science InstituteDeans OfficeDecision NeuroscienceDeloitte Consulting LLPDenovo HealthDevelopmentDevelopment and Alumni AffairsDigital Innovation in Marketing, MSDirector, Duke Cancer Institute BiostatisticsDMA CommunicationsDrucker & ScaccettiElectronic InkEmersonEpiph PartnersERTExecutive MBA ProgramsExecutive Office of the PresidentEYFacebookFenway Management Advisors (FMA)Finance FinanceFinancial Analysis and Quantitative Risk ManagementFinancial Analysis MSFinancial Process AdvisorsFMC CorporationFord Motor Company, Ford China Fordham UniversityFounding Principal and CEO, Vox Medica, Inc.Fox Information TechnologyFox Management Consulting GroupFriedman LLPFull-Time Global MBAFull-Time MBAGraduate AdmissionsGraduate and International ProgramsGreenLight Fund PhiladelphiaHealth KinectHealth Sector ManagementHoly Redeemer Health SystemHomeward Residential CapitalHuman Resource ManagementHuman Resource Management & Organizational BehaviorHuman Resource Management and Organizational BehaviorI DO Wedding ConsultingIBM Global MarketsIndigo Strategic Marketing Group, LLCInformation Solutions, MercerInformation SystemsInformation Systems and Global Solutions, Lockheed MartinInformation Technology Auditing Innovation & Entrepreneurship InstituteInnovative Management & Entrepreneurship MSInstitute for Business and Information TechnologyInterdisciplinary StudiesInternational BusinessInternational Business, Strategic ManagementIT AuditIT Auditing and Cyber SecurityIT OperationsJL HR Solutions, LLCJohnson & Johnson Medical DevicesJordan Brand Advisory BoardKDB Insights LLCKorman CommunitiesKPMG Foundation & the PhD ProjectKPMG U.S.Lancaster General Health/Penn MedicineLegal StudiesLegal Studies in BusinessLevLane AdvertisingLexus of Chester Springs, Wilkie LexusLincoln Financial GroupLiquid HubLiquidHubLockheed Martin CorporationLyondellBasell IndustriesMAC-MOD AnalyticalManagementManagement ConsultingManagement Information SystemManagement Information SystemsManagement Information Systems MajorManager of Business Development Dallas WingsMarketingMarketing & CommunicationsMarketing & Supply Chain ManagementMarketing and CommunicationsMarketing and Supply ChainMarketing and Supply Chain ManagementMarketing ManagementMarketing, Operations and Supply Chain ManagementMarsh USA Inc.Masters of Science in Digital Innovation in MarketingMBA Human Resources concentrationMcCallister ConsultingMcCormick TaylorMcKinsey & CoMHBiglan Consulting LLCMid-Atlantic Region, Arthur J. Gallagher & CompanyMorgan PropertiesMS / Technology & Innovation ManagementMS Actuarial ScienceMS Financial AnalysisNixon PeabodyNorth Star Resource GroupNorthern Arizona UniversityOffice of Development and Alumni RelationsOffice of ResearchOffice of Research and Doctoral ProgramsOffice of the DeanOMBAOmnichannel Marketing Solutions, IQVIAOnline and Digital LearningOnline and Digital Learning Operations and Supply Chain Management Operations and Supply Chain ManagementOperations and Supply Chain Management, Strategic ManagementOperations Management/MKTGOpportunity Finance NetworkOxford CommunicationsParagraphPenn MedicinePeople Supply Chain, Capgemini Invent NAPeopleSharePhilabundancePhiladelphia International MedicinePhiladelphia Office, PwCPhiladelphia, EisnerAmperPolicy MapPolicy, Organizational and Leadership StudiesProduct Management, ElemicaPwCRBx Capital, LP, FSD Pharma, Inc., Parkway Clinical LaboratoriesReinvestment FundResearch AdministrationRisk Actuarial Science and Legal StudiesRisk Management & InsuranceRisk Management & Insurance MajorRisk Management and InsuranceRisk, Actuarial Science, and Legal StudiesRisk, Insurance and Healthcare ManagementRisk, Insurance, and Healthcare ManagementRodan + FieldsRothman Orthopedic InstituteRSM US LLPSAP SESBDCSchool of Sport, Tourism and Hospitality ManagementSenior Consultant, Temple University SBDCSenior Vice President, Harte-Hanks, Sidney E. Gable Associates, Inc. Small Business Development Center (SBDC)Social Security AdministrationSpecialized Masters in Business Analytics Sport and Recreation ManagementSport Industry Research CenterStatistical ScienceStatisticsStatistics, Operations and Data ScienceStatistics, Operations, and Data Sciencestrat360 Strategic Management Strategic ManagementStrategic Management (Entrepreneurship and Innovation Management)Student Financial ServicesSunoco Logistics PartnersSusquehanna Private Capital, LLCTangerine StrategiesTemple SBDCTemple UniversityTemple University, Institute on DisabilitiesThe American University of RomeThe Corelink SolutionThe Graham CompanyThe Kessler GroupThe Swarthmore GroupThink CompanyThomas Jefferson University and Jefferson HealthTL VenturesTourism and SportTourism & SportTourism and Hospitality ManagementTourism and Sport Tourism and SportTranslational Research CenterUndergraduate AdvisingUndergraduate and Honors ProgramsUndergraduate Enrollment ManagementUndergraduate ProgramsUniversity of DaytonUniversity of Massachusetts LowellUS-Asia Center for Tourism and Hospitality ResearchVerizonVictrix Global VSPWalmart Inc.Women in FinanceWyethYour Outsourced CFOZoomi, Inc. and Myota, Inc.
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