Saama Technologies, a healthcare data analytics company, used Coursolve to set up a 12-week digital internship project analyzing public Twitter data sets. Over 300 professionals worldwide joined the project, facilitated by a project mentor from Saama. Through the project, Saama recruited two new hires, discovered new data sources, and gained new insights to showcase to clients. The project enabled Saama to tap into a global talent pool and strengthen its brand in an cost-effective manner.
RDAP 16: DMPs and Public Access: Agency and Data Service ExperiencesASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Outline for Panel 5, "DMPs and Public Access: Agency and Data Service Experiences"
Panel Lead:
Margaret Henderson, Virginia Commonwealth University
CIRPA 2016: It's Show Time: Are Your Data Ready to be the "Next Big Thing"?Stephen Childs
Growing interest in using “administrative data” for research, and government adoption of open data policies, are putting institutional data practices in the spotlight. Are your data ready for prime time? Do you have robust policies on sharing, access and archiving? Are your data well documented, with clear policies on governance? Will the data be re‐useable by others, to add to the body of knowledge in the area? This session will provide an overview of the principles and practices of data management, with a
case study that examines one institution’s experience in making its data available to the EPRI tax linkage project.
RDAP13 Mark Parsons: The Research Data Alliance: Making Data WorkASIS&T
Mark Parsons, Rensselaer Polytechnic Institute
Mark A. Parsons and Francine Berman: "The Research Data Alliance: Making Data Work"
Panel: Global scientific data infrastructure
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Outline of some of the opportunities and risks driving universities in the UK to develop services aiming to help researchers manage their data. Gives an overview of funder policies and service components, and suggests where these are heading in the short term.
This presentation was provided by Joe Zucca of the University of Pennsylvania, during Session Five of the NISO event "Assessment Practices and Metrics for the 21st Century," held on November 22, 2019.
RDAP 16: DMPs and Public Access: Agency and Data Service ExperiencesASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Outline for Panel 5, "DMPs and Public Access: Agency and Data Service Experiences"
Panel Lead:
Margaret Henderson, Virginia Commonwealth University
CIRPA 2016: It's Show Time: Are Your Data Ready to be the "Next Big Thing"?Stephen Childs
Growing interest in using “administrative data” for research, and government adoption of open data policies, are putting institutional data practices in the spotlight. Are your data ready for prime time? Do you have robust policies on sharing, access and archiving? Are your data well documented, with clear policies on governance? Will the data be re‐useable by others, to add to the body of knowledge in the area? This session will provide an overview of the principles and practices of data management, with a
case study that examines one institution’s experience in making its data available to the EPRI tax linkage project.
RDAP13 Mark Parsons: The Research Data Alliance: Making Data WorkASIS&T
Mark Parsons, Rensselaer Polytechnic Institute
Mark A. Parsons and Francine Berman: "The Research Data Alliance: Making Data Work"
Panel: Global scientific data infrastructure
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Outline of some of the opportunities and risks driving universities in the UK to develop services aiming to help researchers manage their data. Gives an overview of funder policies and service components, and suggests where these are heading in the short term.
This presentation was provided by Joe Zucca of the University of Pennsylvania, during Session Five of the NISO event "Assessment Practices and Metrics for the 21st Century," held on November 22, 2019.
From Data to Visualization: Emerging Tools for Research / Jan JohanssonPVC.ASIST
Traditionally, data analysis has required extensive expertise in math and statistics combined with training in complex software applications like SAS, Matlab, and the R programming language. Recently, however, new technologies and services have emerged that make it possible to work with raw data using web applications with simple visual interfaces. These visually compelling tools allow researchers to quickly see and communicate relationships between diverse trends, like the correlation between weather and burglary.This session will include a brief review of the visualizations built into traditional statistical software packages like R and Matlab followed by a demonstration of three new web-based tools applied to three real research topics.
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Jared Lyle, ICPSR
Jennifer Doty, Emory University
Joel Herndon, Duke University
Libbie Stephenson, University of California, Los Angeles
This presentation reviewed the challenges in identifying, acquiring and utilizing research data in relation to an evolving data market. Strategic solutions were examined in which the FAIR principles play a key role in the future of data management.
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
Supporting access: interventions that seek to improve the ways in which decision makers are able to access research based information.
Preseantation by Faye Reagon, HSRC (South Africa) at the Locating the Power of the In-between conference July 08
RDAP 15 EarthCollab: Connecting Scientific Information Sources using the Sema...ASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Erica M. Johns, Jon Corson-Rikert, Huda J. Khan, Dean B. Krafft and Matthew S. Mayernik
an introductory course for Librarians on using Big Data and Data Science applications on the field of Library Science. The course is a 2 hour course module for basic fundamentals of applying DS work.
From Data to Visualization: Emerging Tools for Research / Jan JohanssonPVC.ASIST
Traditionally, data analysis has required extensive expertise in math and statistics combined with training in complex software applications like SAS, Matlab, and the R programming language. Recently, however, new technologies and services have emerged that make it possible to work with raw data using web applications with simple visual interfaces. These visually compelling tools allow researchers to quickly see and communicate relationships between diverse trends, like the correlation between weather and burglary.This session will include a brief review of the visualizations built into traditional statistical software packages like R and Matlab followed by a demonstration of three new web-based tools applied to three real research topics.
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Jared Lyle, ICPSR
Jennifer Doty, Emory University
Joel Herndon, Duke University
Libbie Stephenson, University of California, Los Angeles
This presentation reviewed the challenges in identifying, acquiring and utilizing research data in relation to an evolving data market. Strategic solutions were examined in which the FAIR principles play a key role in the future of data management.
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
Supporting access: interventions that seek to improve the ways in which decision makers are able to access research based information.
Preseantation by Faye Reagon, HSRC (South Africa) at the Locating the Power of the In-between conference July 08
RDAP 15 EarthCollab: Connecting Scientific Information Sources using the Sema...ASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Erica M. Johns, Jon Corson-Rikert, Huda J. Khan, Dean B. Krafft and Matthew S. Mayernik
an introductory course for Librarians on using Big Data and Data Science applications on the field of Library Science. The course is a 2 hour course module for basic fundamentals of applying DS work.
Linked Data Love: research representation, discovery, and assessment
#ALAAC15
The explosion of linked data platforms and data stores over the last five years has been profound – both in terms of quantity of data as well as its potential impact. Research information systems such as VIVO (www.vivoweb.org) play a significant role in enabling this work. VIVO is an open source, Semantic Web-based application that provides an integrated, searchable view of the scholarly activities of an organization. The uniform semantic structure of VIVO-ISF data enables a new class of tools to advance science. This presentation will provide a brief introduction and update to VIVO and present ways that this semantically-rich data can enable visualizations, reporting and assessment, next-generation collaboration and team building, and enhanced multi-site search. Libraries are uniquely positioned to facilitate the open representation of research information and its subsequent use to spur collaboration, discovery, and assessment. The talk will conclude with a description of ways librarians are engaged in this work – including visioning, metadata and ontology creation, policy creation, data curation and management, technical, and engagement activities.
Kristi Holmes, PhD
Director, Galter Health Sciences Library
Director of Evaluation, NUCATS
Associate Professor, Preventive Medicine-Health and Biomedical Informatics
Northwestern University Feinberg School of Medicine
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality Paul Courtney
Presentation made by Paul Courtney (Dana-Farber Cancer Institute, Boston, MA and OHSL, MD) and Anil Srivastava (OHSL) at the 2013 VIVO conference in St. Louis, MO. Material contributed by Rubayi Srivastava (OHSL), Swati Mehta (Centre for Development of Advanced Computing, India), Juliusz Pukacki (Poznan Supercomputing and Network Center, Poland) and Devdatt Dubhashi (Chalmers Institute of Technology, Sweden).
Transforming Research in Collaboration with Funding AgenciesAmazon Web Services
Funding agencies constitute one of the essential pillars for research and have been the backbone for innovation. Data-driven collaborative research is an integral part of many domains. In this session, leaders from the world's largest biomedical and science research agencies, the National Institutes of Health (NIH) and the National Science Foundation (NSF) discuss their programs, including NIH Data Commons and Harnessing the Data Revolution (HDR). The goal of the NIH Data Commons is to accelerate new biomedical discoveries by providing a cloud-based platform where investigators can store, share, access, and compute on digital objects generated from biomedical research. HDR is one of the 10 "Big Ideas" for future investment from the NSF for fundamental data science research. These collaborative initiatives will enable researchers to accelerate science and engineering through improved access to data, tooling, analytic resources in the cloud. These programs will revolutionize the way scientific data and resources are utilized by the research communities.
How DeepSphere.AI Transformed Fresh Graduates Into Data Scientists At Databri...HemaMaliniP5
DeepSphere.AI transformed students who could now become Data Scientists (At Databricks).
Click here ➡️ https://lnkd.in/gzfwdMev For extensive details about this benchmark study.
LET'S START WITH A PROVEN AND VERIFIABLE ACCOMPLISHMENT:
BACKGROUND:
To date, we have not seen a benchmark study on the effectiveness of a data science program.
In this article, we provide enough details based on our teaching and some of the large-scale data science industry projects we are working on with Google and the AWS team. Many data science programs are offered in the market, from big brand names to small educational institutions. Still, we are unsure which one to enroll in and which may be the best choice to achieve my goals and objectives.
WHAT DO WE SEE IN THE MARKET:
We have several conceptual data science programs taught by academically well-qualified professionals without industry implementation exposure. We may also see another extreme, a data science program filled with python programs and taught by technical experts.
We need a balanced curriculum where the learner can learn both concepts and technology, which should be guided by someone who has successfully implemented one or two data science projects for real industry clients at a production scale. The modern data science curriculum should teach beyond use case development and Python syntax.
A BENCHMARK STUDY FOR HIGHLY PRODUCTIVE DATA SCIENCE LEARNING:
We conducted this benchmark study with 500+ students studying data science in the bachelor's program and 243+ teachers teaching data science.
500+ DATA SCIENTISTS: Our six semesters bachelor's in data science program is offered at SRM university. The study focused on transforming students into employable data scientists with industry skills. Around 500+ students are studying across all campuses and semesters.
243+ DATA SCIENCE TEACHERS: HODs, professors, assistant professors, research scholars, and management staff from 110 universities and colleges provided feedback both in quantitative and qualitative formats. Approximately 243 teachers participated in this benchmark and shared their views on our data science program advancement. Here is the participant's profile.
PhDs: 63
Professors: 09
Assistant Professors: 55
Associate Professors: 34
Research Scholars: 11
WHAT DO YOU NEED TO LOOK FOR IN AN EFFECTIVE AND PRODUCTIVE DATA SCIENCE LEARNING PROGRAM:
An effective data science program should balance some or all of the following, an Interdisciplinary learning approach, industrial curriculum, data science advancement, and enabling technologies such as Databricks, Google Cloud, AWS, SAP (Business Process), and other relevant technologies.
I am sharing our benchmarked and proven interdisciplinary data science curriculum, which is currently taught to 500+ data scientists and transforms learners into employable resources at the end.
Future And Scope of Data Science Online Program.pptxlearn bay
Welcome to the presentation on the future and scope of data science online programs.
In this session, we will explore the growing significance of online data science programs and the opportunities they offer.
Similar to Saama Technologies and Coursolve - Case Study (20)
Future And Scope of Data Science Online Program.pptx
Saama Technologies and Coursolve - Case Study
1. Case Study:
Saama Technologies
Finding world class
talent on Coursolve
Background
Saama Technologies is a 700-strong data science
and analytics services company headquartered in
San Jose, USA, dedicated to improving efficiency
in the healthcare industry amongst others. Through
Coursolve, Saama offered a digital internship – a
short, online work-based learning project – that asked
participants to mine Twitter streams to uncover new
ways of improving healthcare delivery. The project
enabled them to explore new data channels and use
open-source communities to gather expert research.
They also had the opportunity to identify and hire
talented professionals.
“Coursolve enabled us to tap into
the insights of hundreds of data
scientists worldwide. Not only did
we grow our vibrant open-source
community, but we also exposed
our brand to emergent talent in the
field, resulting in several new hires
and stronger relationships with our
clients. There’s really nothing out
there like Coursolve.”
– Pratik Mehta,
Project Administrator and Senior Data
Analyst at Saama Technologies
2. 12-week project set up to analyze
public data sets on Twitter
Advertised to Coursolve members
taking an online Intro to Data Science
course
Facilitated by one Project Mentor,
Pratik Mehta, Senior Data Analyst
Project work included weekly
mentoring and coordinating by
Project Mentor
•
•
•
•
The Project
Project Output Examples:
Over 300 professionals worldwide
joined the project
Two hires recruited
New sources of data captured
Saama able to showcase cutting-
edge research to clients
•
•
•
•
The Results
To post your own project, go to
www.coursolve.org/organizations
To find out more about working with
Coursolve, email our team at
projects@coursolve.org
