Data is probably the most omnipresent virtual quantity, of the 21 st century, with astronomically high quantity of data being generated, every minute, almost as a score card of all the events, and non-events occurring around.
But this data is useless unless its vast resource can be tapped for further use. That is easier said than even attempted. When it comes to large quantities of data, traditional database extraction tools provide very limited access to this formidable quantity of Big data.
To make the best of the seemingly untappable resource, a new field of data extraction, visualization, management and manipulation has come about — Data Analytics or Data Science For the interested, here are some advanced insights on data science and related fields of Artificial Intelligence Vs Machine Learning Vs Data Science Vs Deep learning.
People who indulge in this data mining and analysis are called Data Scientists. And the 20, data scientists worldwide are adept in Mathematics, Statistics, and Computer Programming. Over time, and with the foreseeable increase in Data Analysis experts in the future, Data Science is emerging as a formal degree. Canada, being one of the top countries for higher education, has now begun their bid to get into the data race.
In this article, we will list some of the known universities, across all the Canadian provinces, that offer a formal training in handling big data. The list is in no particular ranking order.
Postdoc Fellowship in Canada 2020 – IVADO Postdoctoral Scholarship
It can be referred to as the first step towards looking for degree programs for the data chaser. For a more exhaustive picture, you would be well advised to take the research into the pages of individual universities that you might be interested in.
Before you sign up for an expensive, full-time masters program, check out these free online courses from top universities.Data Analytics career scope in Canada - Story of Sohaib Arshad
They have the option of getting a paid certificate if you want to show it off on your resume. Learn more.
MS in Information Studies. MS in Computer Science. Courses listed here. Specialization in Data Science by participating Masters Programs.
Background in Computer Science, Engineering or related degree. Formal training in programming languages, data structures, operating systems, algorithms, computer architecture, calculus, linear algebra, probability and statistics. Bachelors in Statistics, Mathematics or related degree. Training in calculus, linear algebra and computer science. Bachelors in Computing Science or equivalent. Complete a technical interview with the Program Director. Bachelors in engineering, science, business and economics.
Prerequisite courses in Statistics, data structures and algorithms, databases and R software packages. Background in Mathematics and Computer Science, introductory Statistics, calculus, linear algebra and computer programming. Course listed here. If you are interested, you will need to invest the requisite time to gather your intel.
Postdoctoral Fellowship in Data Science
Given the trend, and the massive awareness for the need of data specialists, the inclusion of this new field is simply inevitable. Free online courses Before you sign up for an expensive, full-time masters program, check out these free online courses from top universities. Information Security and Data Management.The normal duration of the Fellowship is two years.
Fellows will receive a generous salary as well as an annual allocation for research and travel expenses. We are looking for researchers whose interests are in data science, broadly construed, and including researchers with a methodological and applications focus, including primarily methodological researchers and those who advance both.
Fellows will be provided with the opportunity to pursue their research agenda in an intellectually vibrant environment with ample mentorship. We are looking for independent researchers who will seek out collaborations with other fellows and with Harvard faculty.
The Harvard Data Science Initiative Postdoctoral Fellows Program will support outstanding researchers whose interests relate to the following themes:. To give some purely illustrative examples, these fields include health sciences e. This list is by no means exhaustive. Successful applicants will be expected to lead their own research agenda, but also work collaboratively with others including with members of the Harvard faculty, and to contribute to building the data science intellectual community.
The Fellows program will offer numerous opportunities to engage with the broader data science community, including through seminar series, informal lunches, mentoring opportunities, opportunities for fellow-led programming, and other networking events. Fellows should expect to spend most of their time in residence at Harvard. Appointments may be extended for a third year, budget and performance allowing. A cover letter that identifies up to five and at least two Harvard faculty members with whom the applicant would like to work.
Please do not contact faculty directly prior to submission of application. The statement should explain the importance and potential impact of this research. Names and contact information for at least two and up to five references the application is complete only when all letters of reference have been submitted. Referees will be provided with a link to the submission portal. All materials should be submitted as PDF documents.
Subscribe to KDnuggets News. Tag: Postdoc Help develop new e-science methods that fundamentally integrates Deep Learning and Multivariate analysis. The postdoc position is full-time for a period of two years. Nofima is searching for a postdoctoral candidate experienced in image analysis and machine learning for a project to improve sustainability and minimize waste in the seafood industry.
Seeking candidates to develop and apply information retrieval, information extraction, and various Natural Language Processing NLP techniques to the scientific literature in materials science and crystallography for the purpose of building prototype computational data systems. Candidates are expected to have broad knowledge in data science.
University of San Francisco invites applications for a postdoctoral position starting in August The successful candidate will be working with selecting sensors as well as integrating and processing data from these. We are inviting outstanding postdoctoral academics to join our world-class team to deliver high-quality research that will help shape the future of AI for conversational assistants, human-robot interaction, customer service, and other domains.
Develop data analysis methods with focus of data federation and privacy preservation. Seeking recently graduated doctoral students years from completing their Ph. Wil van der Aalst, is a new research group at RWTH focusing on the interplay between processes and data. Seeking a Postdoctoral Associate in the areas of machine learning and bioinformatics. The major research focus is on developing machine learning and mathematics methodologies for analyzing massive genomic data.
Are you interested in using your natural language processing skills to make a social impact? Want to work with the White House and a team from government, academia, and industry to change how job training programs are created all over the US? Read a first-hand perspective on Big Data playing field in Singapore, strong support for Machine Learning and Data Science research, excellent local conditions, and how all these contribute to a bigger aspiration this city state is striving towards.
Developing machine learning and mathematics methodologies to address the computational challenges of analyzing massive genomic data for various biomedical applications. Conduct research in computational biology, genomics and transcriptomics; work together with Trait Informatics scientists and developers, as well as scientists from across research departments.This unique one-year full-time or two-year part-time Master of Science MSc degree program enables students to develop interdisciplinary skills, and gain a deep understanding of technical and applied knowledge in data science and analytics.
Graduates are highly trained, qualified data scientists who can go on to pursue careers in industry, government or research. The MSc in Data Science and Analytics is delivered in both lecture-based and hands-on lab learning environments where students can develop and apply their skills to complex, real-world datasets and data science and analytics problems.
I invite you to join us and discover the fascinating world of data science and analytics in the heart of downtown Toronto by interacting with leading academics and professionals.
Data science is an interdisciplinary field that combines expertise from several domains. Data scientist is the unicorn that needs to possess diverse skills such as statistics, math, machine learning, operations research, data visualization, communication and domain expertise. The program engages industry partners to access extensive data in health care, software engineering, social media, services and finance.
Faculty members involved in the program possess the diverse skill sets needed for this emerging discipline. The program provides advanced training in data science and analytics principles and methodologies, and highlights their applications in solving problems in various industry, government and research domains.
Technical skills such as mathematics, statistics, operations research, and programming are core competencies that are in the greatest demand in the marketplace. Search Site and People.
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Phd in Data Science – Guide to Choosing a Doctorate Program
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For more information, see the Indeed Terms of Service. View all Huron Digital Pathology jobs - St. Jacobs jobs Learn more about working at Huron Digital Pathology. PhD in computer science, physics, or mathematics or equivalent training. Our postdoctoral fellowship program is a 1-year appointment and may lead to full-time….
University of British Columbia 4. BC Cancer Agency 4. Suitable candidates should possess a PhD degree in bioinformatics, computer science, or molecular biology and must be competent in at least one programming…. Graduate degree preferably a PhD in electrical engineering, physics, or computer science with strong research background in high performance computing and…. View all Anyon Systems Inc. Western University 4. Basic computer skills in Microsoft Office Suite. Experience working with particle accelerators, analyzing experimental data from accelerators, as well as computer modelling of electromagnetic devices for….
View all Canadian Light Source Inc.Professional opportunities in data science are growing incredibly fast. But it also means that there are a lot more options out there to investigate and understand before developing the best career path.
This guide is intended for prospective students who are considering pursuing a data science PhD. The guide will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts like course load and tuition costs that are part of the data science PhD decision-making process.
Can I get an Online Ph. D in Data Science? Overview of Ph. School Listings. Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain.
Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.
However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand. While it is not common to get a data science Ph. Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.
A PhD requires a lot of academic work, which generally requires between four and five years sometimes longer to complete. Here are some of the high level factors to consider and evaluate when comparing data science graduate programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.
Core curriculums will vary from program to program, but almost all will have a core foundation of statistics. All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale.
They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise. One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation.
These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science.
Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.
Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field.
Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.
Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.Skip to main content Skip to main navigation.
Data Science Institute. In his dissertation, he studied the exploratory search paradigm for time series data using interactive visual interfaces.
His primary research includes the characterization, design, and evaluation of visual-interactive interfaces to combine the strengths of both humans and algorithms in interactive machine learning and data science applications. His problem-driven research focus includes applications like Earth observation, digital libraries, human motion analysis, service and energy network monitoring, political decision-making, music classification, sports data analysis, finance and stock market analysis, as well as medical and patient-related research in particular.
His work involves measuring and analyzing user signals namely physiological signals for user modeling and advanced human-computer interaction paradigms. His research is now centered in the study of human eye movements applied to the health care domain. In his dissertation, he studied implicit interaction paradigms using brain, cardiovascular, and other physiological signals. Cory Bonn Department: Psychology Project: Quantifying individual differences from complex datasets in developmental psychology Bio: Dr.
His research aims to better understand how humans of all ages make inferences about quantities they observe, with a particular focus on developing models of our sense of approximate number.
During his time at the DSI, he will develop a set of tools for understanding multivariate measures of brain activity and behavior from challenging subjects such as infants, toddlers, and preschool children, who typically give data in much smaller quantities than adults and tend to have much higher rates of missing or censored data.
His main research focuses on interrogating whole genome sequence data to better understand the spread of infectious disease.
He works mainly with bacterial pathogens, notably Mycobacterium tuberculosis, combining genetic and epidemiological data to model outbreaks and reconstruct transmission networks to predict the drivers of transmission through the application of statistical and computational models, including machine-learning methods. Chendi Wang Department: Medical Genetics Project: Using machine learning models for understanding the role of the non-coding genome in brain development and autism Bio: Dr.
Chendi Wang joined the Mostafavi lab as a postdoctoral fellow in Feb For her PhD Dr. Wang developed machine learning and statistical methods for analysis of multimodal brain imaging data including structural, functional, and diffusion MRI data. She was a research software engineer in industry developing machine learning and deep learning methods for computer vision applications before she joined the Mostafavi lab.
Her current research interest is developing statistical and machine learning methods for understand biological and molecular basis of brain development. Kieran Campbell Department: Statistics Project: Large-scale Bayesian modelling of drug resistance and evolution in human cancers at single-cell resolution Bio: Dr.
His research centres around Bayesian statistical modelling of molecular cancer data with a particular focus on understanding why certain cancer cells evade chemotherapy and cause relapse. In his work he focused on designing user models and personalized support in several interactive computer systems, including intelligent tutoring systems and visualization-based interfaces.
His research interests also include user-adapted interaction, intelligent agents, affective computing, and eye-tracking data processing. His current research at UBC is about examining ways to deliver adaptive or personalized interaction in MOOCs Massive Open Online Coursesin order to improve the learners' achievements and engagement. Pauls Hospital and UBC. Her PhD dissertation examined various ways to advance previous methods for the registration of image volumes and sequences using graphical models and discrete optimization.
Her research interests include computer vision, machine learning, medical image analyses, and deep learning strategies. She is currently exploring the the use of various deep learning architectures for the staging and prognosis of chronic obstructive pulmonary disease using lung computed tomographic imaging data.
Zahra Jalali Department: Medical Genetics Project: Modeling multiple types of "omics" data to understand the biology of human exposure to pollution and allergens Bio: Dr. The focus of her doctorate research was on the computational identification and characterization of iron regulatory-related proteins in Glossina morsitans.
She further continued her academic career as a postdoctoral fellow at the same institute, focusing on the comparative genomics analysis of mycobacterium tuberculosis in identifying putative drug resistance-associated markers. She has recently joined UBC as a postdoctoral fellow to work on the development and implementation of efficient statistical models to integrate multiple omics data types with the aim of identify novel genes and pathways associated with the pathophysiology of respiratory health and lung disease.
Her research interests include natural language processing, computational linguistics, discourse analysis, and text mining in various domains. Specifically, her current research is about applying NLP and text mining to the healthcare domain in order to help chronic disease management by processing patient-generated language. Her PhD dissertation focused on computationally modeling metaphor in order to capture how metaphor is used and identify a broader spectrum of predictors from the discourse context that contribute towards its detection.