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$300 Mn Data Science Research Grant for Universities
Technology

$300 Mn Data Science Research Grant for Universities 

Data analytics is quickly becoming a vital part of every business’s functioning and growth model. With the ever-increasing use of the internet around the world, the amount of data getting generated per day is overwhelming and unprecedented. About 2.5 quintillion bytes of data are produced each day by the world, and it’s increasing on a per-day basis, as we speak.

Research in the data science domain has become of critical importance, globally, given the surging applicability of data analytics in trade and people’s lives. Even at Harvard, researchers are busy seeking a solution to stop the spread of COVID-19, deploying data science.

A Brief About WDSI Data Science Research Grant for Universities

World Data Science Initiative, or WDSI, is an initiative dedicated to promoting research work in the data science domain, by university and college students. The program aims to develop a talent force of 250,000+ professionals by 2022 who are trained in the advanced concepts of data science. Grants for universities worth $300 million are up for grabs under the said research initiative that covers educational institutes of five continents across the world. Financial help will be offered to select universities in setting up Centers of Data Science Excellence within their respective campuses under WDSI.

WDSI

Source: WDSI Official Website 

Under WDSI, $300 million in data science research grants will be offered to selected institutes to assist them in getting accredited, and their scholars certified on the world’s leading vendor-neutral data science standards. 

3 Data Science Research Areas That Demand Instant Address

Scientific Conception of Deep Learning Algorithms

No matter how successful and valuable the contributions of deep learning algorithms have been to the aid of mankind, we are yet to get familiar with the scientific understanding of the said technology. We need to learn the mathematical characteristics of the deep learning models.

Data science experts across the world still cannot provide reasoning for why a specific deep learning algorithm generates one result and not the other. We still cannot provide verification on the usage of new input data and the guarantee for the deep learning mechanisms to produce the expected results.  

An amalgamation of Heterogeneous & Diverse Data Sources

Data science is yet to become successful at combining multiple and diverse data sources while developing a predictive model to find solutions to a vital issue of societal importance. For instance, to forecast the efficiency of a particular cancer treatment method, we might develop a predictive model by studying the 2-D cell lines from mice, much expensive 3-D cell lines, and by dissecting the expensive DNA sequence of the cancer-affected cells taken out of a human body.

The most advanced data science deep-learning models are yet to get successful at combining multiple sources of data that are heterogenous, to develop and design a single, universal model.   

AI-Trustworthiness

We have been witnessing off late, a huge deployment of systems across a number of industries that use advanced-AI and machine learning concepts in their working. A few of such industries comprise healthcare, autonomous vehicles, law enforcement, public safety, criminal justice, housing, hiring, and hr management. All of the mentioned industries make use of AI in their decision-making on a daily basis and thereby continue to impact the lives of humans.

But what about the trustworthiness of disruptive technologies like ML & AI? Are people going to believe in the results produced by such technologies in the near future? There are concerns rising against AI-backed decision-making, and its relevance. Is there a 100% guarantee of each result produced by an AI-system, to be reliable, correct, fair, and safe?

Brief Conclusion: We hope, after having read the article, you have realized the importance of funding in the domain of data science research, especially for young university students who will be passed on the baton a few years from now.

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