In 2011, in the shadow of premature death from pancreatic cancer, Apple founder Steve Jobs had an epiphany. “I think the biggest innovations of the 21st century will be at the intersection of biology and technology. A new era is beginning.” (Isaacson, 2011) He was incredibly grateful that his illness sparked a passion for studying genomics by his college-aged son, Reed.
Today, humanity is confronted — and has been for at least ten months — with one of the most significant public health challenges in terms of speed and potential for morbidity and mortality since the Spanish Flu pandemic of 1917. But unlike 1917, science now has an exponentially more advanced understanding of biology, genomics, and disease dynamics. Since 2010 or so, we have a robust capability to use algorithms to discover and glean patterns and insights far beyond humans’ cognitive capacity and even farther beyond in speed. All of this begs the question — in the first global pandemic since the age of machine learning, how can it help us?
A quick search on Google Scholar identifies 19,700 papers in pre-prints or published in peer-reviewed journals in 2020 related to AI or machine learning and COVID-19. A meta-review published this month in Elsevier’s journal Chaos, Solitons, & Fractals found AI and ML applications for COVID-19 could be categorized as functions of screening, predicting, forecasting, contact tracing, and drug development (Lalmuanawma, 2020). The US National Institute of Health (NIH), PLOS, the family of Journal of Medical Internet Research (JMIR), and MedRxiv — a publication of BMC, Yale, and Cold Spring Harbor Laboratory — have been the leading publishers with most dissemination among the public and scientists arguably happening via Twitter.
While I’m biased, one excellent example is “A Machine Learning Explanation of the Pathogen-Immune Relationship of SARS-CoV-2 (COVID-19), and a Model to Predict Immunity and Therapeutic Opportunity: A Comparative Effectiveness Research Study,” published in JMIRx Med last week. In addition to its potential importance, it also exemplifies how data science can play in more accurately focusing, thereby increasing the efficacy of public health interventions and policies (Luellen, 2020).
#health #data-science #machine-learning #ai #covid19
With the world starting to open amidst the COVID-19 pandemic, the number of jobs available in data science sees an upward trend in India as we inch closer to providing vaccines for everyone.
The number of vacancies for data science jobs on the top job portals in India increased by 53% from when India eased the lockdown restrictions on June 8 to Nov 30, according to the data collated by AIMResearch. Although it is difficult to ascertain the exact number of open jobs, the top job portals in India, Naukri, LinkedIn, and MonsterIndia together showed almost 125,000 vacancies on Nov 30.
However, the pandemic did result in a decrease in the number of open data science jobs at the start as vacancies reduced from 101,562 from Dec 17 last year to 81,704 on June 8. Despite this decrease, India’s share of open data science jobs in the world increased from 7.2% in January to 9.8% in August.
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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.
IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.
With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.
Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.
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Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.
Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.
Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.
Some Of The Advantages Are Mentioned Below:-
Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.
Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.
Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.
Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.
Also Read: How Data Science Programs Become The Reason Of Your Success
Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-
Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.
Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.
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