The future of Data Science is Growing with the Advancement of AI and Machine Learning.

Data science in simple words means the study of data. It entails developing methods of recording, storing, and analyzing data to successfully bring out useful information. Data Science put together and make use of several statistical procedures. The procedures cover data modeling, data transformations, machine learning, statistical operations including descriptive and inferential statistics. For all data scientists statistics is the primary asset.

With the biggest innovation of the time, that is a cryptocurrency, the demands for controlling data online have become a crucial challenge. Various techniques are put forward by Data Science to identify a group of people and providing them with the best possible security from fraud activities.

However, the application of data science is not just concerned with one field rather its application disseminated across various sectors.

Major Future Developments in Data Science-

Healthcare sector- The biggest application of Data Science is in healthcare. The accessibility of large datasets of patients can be used to build a Data Science approach to identify the diseases at a very early stage. Healthcare is one of the biggest sectors for providing opportunities for the professional who can use their medical expertise with Data Science and provide immediate help to the suffering patients.

Arms and Weapons- Data Science can help in building various automated solutions to identify any attack at a very early stage. Other than that Data Science can help in constructing automated weapons that will be smart enough to identify when to fire and when not to.

Banking and Finance- Data Science in the Banking and Finance sector can be used in managing the money effectively to invest in the right places based on Data Science predictions for best results.

Other than the above sectors Data Science is also applied in Automobile Industry like self-driving cars, Fixed destination cabs as well as in Power and Energy. Data Science can predict the maximum safest potential and can help in building AI bots that can easily handle enormous power sources.

The implementation of Data Science cannot be ignored as it is already in action in the present stage. When you look for something in Myntra or Flipkart and then you get similar recommendations or similar advertisements for whatever you have searched on the internet is all about Data Science. The whole world is operated by Data Science. For every single search in Google, the process of data science is activated.

The future of data science is growing. According to Cloud Vendor Domo even when a person accounts for the Earth’s entire population, the average person is expected to generate 1.7 megabytes of data per second by the end of 2020.

An overreaching motif today and moving ahead, big data is assured to play an authoritative role in the future. Data will stipulate modern health care, finance, business management, marketing, government, energy, and manufacturing. The scale of big data is truly staggering as it has already entwined itself in the fundamental aspect of business as well as personal life.

The Dominance of AI and Machine Learning in near Future of Data Science-

Like almost all businesses prime concern is tech, there is a high possibility of the growth of data science jobs.

Artificial Intelligence is the most impactful technology among others that data scientists will run up into. Today Ai is already refining the business operations and assures to be a major trend in the near future. The applications of AI in today’s world have driven the adoption of other AI applications such as machine learning, deep learning and this will lead the way as the future of data science. Machine learning is the aptitude of statistical models to develop the capabilities and improve the performance with time in the absence of programmed instructions. This principle can be seen in the chess machine that is developed by Google’s DeepMind unit – the AlphaZero. The AlphaZero improves on its other computerized chess-playing peers in the absence of instructions is an example of how it learns from its movements to reach the most desired outcome.

As a greater number of businesses are merging with AI and data-based technologies at a high rate there is a need for a greater number of data scientists to help guide the initiatives.

Data science is a leviathan pool of multiple data operations that include statistics and machine learning. Machine Learning algorithms are very much dependent on data. Therefore, machine learning is the primary contributor to the future of data science. In particular data science covers the areas like Data Integration, Distributed Architecture, Automating Machine learning, Data Visualisation, Dashboards and BI, Data Engineering, Deployment in production mode, Automated, data-driven decisions.

While IT-focused jobs have been all the rage over the last two decades the rate of growth in the sector has been projected to be about 13% by the Bureau of Labor Statistics. It is still higher than the average rate of growth for all other sectors. However, data science has seen an explosive growth of over 650% since 2012 based on an analysis done on LinkedIn. The role of a Data Scientist has projected forward to one of the most in-demand jobs and ranks second to machine learning engineer- which is a job that is adjacent to a data scientist.

In the upcoming time, Data Scientists will have the ability to take on areas that are business-critical as well as several complex challenges. This will facilitate the businesses to make exponential leaps in the future. Companies in the present are facing a huge shortage of data scientists. However, this is set to change in the future.

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Algorithmia, a provider of enterprise machine learning operations (MLOps) software, has been selected by Raytheon Intelligence & Space, a Raytheon Technologies (NYSE: RTX) business to support the team’s development of the U.S. Army’s Tactical Intelligence Targeting Access Node (TITAN) program. TITAN is a tactical ground station that finds and tracks threats to support long-range precision targeting.

Algorithmia, along with other leaders in artificial intelligence and machine learning, will enable Raytheon Technologies TITAN team to deliver easily digestible data to Army operators. TITAN will ingest data from space and high-altitude, aerial and terrestrial sensors to provide targetable data to defense systems. It also provides multi-source intelligence support to targeting, and situational awareness and understanding for commanders.

Algorithmia’s MLOps platform has been used by over 130,000 data scientists in a wide range of organizations. Its customers include large and midsize enterprises, Fortune 500 companies, the United Nations and multiple government intelligence agencies. The company’s momentum is a product of growing interest in AI-based applications and the need organizations have to efficiently manage cost and security for machine learning models.

“Machine learning significantly accelerates the process by which organizations can uncover important data points and respond to critical issues,” said Diego Oppenheimer, CEO of Algorithmia. “Our platform streamlines the deployment of machine learning models into production while providing important oversight, including review for ethical standards, to ensure models operate when and how they should, which makes Algorithmia a natural fit for sensitive applications. We are excited to join Raytheon in supporting its work with the U.S. Army.”

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Small-molecule therapeutics treat a wide variety of diseases, but their effectiveness is often diminished because of their pharmacokinetics—what the body does to a drug. After administration, the body dictates how much of the drug is absorbed, which organs the drug enters, and how quickly the body metabolizes and excretes the drug again.

Nanoparticles, usually made out of lipids, polymers, or both, can improve the pharmacokinetics, but they can be complex to produce and often carry very little of the drug.

Some combinations of small-molecule cancer drugs and two small-molecule dyes have been shown to self-assemble into nanoparticles with extremely high payloads of drugs, but it is difficult to predict which small-molecule partners will form nanoparticles among the millions of possible pairings.

MIT researchers have developed a screening platform that combines machine learning with high-throughput experimentation to identify self-assembling nanoparticles quickly. In a study published in Nature Nanotechnology, researchers screened 2.1 million pairings of small-molecule drugs and “inactive” drug ingredients, identifying 100 new nanoparticles with potential applications that include the treatment of cancer, asthma, malaria, and viral and fungal infections.

“We have previously described some of the negative and positive effects that inactive ingredients can have on drugs, and here, through a concerted collaboration across our laboratories and core facilities, describe an approach focusing on the potential positive effects these can have on nanoformulation,” says Giovanni Traverso, the Karl Van Tassel (1925) Career Development Professor of Mechanical Engineering, and senior corresponding author of the study.

Their findings point to a strategy for that solves for both the complexity of producing nanoparticles and the difficulty of loading large amounts of drugs onto them.

“So many drugs out there don’t live up to their full potential because of insufficient targeting, low bioavailability, or rapid drug metabolism,” says Daniel Reker, lead author of the study and a former postdoc in the laboratory of Robert Langer. “By working at the interface of data science, machine learning, and drug delivery, our hope is to rapidly expand our tool set for making sure a drug gets to the place it needs to be and can actually treat and help a human being.”

Langer, the David H. Koch Institute Professor at MIT and a member of the Koch Institute for Integrative Cancer Research, is also a senior author of the paper.

A cancer therapy meets its match

In order to develop a machine learning algorithm capable of identifying self-assembling nanoparticles, researchers first needed to build a dataset on which the algorithm could train. They selected 16 self-aggregating small-molecule drugs with a variety of chemical structures and therapeutic applications and a diverse set of 90 widely available compounds, including ingredients that are already added to drugs to make them taste better, last longer, or make them more stable. Because both the drugs and the inactive ingredients are already FDA-approved, the resulting nanoparticles are likely to be safer and move through the FDA approval process more quickly.

The team then tested every combination of small-molecule drug and inactive ingredient, enabled by the Swanson Biotechnology Center, a suite of core facilities providing advanced technical services within the Koch Institute. After mixing pairings and loading 384 samples at a time onto nanowell plates using robotics in the High Throughput Sciences core, researchers walked the plates, often with quickly degrading samples, next door to the Peterson (1957) Nanotechnology Materials Core Facility core to measure the size of particles with high throughput dynamic light scattering.

Now trained on 1,440 data points (with 94 nanoparticles already identified), the machine learning platform could be turned on a much bigger library of compounds. Screening 788 small-molecule drugs against more than 2,600 inactive drug ingredients, the platform identified 38,464 potential self-assembling nanoparticles from 2.1 million possible combinations.

The researchers selected six nanoparticles for further validation, including one composed of sorafenib, a treatment commonly used for advanced liver and other cancers, and glycyrrhizin, a compound frequently used as both a food and drug additive and most commonly known as licorice flavoring. Although sorafenib is the standard of care for advanced liver cancer, its effectiveness is limited.

In human liver cancer cell cultures, the sorafenib-glycyrrhizin nanoparticles worked twice as well as sorafenib by itself because more of the drug could enter the cells. Working with the Preclinical Modeling, Imaging and Testing facility at the Koch Institute, researchers treated mouse models of liver cancer to compare the effects of sorafenib-glycyrrhizin nanoparticles versus either compound by itself. They found that the nanoparticle significantly reduced levels of a marker associated with liver cancer progression compared to mice given sorafenib alone, and lived longer than mice given sorafenib or glycyrrhizin alone. The sorafenib-glycyrrhizin nanoparticle also showed improved targeting to the liver when compared to oral delivery of sorafenib, the current standard in the clinic, or when injecting sorafenib after it has been combined with cremophor, a commonly-used drug vehicle that improves water solubility but has toxic side effects.

Personalized drug delivery

The new platform may have useful applications beyond optimizing the efficiency of active drugs: it could be used to customize inactive compounds to suit the needs of individual patients. In earlier work, members of the team found that inactive ingredients could provoke adverse allergic reactions in some patients. Now, with the expanded machine learning toolbox, more options could be generated to provide alternatives for these patients.

“We have an opportunity to think about matching the delivery system to the patient,” explains Reker, now an assistant professor of biomedical engineering at Duke University. “We can account for things like drug absorption, genetics, even allergies to reduce side effects upon delivery. Whatever the mutation or medical condition, the right drug is only the right drug if it actually works for the patient.”

The tools for safe, efficacious drug delivery exist, but putting all the ingredients together can be a slow process. The combination of machine learning, rapid screening, and the ability to predict interactions among different combinations of materials will accelerate the design of drugs and the nanoparticles used to deliver them throughout the body.

In ongoing work, the team is looking not just to improve effective delivery of drugs but also for opportunities to create medications for people for whom standard formulations are not a good option, using big data to solve problems in small populations by looking at genetic history, allergies, and food reactions.

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Artificial intelligence (AI) is poised to change lives. From fueling medical discoveries to smart collars that can decipher and display the emotions of our household pets, this emerging technology is enabling organisations to innovate. According to IDC[1], by 2025, AI-powered enterprises will see a 100% increase in productivity and new product introduction success rates, higher than those of their non-intelligent peers. By being able to anticipate the market and operational changes with AI, organisations will respond much faster than their competitors. They will be agile enough to adapt to changes in the market and innovate.

Hence, accelerating AI solutions for businesses should be the focus for organizations in India. For example, HPC workloads are becoming more data centric and adding AI technologies to advance the capabilities of traditional HPC modeling and simulation. In the next few years, HPC technologies, such as HPC-enabled machine learning training, will go from experimentation models to production models. As CTOs and CIOs in India look to create an enterprise infrastructure that provides robust performance and scalability for large and highly complex AI models while keeping deployment costs low, the answer may lie in HPC for three key reasons:

· Data analytics: Businesses relying heavily on data analytics generate new insights which improve efficiencies and give them a competitive edge. However, when it comes to analysing large sets of unstructured data that are exponentially increasing in volume and velocity, traditional IT infrastructure is often hamstrung due to slow storage speeds. To adapt to significantly larger data sets and compute-intensive analytics processes, researchers are looking to exascale computing systems, capable of performing one quintillion calculations per second. Powered with HPC, these advanced performance systems are expected to have a profound impact in the faster identification of pandemics, discovery of effective medication and indication of hazardous weather conditions before they happen.

· Acceleration of AI deployments: According to a study, in India, almost 70% of organisations are currently investing in Artificial Intelligence to deliver better business results. As HPC and AI continue to converge and evolve to involve more use cases across industries, the possibilities are nearly endless. Organisations are increasingly looking for pre-designed and pre-validated solutions to generate value instead of constructing IT infrastructure. To help companies with roadblocks involved in setting up an AI system, large vendors are also beginning to introduce reference architectures together with HPC infrastructure for data scientists and researchers working. With HPC and software combined, launching new AI applications is becoming easier and faster. Technology vendors are helping data scientists focus less on maintaining AI systems but more on experimenting, exploring and uncovering insights. Organizations do not need to walk their AI journey alone. By collaborating and working with end-to-end technology infrastructure providers, organisations can co-design and customise unique HPC infrastructures to meet AI research, development and AI model deployment needs.

· Growing support for HPC globally and in India: The business benefits of AI are being increasingly recognised, with infrastructure providers now helping the research community and customers capitalise on HPC, expanding it from a niche market to a broader audience. More than ever, customers and partners have access to guidance from technology vendors on how to kick-start their AI initiatives including design, installation, maintenance and most importantly, delivering tangible business outcomes for their organisations. Organisations are focused on promoting open collaboration, bringing together the wide-ranging experience and knowledge of technology developers, service providers and end-users in a worldwide forum that promotes the advancement of innovative, powerful HPC and AI solutions.

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The world is moving at a brisk pace. Several technologies have become obsolete, while the technologies that looked impossible a few years back are at our fingertips now. Hence, it has become pertinent for young professionals to learn the skills that drive future technologies. Data Science is one such promising field. In recent years, millions of professionals have secured their future by choosing Data Science as a career. You can enter this career by completing a degree or a course in Data Science. Finding these courses has become a cakewalk these days. You can easily find a reliable Data Science course in Hyderabad or other top tech cities to boost your career growth. But before we move ahead, let us look at this field in more detail. 

Who is a Data Scientist?

The world economy is leaning more towards analytics, with companies collecting data for many years. According to LinkedIn, there is an enormous demand for professionals who can mine and interpret data. These are the Data Scientists.

Data scientists are a mix of mathematicians, trend-spotters, and computer scientists. The data scientist’s role is to decode large volumes of data and analyze it further to find trends in the data and gain an insight into what the data suggests. Data scientists work between the business and the IT worlds. They drive markets by analyzing complex datasets to reveal insights that companies can leverage into actions.

Skills Required For Data Scientist

If you want to have a career in data science, you will need hard skills like analysis, machine learning, statistics, Hadoop, etc. Excelling in this role requires excelling at critical thinking, persuasive communication, and having good listening and problem-solving skills. 

Opportunities are plenty in this field. So, once you have the skill-sets and qualifications, the jobs are waiting for you—now and in the future.

Job Roles for Data Science Experts

Most companies are embracing data analysis to skyrocket their growth. Data Scientists are in demand not just in the tech world but also in all other major sectors, including FMCG, logistics, and more. The big-five tech giants: Google, Amazon, Apple, Microsoft, and Facebook, employ half of the world’s total data scientists. 

Here, we discuss the lucrative Data Science careers for the aspirants:

Data Scientists:

A Data Scientist explores and scrutinizes various data patterns to estimate the impact on an organization. A Data Scientist’s role is to explain the importance of data in a simple method so that others can understand it. They require statistical knowledge of different programming languages for solving complex problems. 

Data Analyst:

A Data Analyst’s role is to analyze the data to figure out a market trend. They provide a clear picture of the company’s standing in the market. The company sets the desired goal, and then the Data Analyst provides datasets to achieve the required target.

The Data Analyst’s role changes according to the company’s needs. For example, the marketing department may need their services to understand consumer behavior and responses against various marketing strategies. 

Data Engineer:

Data Engineers work with the organization’s core and are the company’s backbone. They are the builders, designers, and managers of large databases. Their task is to build data pipelines, correct data flow, and ensure the data reaches the relevant departments.

A Data Engineer has to collaborate with other data experts to communicate the results with his colleagues. In a nutshell, a data engineer shares his insights with the company through data visualization and helps the organization grow. 

Business Intelligence Analyst:

A business intelligence analyst analyzes the collected data to maximize the company’s efficiency. They have a more technical role than analytical and require knowledge of popular machines. They are the bridge between business and IT. 

Marketing Analyst:

A marketing analyst assists companies in their marketing division. They analyze and suggest which products to produce and which products to discontinue. They monitor customer satisfaction reports to help the company in improving existing products and services.

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Python, the general-purpose coding language has gained much popularity over the years. Speaking of web development, app designing, scientific computing or machine learning, Python has it all. Due to this favourability of Python in the market, python developers are also in high demand. They are required to be competent and out of the box thinkers- undoubtedly a race to win.

Are you one of those python developers? Do you find yourself lagging behind in proving your reliability? Maybe you are going wrong with some of your skills. Never mind!

I’m here to tell you of those 8 trendsetting skills you need to hone. Implement them and prove your expertise in the programming world. Come, let’s take a look!

1.   Knowing Python Libraries

Being able to use the Python Library in its full potential also decides your expertise with this programming language. Python libraries like Panda, Matplotlib, Requests, Pyglet and more consist of reusable codes that you’d wish to add to your programs. These libraries are boon to you as a developer. They will increase workflow and make task execution way easier. Nothing saves more time from having to write the whole code every time.

2.  Knowing Python Frameworks

You might know how Python omits repeated code by using pre-developed frameworks. As a developer using a Python framework, you typically write code which conforms to some kind of conventions. Because of which it becomes easy to delegate responsibilities for the communications, infrastructure and low-level stuff to the framework. You can, therefore, concentrate on the logic of the application in your own code. If you have a good knack of these Python frameworks it can be a blessing, as it allows smooth flow of development. You may not know them all, but it’s advisable to keep up with some popular ones like Flask, Django and CherryPy.

Not sure of Python frameworks? You can seek help from Python Training Courses.

3.  Object Relational Mappers

Object-relational mapping (ORM) is a programming method used to access a database. It exposes your database into a series of objects without writing commands to insert or retrieve data. It may sound complex, but can save you a lot of time, and help to control access to your database. ORM tools can also be customised by a Python developer.

4.  Knowledge of Front End Technologies

Front end technologies like the HTML5, CSS3, and JavaScript will help you collaborate and work with a team of designers, marketers and other developers. Again, this can save a lot of development time.

5.  Analytical Skills

A good Python developer should have sharp analytical skills. You are expected to observe and critically come up with complex ideas, solutions or decisions about coding. Talking of the analytical skills in Python you need to have:

• A  complete idea of algorithms to write accurate codes.
• Visualize datasets, design attractive web sites for web development.
• Optimize algorithms

Analytical skills are a mark of your additional knowledge in the field. Building your analytical skills also make you a better problem solver.

6.  Data Science Foundation

Python Developers have a bright future in Data Science. Companies on the run will prefer developers with Data Science knowledge to create innovative tech solutions. Knowing Python will also gain your knowledge of probability, statistics, data wrangling and SQL. All of these are significant aspects of Data Science.

7.  Machine Learning and AI skill

Python is the right choice to grow in the Artificial Intelligence and Machine learning domain. It is an intuitive and minimalistic language with a full-featured library line (also called frameworks) which considerably reduces the time required to get your first results.

However, to master artificial intelligenceand  machine learning with Python you need to have a strong command over Python syntax. A fair grounding with calculus, data science and statistics can make you a pro. If you are a beginner, you can gain expertise in these areas by brushing up your math skills for Python Mathematical Libraries. Gradually, you can acquire your adequate Machine Learning skills by building simple neutral networks.

8.  Deep Learning

In the coming years, deep learning professionals will be well-positioned as there is a huge possibility awaiting in this field. With Python, you should be able to easily develop and evaluate deep learning models. Since deep learning is the advanced model of Machine Learning, to be able to bring it into complete functionality you should first get hands-on:  

• Artificial Neural Network Architecture
• Natural Language Processing (NLP)
• Audio and Video Processing
• A good python developer is also a mixture of several soft skills like proactivity, communication and time management. Most of all, a career as a Python Developer is challenging, but at the same time interesting. Empowering yourself with these skill sets is sure to take you a long way. Push yourself from the comfort zone and work hard from today!

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Every year, over 4 lakh Indian students go overseas to study, however, the outbreak of the pandemic has led to uncertainty around the feasibility of pursuing higher education at foreign universities. Luckily for them, well-known international universities are partnering with leading ed-tech companies in India to increase the accessibility of their top-notch programs in new-age technologies like Data Science, Analytics, and Machine Learning.  

With most businesses depending on data-driven insights, the demand for Data Scientists is outpaced the current availability. Data Science programs by Ivy League Universities provide a massive opportunity to students and working professionals who are looking to build a career in Data Science. 

Here is a list of the top 5 international online programs available in India for students and working professionals who are looking to upskill and explore careers in Data Science.

Master of Applied Data Science 

This program is designed by the University of Michigan School of Information and Coursera to help aspiring data scientists learn how to use data to improve outcomes and make data-driven decisions. The program also provides learners with a deep understanding of key topics such as privacy, data ethics, and persuasive communication. The students will not only have access to the renowned University of Michigan faculty and resources but will also have the opportunity to be a part of the University of Michigan School of Information student community. 

Applied Data Science Bootcamp

The 12-week ‘Applied Data Science Bootcamp’ in collaboration with faculty members from the Massachusetts Institute of Technology Institute of Data, Systems, and Society (MIT IDSS) and Great Learning is for students and working professionals who aim to develop skills in Data Science and Machine Learning. The unique highlight of the program is the “Live Online Classes” conducted by five highly renowned MIT faculty members along with live, personalized mentorship, and hands-on support by Great Learning Data Science practitioners.

Master’s Degree in Data Science

This Master’s Degree in Data Science in partnership with UT, Austin by edX provides solid foundational knowledge in Statistical theory and principles of Data Science to help students learn the skills they need to advance in their careers. The course curriculum incorporates ideas and methods such as simulation, data visualization, data mining, data analysis, large scale data-based inquiry for big data, and non-standard design methodologies, along with topics of machine learning, algorithmic techniques, and optimization, to tackle issues that come up with large-scale data such as memory and computational speed.

Post Graduate Program in Data Science

Amity Future Academy’s data science program in collaboration with Cornell University helps professionals master the concepts of data science, data visualization techniques, decision making, and predictive analysis, and types of machine learning and deep learning algorithms. Through this program, students and professionals can gain an understanding of the process of moving from data to knowledge and how to explore this process to make informed business decisions.

Post Graduate Program in Data Science

Designed in partnership with Purdue University and in collaboration with IBM, the program has a comprehensive curriculum covering statistics, key programming languages, Machine Learning algorithms, along with a capstone project in 3 domains. Students and professionals who are interested in developing skills and experience in Data Science should apply for the program. Learners pursuing the program also get lifetime access to all the e-learning content created by Simplilearn along with access to IBM Cloud platforms featuring IBM Watson.

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September 15, 2020 – While the rapid spread of COVID-19 has exposed many unflattering healthcare truths, the glaring health disparities highlighted by the pandemic are perhaps the most detrimental to patient health.

The virus has had a disproportionate impact on minority and underserved communities, shining a spotlight on existing clinical and non-clinical inequities.

“Minorities are more likely to suffer from chronic conditions like high blood pressure, diabetes, obesity, and heart disease. Additionally, these patient populations typically lack access to adequate healthcare, or have a limited understanding of the healthcare system,” said Sampson Davis, MD, an emergency medicine physician.

“These individuals also tend to work in the service industry – transportation, the food industry, or airports. In these jobs, there’s no work-from-home possibilities that can allow people to distance themselves socially. In that sense, there’s a heighted risk of exposure to COVID-19.”

In order to target and reduce the impact of the virus on minority populations, organizations have increasingly turned to data analytics techniques to better track COVID-19 spread.

“As healthcare experts, collecting data is invaluable in what we do. It allows us to track what is working and what is not. Without data, we will pretty much continue to do the same thing over and over again expecting a different outcome,” said Davis.

“In order to decrease the impact of this virus, we need to track which solutions work and which don’t work, as well as where the virus is currently having the biggest impact. The data also breaks down the tedious minutia needed for our pandemic response, because what works for one community won’t necessarily work for the next community.”

With data analytics tools, providers have been able to deliver the right care to the right patients at the right time. These technologies helped clinicians navigate the early months of the pandemic, allowing them to uncover essential information to appropriately treat their patients.

“We started off thinking medications like hydroxychloroquine would have an impact, which it hasn’t. What we have seen with the data collected is that a medication called dexamethasone has helped us to decrease the impact of the virus. We have also discovered that turning patients from a supine position on their back to lying on their chest or abdomen allows them to actively participate in their breathing, which improves their chances of recovery,” said Davis.

“The data has also enabled us to know where the hotspots were – that at one point in time the virus was surging in New York and New Jersey, and then it traveled south to Georgia and Florida, and then over to Texas and Colorado. When we know where these hotspots are, we can try to understand why these areas are impacted the most, and we can target interventions to mitigate the impact of the virus.”

In the coming months, as the country works to reduce the effect of COVID-19, Davis noted that the healthcare industry will need to use data to get ahead of the virus.

“We have experienced similar scenarios throughout history. We’ve seen polio, we’ve seen the flu, we’ve seen mumps and measles. These are all diseases that we now have vaccines for, and that is the result lot of discovering the technology to defeat the virus or the bacteria, but also collecting the data to know what works and what doesn’t,” said Davis.

“So now, we have to think about how we gather and share data across the country to pinpoint where the need is the greatest. And by knowing where the need is the greatest, we can then say that if we have a vaccine or treatment, we can start there to see what type of impact this treatment may have.”

Data sharing will also play a key role in mitigating the impact of coronavirus, Davis stated.

“The virus is ahead of us and we’ve had to do catch-up for the last six months. And now I feel like we’re starting to get to a place where things are balancing. Unfortunately, we have lost hundreds of thousands of lives. But at the same time, sharing the information liberally and equally across the table allows us to see where the greatest need is and how to make the greatest impact with the treatment of patients,” he said.

“When I started in medicine, we used paper charting, and now we have everything in a digital format with the latest information readily accessible. It goes without saying that data analytics are instrumental when it comes to healthcare delivery day in and day out, and it’s going to serve the same role in defeating this pandemic.”

The pandemic has highlighted the substantial care disparities that exist in healthcare, and many expect that the strategies developed during this period will endure even after the situation has waned.

“We will get past this moment with coronavirus. But it has shown us is that in order to achieve health equity, we have to defeat healthcare disparities. That means reducing the disproportionate numbers of minorities that suffer from high blood pressure, diabetes, heart disease, obesity, and other conditions,” Davis concluded.

“Coronavirus did not target one community or race or gender, but you can see those who are at greatest risk suffer the most. The beauty of collecting the data is not just that we have this information, but also that we can use this information to move forward. My hope is that we all step up and continuously push ourselves to be on the forefront of making a difference in our country.”

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In this world where there is competition everywhere for example in terms of education, sports, jobs, etc. and people always try to show their best to achieve the point of success by defeating the others. It can be thought of as a war that is going around the world with a different name just to suppress the actual meaning i.e. competition. This war will never end until the earth doesn’t get demolished. So, the gist is we need to stay ahead of everybody in the domain we are interested in. To talk in the context of this and also referring to the intended topic of the day we will be collating both the terms together and now let’s move forward. Today, the very first competition that arises amongst the people is how tech-savvy they are that is how much they have the technical knowledge and expertise to solve any problem. Everyone is trying to engineer new things or upgrading the obsolete ones.

So, to help aid in these upgrades we have many technologies around. These technologies are somewhere or the other related to computer-related applications and here we will be discussing the top 5 technologies that a person needs to learn to stay ahead in 2020. Let’s begin!

1. Artificial Intelligence: The very first and topmost technology that one should know of. The technology rotates about the topic of automating the machinery and creating humanoids that can help aid people to solve any kind of task. The concept was started in the 1960s and since then have evolved so much that people are shifting their career towards this field. The field consists of various sub-components that one can master to achieve the desired success. The components include Machine learning, Deep learning, and subsets to Deep learning. With this technology in hand, one can land in his/her dream job and can also get high pay because of the demand for this technology in every employable sector of the world.
2. Internet of Things (IoT): Yet another technology that is closely linked with AI. The idea behind this technology is connecting various tech devices with a single parent device to monitor everything that is taking place within the device and fix underlying issues if any. Also, the idea is to gather and share different kinds of data so that there is no discrepancy in the same. All gadgets that we use are somewhere or the other linked with IoT starting from the mobile device, washing machines, etc. IoT can even help inn minimizing electricity by developing a cleaner and greener city. With this technology in hand, one can come up with flying colors and get a highly paid job.
3. Augmented Reality and Virtual Reality: These are the most used technologies by modern-day people. Many researchers and scientists prefer to work in Augmented reality to practice their work and then work in the real world. Many AI-based environments especially in reinforcement learning use these technologies to test the humanoid in a virtual environment first and then use it in the real world. Today AR and VR are also been implemented in mobile phones and tablets to make things more real for the people. Learning these technologies may aid you in getting a good job in the market out there.
4. Blockchain Technology: The world’s more trending and safest mode of transaction wherein people use virtual coins to pay their bills. This technology gave rise to the Bitcoin and Etherium we see today. There are many such cryptocurrencies out there regarding which a person can take the knowledge and also learn the behind the scenes of how this Blockchain works. This technology is gaining immense popularity since its creation and many people are investing their money on it. So, I would prefer to learn about this and get a good job in the top tier companies.
5. Cognitive Cloud Computing: The cloud as we see today was not the same in the past. Here I am talking about the cloud-based services that are provided by companies like Microsoft, Google, Amazon, etc. These services since time have evolved so much that now we can do nearly any type of computational work in the cloud-like hosting websites, storing databases, performing AI-related work, deploying the work, etc. These things were at first not in the reach of normal people but, with the help of the modern-day cloud, we can get access to these things and use it for our benefit. Today many companies are hiring cloud-based engineers rather than hard-coding ones because of the fast implementation with it and are paying huge salaries to them. So, I would personally suggest learning this technology to get a decent job out there and stand ahead of others.

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Entrepreneur First (EF), a leading UK-based global talent investor with a presence in India since 2019, announced its second Online Demo Day of the year, and its third cohort of startups since inception. EF has supported over 200 founders, helped create and invested in 20 startups in just over 18 months.

As industries embrace a new normal the investor has adopted a remote first approach to its programme, today’s event will be fully virtual. EF has seen a 100% increase in applications received since it first launched, with over 1500 applications in the most recent cohort. Today’s Demo Day has unveiled 12 exceptional founders and their 6 deep tech startups showcasing new age, cutting-edge technologies such as Artificial Intelligence and Machine Learning across different industries. There are over 180 investor confirmations from the Investor Day, and over 50 investor meetings are already booked in with the startups.

“The scale of this year’s global crisis is unprecedented. However, this hasn’t stopped Entrepreneur First’s third cohort of ambitious and talented founders to start globally relevant companies here in India. We believe every crisis offers opportunities for innovation and we are confident that these companies will gain unprecedented investor interest as the new normal encourages the new wave of entrepreneurs to think beyond physical borders. ”said Matt Clifford, Co-founder and CEO, Entrepreneur First.

The startups showcased solutions ranging from AI-powered technology platforms for industries like fintech and retail to Machine Learning enabled solutions in the video streaming industry. One common element that binds them together is the implementation of new age technologies.

Matt further added that, “We encourage budding entrepreneurs and groups of like-minded potential co-founders by providing mentorship, financial support and pre-seed funding to help them form companies. Entrepreneur First dares ambitious people to dream big. We provide the right support to talented individuals who find their cofounders here to unleash their potential.”

Following are the startups that were showcased:

MedPay: MedPay is an AI-powered technology platform that creates a large connected network of Clinics, Pharmacies and Diagnostic centers for the first time ever, thus enabling real-time insurance claim processing

Retail Pulse: Retail Pulse is an AI powered Computer Vision solution that enables Consumer Goods companies to harness the immense data across unorganized retail stores to gain operational transparency, optimize product visibility and re-capture 8% of lost revenue

LitmusEye: LitmusEye is using 3D Vision and Deep Learning to build a package dimensioning system for stakeholders in Express Logistics, to help them bill, plan & utilize assets efficiently

Vadoo: Vadoo uses peer-assisted streaming, powered by ML, to help video streaming platforms significantly reduce bandwidth cost and improve latency

Marlin AI: Marlin is building an AI enabled platform to optimize the land-side movements of containers for shipping lines, helping enhance their utilization and revenue streams

BrainEntech: BrainEntech is disrupting the online education industry by using Deep Learning on brain activity of learners to enhance content creation and consumption

Speaking about the third cohort, Esha Tiwary, General Manager, India at Entrepreneur First said, “The founders from this third cohort have faced incredible headwinds and adversity. But we continue to be impressed by their resilience and resolve to build globally important companies. At EF we encourage outliers to choose their own path and found companies for the future. EF believes that connecting such talented, resilient tech founders with the investor community will create tremendous value for the Indian startup ecosystem.”

Esha further added that, “Since we launched, we have seen a very high quality of founders and startups at EF India – we are convinced that world class innovative technology-led startups can be created from our region. Our applications are growing significantly with each new cohort every 6 months. We are also very closely working with other ecosystem players and investors to create a robust structure for supporting early stage tech entrepreneurs in India.”

EF had recently collaborated with Foundation for Innovation and Research in Science and Technology (F.I.R.S.T.), a section 8 company promoted by Indian Institute of Technology Kanpur (“IITK”) to build a strong support ecosystem for aspiring tech entrepreneurs. This partnership facilitates the journey for exceptional and talented individuals who aspire to start tech ventures. EF will support with the process of finding a co-founder at the very early stage of a startup, as well as provide close mentorship on the idea and business model, and pre-seed investment. The startups formed will have an opportunity to access IITK business incubator as the next step to build a network of expert advisors, access to prototyping facilities and grants. The partnership will also help expose smart technical graduates and researchers from IITK and affiliated institutions to the potential impact they can create through entrepreneurship.

EF appointed Venkat Raju as Venture Partner recently in India. He has more than three decades of experience in strong business, technology and general management. Venkat will work closely with the Entrepreneur First startups in India across areas such as technology, business model and go to market strategies. His expertise will help the Entrepreneur First start-up ecosystem build on its foundation in India.

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