Data Science, AI/ML, IoT and Analytics Trends During the COVID-19 Recession
The coronavirus (COVID-19) outbreak is having a growing impact on the global economy. So, how is the impact of COVID-19 going to be on the tech job market and what are the latest trends for data science, AI/ML, analytics, IoT, cloud computing? What are the key in-demand tech job profiles and domains during and after the COVID-19 phase? Let’s find out.
There have been more than 12,750 confirmed cases of COVID-19 in India so far. Between April 6 – 12, 46% and 39% of new confirmed cases have been reported in Europe and the USA respectively. Several nations including India, UK, etc. are under lockdown currently. Lockdowns, while effective at slowing the spread of the virus, are economically harmful. The world is already witnessing layoffs, furloughs, and pay cuts amid the current financial crisis due to COVID-19. However, just like biosciences, there are a few tech job profiles that are likely to be in high demand during and post-pandemic recession.
AI, ML, Data Science, IoT and Big Data Analytics Trends During the COVID-19 Recession
Although a lot of Tech and IT employees are feeling the heat, hiring in the AI sector is supposed to stay positive during this phase. In fact, between February and March 2020, job titles that include the term ‘artificial intelligence’ (AI) are on track to increase by 11%” at a time when job postings in most occupations are falling. 285 AI-related companies in the U.S. raised $6.9 billion in the first quarter of 2020 and the number of AI jobs worldwide could increase by 16% in 2020, reaching a total of 969,000 (sources: WSJ, and Forbes).
Let’s dig in a bit deeper.
AI, ML, Data Science, and Analytics Trends (2020 – 2021) to Know for Job Seekers
AI, ML & Data Analytics in Biomedical Research
A lot of work is going on to try to develop a vaccine to find out whether there is any current drugs work against COVID-19. All of those projects require molecular modeling, and many of them are using AI and machine learning (ML) to map things we know about the virus to things in pharmacological databases and genomic databases.
AI can eliminate many false tracks and allow us to identify potential targets. So instead of trying 100 or 1000 different things, researchers can narrow it down to a much smaller size much faster. That’s going to accelerate the eventual finding of the vaccine.
To make any real progress in this situation, you need to bring together people who understand the computation and AI, people who understand the biological and biomedical implications, and people who understand population models. It’s a very interdisciplinary problem, and to make any headway, the world needs the data and the academia & industry need the trained talent.
AI, ML, IoT & Analytics in Supply Chain Management & Logistics
From shortages of personal protective equipment to a variety of grocery items to electronics and apparel, coronavirus (COVID-19) has hit the global supply chain in expected and unforeseen ways, and it seems likely that it could take many months to recover.
Bouncing back more quickly, said experts, will require supply chain managers to turn to new ways of managing the supply chain, including using Internet of Things (IoT) data, analytics and machine learning. These tools will become the foundation on which supply chain managers gain insight into their markets and erratic supply and demand trends. Read Analytics and IoT in Supply Chain Management Becomes Critical as Coronavirus Spread Escalates.
Deep Learning & IoT in Monitoring Mobility and Social Distancing
Recently, the Newcastle University Urban Observatory has developed an urban data dashboard to help understand the impact of social distancing measures on people and vehicle movement within a metropolitan city in real-time. The objective is to understand the dynamics of movement in a city using thousands of sensors and data sharing agreements to monitor movement around the city, from traffic and pedestrian flow to congestion, car-parking occupancy and bus GPS trackers. It also monitors energy consumption, air quality, climate, and many other variables.
This type of data not only shows how physical distancing is changing in real time, but will also provide detailed insight into long-term behavioral changes.
Read IoT Statistics for 2020.
Cities, enterprises, and national governments should collectively create a massive global network of sensors to detect viruses for future pandemics as well. This further fuels the implementation of the “smart-city” concept, which had received severe criticism in the past due to data-privacy issues.
Smart cities use information and communication technologies (embedded sensors) to streamline urban operations on a large scale. Technological ecosystems collect traffic, noise, air quality, energy consumption, and movement data in order to make improved and sustainable decisions by authorities and enterprises. Citizens can engage with the smart city in a number of ways. Read the full story here.
Data Science & AI in Controlling the COVID-19 Situation through Social Media
Be it reaching to the targeted audience through cluster algorithms or identifying the at-risk individuals (who are close to the infected individuals in terms of frequency and recency of communication) through graph analysis, data science and AI will have a big role to play by leveraging the social media platforms.
Additionally, natural language processing (NLP), data mining, and predictive analytics will come handy to detect (and prevent) fake news and analyze people’s movements in the near future, including travel plans, destinations, and the number of travelers.
Read more about leveraging social media and data science to combat COVID-19.
IoT & AI in COVID-19 Screening and Diagnosis
Connected thermometers are being used by hospitals (and at other public locations) to screen patients and staff. Kinsa Health has used data gathered from its over one million connected thermometers to produce daily maps showing which US counties are seeing an increase in high fevers. These data points are capable of providing unparalleled real-time disease surveillance and could serve as an early warning sign of new clusters of the disease.
With the new Coughvid app analyzing cough and screening-tests by various chatbots, AI is doing a great job in the process of detecting coronavirus cases. Given the limited bandwidth of the health agencies, conversational AI is going to be critical during this phase.
Interactions with conversational AI agents provide real-time insights to understand where there is a trend of unanswered questions and anxiety across different groups of people, such as test sites, turn-around time for results, common symptoms, location-sensitive recommendations, etc. A showcase example is MyGov in India, the largest government to citizen digital infrastructure in the world. MyGov launched the MyGov Corona Hub with an interactive Facebook Messenger system: https://m.me/MyGovIndia.
Cloud Computing in Making Remote Work more Convenient for Organizations and Team Members
With so many people in self-isolation for what could be as long as 18 months under some estimates, and with a possible long-term shift towards home-based work for a large segment of the population, large-scale deployment of public 5G access points is likely to be curtailed.
Cloud capacity, especially from the hyperscale vendors — Amazon Web Services, Microsoft Azure, Google Compute, Oracle Cloud, Alibaba Cloud, and IBM Cloud — is what is going to get us through this storm. With a remote, home-based workforce, remote desktop technologies will be essential until native born-in-the-cloud applications can run core business functions.
Legacy vertical industry Windows client-server apps are problematic and will need to be refactored and rewritten entirely for PaaS, or SaaS. This will occur using enabling technologies such as containerization and microservices because VMs and IaaS are computationally very expensive.
Cloud computing is already helping. It can be even more helpful during the COVID-19 phase. Read In The Era Of The COVID-19 Crisis, Look Up To The Cloud(s).
AI and Neural Networks in EdTech, Fitness, Entertainment, Social Media and OTT Platforms
With people staying indoors, the demand for online learning, fitness, social media and digital consumption has increased exponentially across the world. Video conferencing apps such as Zoom, Google Hangouts, Google Duo and Houseparty have seen a 71.11% increase in time spent while the average user count has risen by over 100%.
You can read the full report and analysis here. Since users will be looking for more personalized digital content, the demand for professionals with solid skills in AI, ML, Deep Learning & Neural Networks will go up tremendously.
AI, ML, and Cybersecurity in FinTech
Banking and financial services are essential for everyone. With people working from home and maintaining social distancing, this would be a great opportunity for fintech startups during the COVID-19 phase. Online services that we can pay for through fintech are now practically covering every industry imaginable. In Europe, the COVID-19 situation has already driven a massive 72% rise in the use of fintech apps.
Even in such tough times, Setu (a Bangalore-based fintech startup) raised USD $15m. Kaarva, another fintech startup that has launched the COVID-19 Sure product, where employers can help reduce uncertainty for their employees by providing quicker access to their own salary and provide options for affordable advances in any financial stress.
ML and AI play a crucial role in banking and financial services (and the fintech ecosystem). Additionally, the issue of cybersecurity in fintech is also extremely critical. So, the demand for such professionals in the fintech space is likely to be there.
Conclusion: Tech Job Market Trends in a Nutshell
The crisis is in its early days and it is hard to predict where it will head. However, according to the recent survey report by Burtch Works and IIA (International Institute for Analytics), the scenario for data science, AI and analytics professionals is promising. Demand for data-oriented occupations and skillsets skyrocketed in 2019. This year, it might not see the same speed, but there will be jobs for good talent. Read the full report.
Data scientists and data engineers have built-in job security relative to other positions as businesses transition their operations to rely more heavily on data, data science, and AI. That’s a long-term trend that is not likely to change due to COVID-19, although momentum had started to slow down. Read COVID-19 crisis is likely to trigger growth in the AI sector and Data Science, Analytics & AI Trends 2020-2021 for Job Seekers and Study Abroad Aspirants.
Suggested Roadmap for Aspiring Data Scientists and ML / AI Developers
- Start learning Python including Pandas and NumPy
- Get your basics cleared on Linear Algebra, Probability, Multivariate Calculus, and Inferential Statistics
- Get introduced to SQL for data science
- Start engaging in data science and machine learning forums and communities
- Learn the basics of Machine Learning – Linear Regression, Logistic Regression, Decision Tree, Naïve Bayes, Vector, Supervised & Unsupervised Learning
- Spend time on building your GitHub profile
- Go for the advanced stuff in machine learning – Random Forest, Ensemble Learning, Time Series, and Hyper-parameter Tuning
- Work on your resume, network with people, participate in competitions (e.g. Kaggle), and look for projects and/or internships.
- Master Recommender Systems
- Get started with Neural Networks and Deep Learning
- Understand Convolutional Neural Network
- Get involved in Computer Vision Projects
- Get familiar with NLP – Text Processing, Cleaning, and Classification
- Polish your resume
- Apply for Jobs
Data Science, Analytics & ML/AI Trends (2020 – 2021) to know for Aspiring Graduate (MS & Ph.D.) Students
Combining AI with Cybersecurity
As the volume, velocity, variety, virality, and viciousness of cyberattacks inexorably increase, AI solutions will increasingly become the only way to compensate for the projected shortfall of 1.8 million cybersecurity professionals. It’s going to be one of the hottest AI trends in 2020.
Applying Reinforcement Learning in Business
Reinforcement Learning (RL) has begun to show up in enterprise applications. Netflix, YouTube, and Facebook have described how they’ve incorporated reinforcement learning into production recommender systems. So, applications of RL in business process optimization and simulation is a great area to explore.
Hyper-Automation of Data Science and Machine Learning
Automated Machine Learning (AutoML) tools along with deep learning and Natural Language Processing (NLP) are going to be the hot research areas within the field of machine learning.
With Robotic Process Automation (RPA) at its core, hyper-automation is a combination of process mining, AI, analytics, and other such tools.
Data scientists also require tools that allow them to scale and solve more problems. Augmented analytics will automate more of what analysts and data scientists do today.
The “black box” stuff is going to be obsolete. AI and ML have to be explainable and applicable in a business context.
Image Data Processing using AI and Deep Learning
Image data will grow in importance as the camera becomes more than a way to capture memories. With improvements in facial recognition technologies, people and local governments will be pushing back even harder against the invasion of their privacy.
Combining IoT with AI
With 5G adoptions steadily increasing, there will be more exciting initiatives that arise in the area of convergence between 5G, IoT, and AI. The fields of big data, IoT and AI will continue to fuse, and where optimized AI chips and 5G will enable amazing innovations. This will enable AI to be used as a service (or AIaaS), in the form of cloud services, and help organizations to leverage machine learning.
Data Analytics for Better Decision Making
Being able to build a great data visualization report, analyze and read data is one thing, but it has to translate into smart decision making. So, implementing machine learning and AI will be critical for analytics as well.
TensorFlow vs PyTorch
Tensorflow is based on Theano and has been developed by Google, whereas PyTorch is based on Torch and has been developed by Facebook. As per experts, PyTorch could gain more momentum in 2020.
If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options. However, it’s also wise to explore Keras.
Featured Image Source: Forbes
Author: Tanmoy Ray
I am a Career Adviser & MS Admission Consultant. Additionally, I also manage online marketing at Stoodnt. I did my Masters from the UK (Aston University) and have worked at the University of Oxford (UK), Utrecht University (Netherlands), University of New South Wales (Australia) and MeetUniversity (India).
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