What is epidemiology? It is the study and analysis of the distribution, patterns, and determinants of health and disease conditions in defined populations. And what if you add data with some AI power to this mix? 

It can detect patterns, red flags, spot unsafe areas, and help policy-makers and healthcare teams respond quickly. This happens with a significant elevation of the efficacy of predictive epidemiology models. In addition, machine learning (ML) can help epidemiologists evaluate as many variables as desired without increasing statistical error. This matters because it is a problem that often arises with multiple testing bias, which is a condition that occurs when each additional test run on the data increases the possibility for error against a hypothetical target result.

Or imagine using Natural Language Processing (NLP) to capture clinical notes for preservation in EHR databases. Artificial intelligence (AI) can then identify conditions, diagnoses, and exposures that are otherwise difficult to grasp or identify using traditional data source mining. This opens new windows for data discovery and validation and knowledge representation.

Data against diseases

An enormous area for artificial intelligence (AI) in epidemiology is drug discovery, safety, and risk analysis. It is slated to be a $699 million global market by 2026. AI can also find applications in disease and syndromic surveillance, infection prediction and forecasting, monitoring population and incidence of disease, and use of AI in Immunization Information Systems (IIS). AI has tremendous value in mapping vaccinations to disease incidence, assessing the impact of public sentiment analysis, and public safety services such as mass notification.

Epidemiology and Data Science – it turns out they are an excellent formula. Put together, they can act as painkillers for a lot of problems. We need to find answers to several thought-provoking questions.

• Is Covid-19 ‘lab leak theory’ plausible?
• How do we build effective data models for a complex phenomenon like the COVID-19 pandemic?
• What has been the learning from the first and second waves?
• How good are the models that we worked on?
• What has been the accuracy and the fallacies? 
• The data-angle of the pandemic with the wet-market theory of Covid-19.

Experts argue that humans are a very attractive host to viruses. It is natural for a virus to make the transition because it helps their ability to survive. They are just trying to adapt to us and not, maybe, kill us. The lab theory is not entirely plausible, but specialists remind us that we will never know the real answers given China’s unwillingness to share information. Every bit of evidence we have is circumstantial. It is difficult to rule out either of the pathways – whether the bat market or the lab.

‘Pandemic Pandemonium’ – A DeepTalk with Dr. Ramanan Laxminarayan, a Leading Epidemiologist and Economist

There is so much data being produced every day. You can build so many models, but what about a once-in-a-century event like the pandemic? How do we go about building models here? 

Viruses show predictable behavior, to some extent. They work purely in an algorithmic sort of way, with not much variability. But human behavior dictates the transmission. That isn’t easy to predict. The scientific community faced this challenge in HIV scenarios, too. 

Viral dynamics are not independent of how we behave. Understanding models through large data is excellent both from a prediction and response angle. Data Science is the only way in which we can understand diseases. 

Artificial intelligence (AI) empowers epidemiology

These arguments and directions are significant given the direction that AI is taking in the epidemiology space. 

• Globally, the total AI in the epidemiology market will reach $9.7 billion by 2026, and machine learning (ML) in the epidemiology market will touch $1.2 billion by 2026, as per ResearchandMarkets’s estimates. 
• Related to vaccines R and D, we would probably see that AI in drug discovery and risk analysis will reach $699 million by 2026. 
• Also, AI will support various disease-related public health and safety services, such as mass notification. We would also see EHR databases and other disease-related data aggregation. 

Interestingly, Epidemiologic predictive models will find many improvements via advanced data analytics and various AI tools and techniques. This will be a giant leap, as AI can improve the efficiency and effectiveness of transforming healthcare data correlation to meaningful disease insights and information.

The first and second waves have shown a lot of impact, success, and fallacies of models. In India, the peculiar part is accepting when we see mind-blogging numbers. If we look back historically, no one would believe that we would see 400 million infections. Yet, some experts were spot-on about how ferocious the virus can be. The prediction was made when there were 400 cases in India. But a numerical person would know that exponential curves work that way. Even if psychologically it is hard to accept.

The crisis is not over yet

It looks like we have data. We have the tools too. But we might need the mindset of acceptance and interpretation to use it well so that we do not repeat the mistakes we have made already.

We need to fight these anti-science and anti-intellectual cultures emerging in the country, especially in social media. The deeper issue is that the challenges that the world faces today are not trivial. They need a significant level of study. Most people do not know about CRISPR, for instance. Most people would not know how to use it, and it is so powerful in changing our lives.

Asking a doctor to fix public health is like asking a plumber to improve the city infrastructure. Being a doctor does not mean that person is a data scientist. It is primarily anecdotes, he added.

We have to sort this out. Covid is going to be a minor problem compared to what lies ahead, like climate change. We have to get smarter and fight it together with knowledge.

Primary healthcare should be powered with early warning systems and timely screening systems. It should be about the management of well-being and not the management of disease, they both concluded with a sharp reminder to the country.

Artificial intelligence (AI) can help a lot in detecting diseases and helping to prevent them – but the actual ingredient in this pill is data. So it is important to build relevant capabilities to leverage Data Science and AI.

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Data Science was at the 3rd rank in 2020 Glassdoor’s annual ranking. It is not a surprise. After all, every enterprise and every industry vertical is waking up to the power of data and insights. They need to tap the fresh waves of digital transformation, customer intimacy, and personalization-edge.

As per the U.S. Bureau of Labor Statistics, there has been substantial growth in the Data Science field, and the number of jobs is expected to increase by about 28 percent through 2026. In the reckoning of LinkedIn, too, there has been a 650% jump in Data Science jobs since 2012. Closer home, analysts predict that the country will have a massive number of job openings in the coming years. 

And why not? Companies are scrambling to make the most of the power that Data Science and its adjacent areas bring in. Gartner predicts that by 2025, the AI and Data-Science-equipped VC or PE investor will become commonplace. As per ResearchandMarkets, the Global Data Science Platform Market size could reach $165.5 billion by 2026. The report explains that the models used for estimating or segmentation fail because of rapid changes in online traffic or shopping trends. As the borders have been locked down and the supply chains have been disrupted, organizations focus on new priorities like laying short, medium, and long-term data-driven plans to make educated choices. It is also spotted that new cycles are being completed. Data substitution, changes in traffic, focus on healthcare-related supply chains are some current trends in the market.

The drive towards AI and Machine Learning is adding a new fillip to the Data Science domain. A McKinsey Global Survey on artificial intelligence (AI) suggests that organizations use AI as a tool for generating value. The companies that are using AI are seeing that value accrue to the enterprise level. Almost 21 percent pointed out that over 5 percent of their organizations’ enterprise-wide EBIT in 2019 was because of their use of AI, with 48 percent reported less than 5 percent. Since top performers can develop AI solutions in-house – this creates a domino effect for the demand curve for data science professionals. As opposed to purchasing solutions, companies typically employ more AI-related talent, such as data engineers, data architects, and translators, than their counterparts. They have built a standardized end-to-end platform for AI-related data science, data engineering, and application development.

Dealing with a thin supply chain

But are there enough professionals and capable professionals to match this growth appetite? In India, thousands of jobs in Data Science were vacant in 2020. A major portion of these vacancies was categorized for positions with less than five years of experience.

That’s surprising given the attractiveness of this field, both financially and intellectually. In Michael Page’s 2021 India Talent Trends report, Data Science professionals with 3-10 years of experience are slated to get annual salaries in the 25-65 lakh range. However, those with more experience can command pay packages upwards of 1 crore. Professionals with over 15 years of experience can command up to 1.8 crores. Also, the average annual pay hike for Data Science professionals ranges at 20-30 percent compared with 15-20 percent for professionals from other backgrounds.

Bring the fork, can you?

It is the best time to equip oneself in the Data Science domain and leverage this growth trajectory. 

Learn Data Science right – do this at a place where you are taught by someone who has seen the inside-out of this evolving field. And where you can gain from the minds of top researchers like those leading the research initiatives at the Robert Bosch Centre for Data Science and AI (RBCDSAI) at IIT Madras. You learn it right when you are taught by someone who will encourage and challenge you, helping you to reach your full potential.

A recently held webinar introduced Dr. Balaraman Ravindran and Dr. Arun Rajkumar, Faculty Members of IIT Madras’ Data Science Programme.

Like the IIT Madras’ Data Science Programme, where you will learn from top researchers leading AI and Applied Data Science research at RBCDSAI, India’s leading research hub. One needs to have a clear grasp of the foundational level and then have a progressive coverage of other modules like supervised and unsupervised machine learning, deep learning, sequential learning, algorithms, and later to real-world use-cases, industry-level challenges.

In a recently held webinar Dr. Balaraman Ravindran and Dr. Arun Rajkumar, IIT Madras faculty members, gave an insightful glimpse into how the IIT Madras And TalentSprint’s Data Science Programme is a tailor-made solution for emerging professionals, how participants will be benefited from the cutting-edge research of the Robert Bosch Centre for Data Science and AI (RBCDSAI) and more, also, how candidates can be equipped to handle technical rounds and other placement challenges.

The temperature is correct, and if you can tap this unique opportunity, you can build a robust career in the exciting and ever-fresh field of data. So pick it while it is still piping hot.

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Data Science is everywhere – from fraud and risk detection, internet search, targeted advertising, product recommendations, advanced image and speech recognition, predictive modelling to product development and more. 

Data Science is exploding 

The global Data Science platform market is expected to hit $25.94 billion by 2027. It will expand at a compound annual growth rate (CAGR) of 26.9 percent from 2020 to 2027, as per GrandView Research. Today, Data Science platforms can empower Data Scientists to design techniques, reveal insights from information, and a lot more. The focus has become stressed because the COVID-19 (Coronavirus Disease) pandemic has also affected the Data Science industry. The models earlier used for forecasting or segmentation are failing. This could be because of rapid changes in online traffic or shopping patterns, as stated in this report. 

As companies revisit their data strategy and map short, medium, and long-term data-driven plans to make informed decisions, there would be a need for a deeper understanding of data models. There will undoubtedly be a massive demand for the right people to design and operate these models.

Building Data Science capabilities

It is an excellent time to sharpen one’s Data Science proficiency and be ready for all the new defining opportunities ahead with Data Science. As the pandemic has reminded us well, every industry would be impacted with the help of data. There is a significant need to invest in the right capabilities, which would traverse all kinds of domains. So no matter which vertical you are in, it is advisable that you build up your ability to leverage data through intelligent and effective models. Without this expertise, it would be hard to leverage what the future brings forth. For example, one needs to know why and how to avoid over-fitting, regularization, over-learning excessively based on existing data, etc.

The world of Computational Data Science

Computational Data Science is the study and practice of Data Science that requires modern high-end computational infrastructure.

IISc Faculty Masterclass on ‘Data Science in Practice’ where Prof. Sashikumaar Ganesan and Prof. Deepak Subramani from IISc shares a glimpse of the evolving domain of Data Science

Also this space forks into supervised and unsupervised learning.This can go deeper into clustering, anomaly detection, density estimation, and dimensionality reduction in unsupervised categories as well. And similarly into the workings of instance-based and model-based decision structures.

Simply use a tree with as many leaves as several training points. Restrict the decision tree’s degrees of freedom. Use some hyper-parameters to regularize and avoid overfitting.  There is a full assignment in the Computational Data Science Programme to learn how to do it. We have to also learn about issues like – decision trees produce orthogonal decision boundaries. Also, rotating data makes the tree convoluted. Decision trees are sensitive to slight variations in training data. That’s where random forests can overcome this instability by ensemble learning. 

That’s why this program is built on basics and the building of mathematical models. The program is designed with many assignments, one-to-one interactions, cohorts, and doubt-clearing sessions with unique slots with professors. Structured projects, hands-on experience for theory as well practice are being focused on here. The emphasis is on intuitive understanding as well.

All classes are live. As to the program’s eligibility, any graduate with comfort in basic mathematics and coding language with some work experience would be a good fit for the program. Team leads, architects, software developers, exceptional engineering graduates from various sectors like IT, healthcare, banking – can easily join. It is an advanced program that will give candidates an added advantage, he iterated.

The practice, needs, and constraints of making a model are difficult but of enormous advantage today. These aspects were unlocked well in this Masterclass on ‘Data Science in Practice.’

TalentSprint has been covering many aspects as an interactive series in the emerging technology space like AI, ML, Cyber Security, Data Science, FinTech, IoT, etc., through such discussions and master classes. As a result, current and aspiring professionals get a direct peek into the breadth and depth of the DeepTech programs.

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If data has become the new fuel that drives the world, imagine what this new reality means for people who can drive well with this fuel. Enterprises from almost all verticals are investing in the power and impact of Data Science. As a result, they are reaping huge gains with real-time insights, unprecedented customer intimacy and split-second decisions made well and with a competitive edge – all thanks to the vast pools of data that they can now use to their advantage.

The maths behind the science

Data Science is, indeed, changing the world drastically and irreversibly. It is creating exponential opportunities for professionals. As a result, the market is continuously on a northbound trajectory. As per MarketsandMarkets, the Data Science platform market size is rising from $37.9 billion in 2019 to touch $140.9 billion by 2024. Research Dive’s estimates put the size of the Data Science platform market at $224.3 billion by 2026 and attribute its growth to a lot of factors like how Data Science aids a user to assess, build, and control data. Among other boosters for companies as advantages of Data Science, we can observe many attractive facets like attracting new customers, updating business processes, and giving meaningful insights into the data. And all this potential has to translate into demand for professionals with relevant expertise.  

Look at a recent Dice report, and you will notice how demand for Data Scientists in 2020 surged by an average of 50 per cent- and this was seen across the berth, from healthcare, tele communications, media/entertainment, to the Banking, Financial Services, and Insurance (BFSI) sectors. Some time back, the Dice Q1 Tech Job Report unravelled that companies have started to look towards building for the future and hiring technologists again in considerable numbers. This is where Data Scientists (ranked 34th, improving by six slots) have gained enormously from this trend. In terms of job postings’ growth rate tracked here, Data Scientists registered a 27 per cent jump.

Thinking of Data Science? Think of this too

It is, indeed, a good time for aspirants to leverage this wave and be at the forefront of this opportunity. First, however, they need to tap this well by equipping themselves with relevant expertise. Well-stacked and excellently-executed programmes pave the path of accelerated growth. But it can be tough to spot the difference between ‘any port in the storm’ and ‘a programme that will make you travel to a bright future’. 

“ If you are an aspirant of Data Science and want to change the domain this is the right programme to start with. You will get all the concepts to get started with the Data Science journey.” ~ Kanchana Kumar, an alumnus of IISc and TalentSprint’s Computational Data Science Programme

Kanchana Kumar, an alumnus of IISc and TalentSprint’s Computational Data Science Programme, recently shared his learning experience with us. This is like hearing from a person with a hands-on perspective in building Data Science capabilities. Here are the excerpts. 

1. Even if you know the brass tacks, a good programme won’t hurt because it will give you a strong foot forward in what matters out there right now. For example, people familiar with only a few python libraries specific to Data Science came out of the program with a wide berth of knowledge. In the programme, they could learn how machine learning algorithms work, the basic mathematical concept behind each algorithm, data engineering concepts and parallel programming concepts. But above all, they became proficient and confident in analysing raw data to derive hidden insights to solve business problems. That’s the ultimate value that a true Data Scientist can bring.
2. It is not just what you learn in the programme, but what you learn from people. The programme at TalentSprint, for instance, ensured that the cohort is the most diverse one in terms of experience, domain and the organisation to which these participants belong. As a result, every day became learning for these programme participants – and practically and collaboratively.
3. While the quality of faculty, trainers and session design are essential factors that can impact the actual value of the programme, the practical side also matters a lot. Like – how well-curated the programme is, how fast and responsive are the teams on support and query-resolution; and how well-managed the entire administrative part is. But, of course, good programmes like this one always take care of these BTS (Behind The Scene) details.
4. It is an excellent option to consider for general aspirants, but more so for professionals who wish to change their domain. These programmes are great starting points to cover the domain fundamentals and enable a smooth transition.
5. Just coverage of topics will not suffice. Unless the topics are taught in the context of actual industry needs that will play out outside the progr experience, the programme would be just another tick-in-the-box. Each discussion with faculty, cohort, mentor and the team helped the programme participants to master different minuscule aspects of the Data Science domain. And this makes a significant difference in the real takeaway of any Data Science programme.
6. What also sets a programme apart from the crowd is the value amplified by the Capstone projects. The participants worked on the Computer Vision R&D domain. The objective of the project is to develop a super-resolution image from a low-resolution image using the GAN algorithm. The programme helps the learners confront practical issues like dataset availability and the resources needed to train the image dataset. With the mentor’s help, the participants applied the concepts learned in the last 10 months and completed the project.

So remember these guiding stars and start driving faster on the exciting lane of Data Science opportunities.

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As per the Data Security Confidence Index from Gemalto, around 65% of the companies agreed they cannot categorize or analyze their stored data. Also, 89% of the businesses know that they’d have a competitive edge if they could analyze and categorize data. In another study conducted it was found that 90% of the data across the globe was created within the previous two years. In 2018, the amount of information available globally totaled 33 zettabytes, and projections show an increase to 133 zettabytes by the year 2025.

Data Science, also referred to as the “oil of the 21st century,” carries the importance of digital data in the field. It combines mathematics and statistics, scientific methods, advanced analytics, specialized programming, and artificial intelligence (AI). This multidisciplinary approach extracts valuable insights from the enormous and ever-increasing volumes of collected data. It presents results and benefits research, businesses, and everyday life by revealing patterns to draw well-informed decisions. 

Data Science was already having a big moment before the pandemic. The interest peaked further when it took centre stage in battling the spread of COVID-19. Businesses, too, are waking up to the need of being adequately skilled at data acquisition and analytics to combat and grow with what comes next.

In a recent TalentSprint webinar titled ‘Data Science in the Post-COVID World’,  Bidhan Roy, Director (Data Science/AI), Business Analytics and Research, Fidelity Investments answered some unique and intriguing questions.

Is there a dip in the demand for Data Scientists post Covid-19 pandemic? What are the objectives of artificial intelligence (AI)? What makes Data Science adoption a must for organizations? How can professionals train and tap such Data Science opportunities? and more.  Excerpts.

Data Science in the post COVID world – myth or truth?

Data Science uses large volumes of data to make informed business decisions. It is a study to create new products. Typically, Data Scientists analyze data and find new insights. They help companies to make progress with gathered data. Modern Data Scientists are part mathematicians, part computer scientists, and part trend spotters. They work with advanced machine learning (ML) models and know the technicalities to predict future customer or market behavior based on past trends.

How to gear up for the post-pandemic world with Data Science? Bidhan Roy, Director (Data Science/AI), Business Analytics and Research, Fidelity Investments, shared some interesting views and insights.

The goal of businesses from Data Scientists is to make data-driven decisions for business impact. Demand for Data Scientists is relatively high, with abundant opportunities to evolve. However, with the Covid-19 pandemic, there was a bit of flattening out of the demand. Today, after a few months of the pandemic, there are no signs of a decrease in the demand for Data Scientists and a slowing down of Data Science. 

With growing data, the need for data analysis and making sense of gathered data is also increasing. Thus, there is no such thing as Data Science post-Covid. There is a sky-high pay and growing demand for Data Scientists in various companies. It has resulted in an upward career arc.  

Expectations from Data Scientists

Data Scientists find data analytics issues, insightful data from big data, solve data problems, and know the current trends to boost business performance at all levels. They must use suitable algorithms or potential modeling techniques to solve business problems, craft experiments, and identify risk areas, issues, and/or opportunities at hand. Identifying business needs and collaborating with stakeholders is a crucial role of Data Scientists. 

Challenges in the adoption of artificial intelligence

The impact of artificial intelligence (AI) on the economy and our lives has been astonishing. Applications of AI are countless. Expectation from AI is humanlike, and the AI technologies are classified into sense, comprehend, and act. The machine must sense and perceive the data through audio-visual processing. It must recognize the pattern and understand the information as context or pattern. AI must enable the machine to derive powerful insights that can support action.  

One of the significant challenges in the adoption of AI is privacy. AI works on a massive volume of confidential data. This information is often personal and sensitive. Unclear privacy, security, and ethical regulations create a plethora of challenges. In some countries, the General Data Protection Regulation (GDPR) act adds further challenges to the adoption of AI. Plus, AI needs highly skilled and trained professionals. Lack of awareness on the adoption of AI in businesses, infrastructure, technology, and research poses further problems. However, companies and businesses are aggressively adopting AI and trying to diversify it. 

Does Data Science have more scope?

The technology-driven 21st century is data-driven. It is one of the lucrative career options in the entire world. There is a high demand and never-ending shortage of Data Scientists. It is a versatile career, and one professional can jump from one role to another. In Data Science, the sky’s the limit.
To enter the vast field of Data Science, professionals have to build new capabilities and gain in-depth knowledge of its practical application in various fields.

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Data Science and Machine Learning have become a savior for several organizations across industries in accelerating their digital transformation journey. Today this field is highly evolved, and so are the imperatives and opportunities for Data Scientists. 

“Keep telling stories. Be a great Data Scientist. New tools, new cross-pollination, and new purposes – a lot is changing for data professionals. But there is no excuse not to learn the fundamentals. And not to tell delightful stories. We need to foresee the future, develop it, and bring it here.”

That was the poignant thought that Dr. Chiranjiv Roy, Senior Vice President and Practise Head of Data Analytics at SG Analytics, a leading provider of data-centric research and contextual analytics services, stirred up when he shared the trends and forces of Data Science and Machine Learning in a recent webinar.

Dr. Roy thoroughly explained these aspects and then interpreted them from the context of professional capabilities. He unlocked the fascinating elements of story-telling and solving as he drilled more profound into the paradigm of Data Science and Machine Learning.

“Data Science is not a fresh idea. But it is becoming more and more important for future professionals.” He augured. Are we not doing it all already – like Analytics, etc.? Yes, we are. But today, 80 percent of data is unstructured. We need highly scalable algorithms. Look at how people have been hiring from 2018 onwards – that reflects a decade’s switch. Statistical modeling became Data Science. Things have been evolving in this space.

Join hands

He also pointed out how today’s Data Scientists have to understand software engineers. Data Analysts and Data Engineers have to work together today. The way we used to approach Data Science has also evolved. In traditional Data Science, it was about modeling and reports. Now we need to stand out, scale out of our general ways of development. Now acquisition, modeling, etc., are different. You do not choose a model and sit down for the next three months. Today you run the models on the fly and tune them as required. The demand requires you to architect algorithms on the move. It does not require you to know everything. But you need to understand the entire picture. A Data Scientist has to create a wireframe and pass it on to a Machine Learning Engineer, and hence they should know the programming languages here. The model can take shape as expected. It is not a Jack-of-all-Trades thing, but one should be cognizant of the overall context.

Also, the world is about to grow strongly toward IoT. Every sensor and device in IoT is churning signal data. That’s when we need Python to transform the data. We need to watch patterns to see fluctuations etc. IoT and AI will be big, and Data Science would be a vital bridge and enabler.

Stop worrying

TalentSprint’s industry interactions have been covering many such issues as an online interactive series on DeepTech with leaders, experts, and trendsetters in the emerging technology space like AI, ML, Cyber Security, FinTech, IoT, etc. 

In this episode, Aritro Bhattacharyya, Senior Director Sales at Marketing at TalentSprint, asked Dr. Roy many exciting questions that came from curious and ambitious professionals and learners. They dotted many areas of the future map.

Do I need to be a mathematician to become a Data Scientist? Someone asked in the webinar. Yes, said Dr. Roy. But to what degree would depend on a host of factors.

There were also questions about the relevance of languages with low-code platforms. Dr. Roy reminded everyone about the days when calculators were not around. “Have we forgotten how to calculate after calculators? So a low-code tool will still need you to know the methodologies, like the difference between a support vector machine and a classifier. Do you want a regression or a classifier for a problem?” he encouraged minds to ask and explore.

Be patient

Are our Data Science and Analytics the same things or different paths? It’s an evolution, as Dr. Roy clarified. “You need to solve a problem. Then, you start deep diving into where the data would come from. If it’s petabyte of data, you cannot make a model on that. First, get the data in line, clean it up, store it, etc. Multiple mathematical models are where engineers come in. The daily data work is usually 80 percent Data Engineering, 15 percent Data Analytics, and five percent is then finding the best model and deploying it. This is where a Data Scientist comes in.”

A model is a child. It has to grow. It takes time. It takes evolution. And every problem is unique. To solve a problem, nurture it as a child. It will also make mistakes. So treat the model as a child. Give it time to learn and grow, he advised.

Companies are investing in new competencies as we move ahead. Dr. Roy observed that – every organization has its problem to solve. The expectation from a Data Science professional is about gathering as much knowledge as possible in the first four years. But start diversifying across different domains. 

Data Science is the art of portraying science. Domain, technology, and process are all critical, and the art constantly evolves through creativity and the design of portraying anything.

It is the sexiest job of the century. There is no scope for short-cuts, he advised aspiring professionals.

“The world is one big data problem.” – as Andrew McAfee said. 

That sounds like a lot of challenge, but in the same vein, a lot of opportunity for the future of Data Science professionals. Hence this is the right time to develop new capabilities with Data Science and Machine Learning to stay relevant and grow in careers.

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