Data Science
August 16, 2021
There is now an overwhelming demand for industry-ready Data Science professionals who can lead digital transformation journeys. In a recent webinar, Dr. Chiranjiv Roy, Senior Vice President and Practice Head of Data Analytics at SG Analytics, shared his views on how professionals can train and tap into such opportunities.
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.
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.
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.
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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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 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.
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.
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.
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.
Driving Ahead With Data Science? Skip the Back Seat
Hold the wheel. Learn the actual scenarios, challenges and factors that play out in the real world. Data Science needs you in the front seat. Here’s how to get there. Kanchana Kumar, an alumnus of IISc and TalentSprint’s Computational Data Science Programme, shares his experience.
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.
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.
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.
So remember these guiding stars and start driving faster on the exciting lane of Data Science opportunities.
Data Science in Practice
Masterclass on ‘Data Science in Practice’ where Prof. Sashikumaar Ganesan and Prof. Deepak Subramani from the prestigious IISc, share a glimpse of the evolving domain of Data Science.
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.
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.
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.
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.
