Data Science continues to evolve as the most in-demand career today with India and the United States dominating the industry with the highest number of job openings.

An estimate by the U.S Bureau of Labor Statistics states the availability of 11.5 million jobs in the Data Science and Analytics industry by 2026. Currently, the number of job openings available in the sector in India is 1.5 lakh, a 62% rise as compared to the last year.

With the ubiquity and easy availability of data, companies and businesses, irrespective of their sizes, are turning to Data Science tools and technologies to analyze, optimize and re-strategize each aspect of their businesses for improved efficiency.

With the demand rising exponentially, a significant skill gap is also observed in the industry. Companies are finding it difficult to find the right talent for positions like Data Scientists, Data Analysts and other similar roles in Data Science and Analytics. To address this skill gap, Boston Institute of Analytics and KPMG in India have come together to bring quality education and industry exposure to students and working professionals interested to tap the emerging opportunities in this sector.

On this collaboration Ashwin Malik Meshram, Director BIA said “BIA has always stressed on ensuring an industry-focused curriculum, and this collaboration with KPMG in India is taking our vision to the next level and opening opportunities for our students to capitalize on the additional industry experience brought on by KPMG in India.”

Arihant Garg, Partner (Lighthouse – Data Analytics and Data Science Practice), KPMG in India on this collaboration said “KPMG in India and BIA’s collaboration will help the students and people with experience equally to get a complete perspective on Analytics and its implementation in Industry. We are delighted to be a part of this collaboration and are confident of its success.”

BIA in collaboration with KPMG in India is offering a course in Data Science with expert faculty and mentors from both, BIA & KPMG in India. Admissions for this course are now open. The Core module of this Data Science course has no pre-requisites and is open to everyone with the curiosity and willingness to learn.

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AI has become the need of the hour and all the industries are now integrating analytics and AI to drive the decision-making process. Bhagirath Kumar Lader, who is the  Chief Manager (Business Information System) at GAIL led us through a session briefing Artificial Intelligence essentials for business leaders in today’s age. Lader is one of the key members of the digital transformation team at GAIL and carries huge knowledge about how AI, ML and DL are crucial to businesses. He gave us a quick overview of the motivation for AI, AI essentials, AI hype vs reality while taking us through use cases. 

Motivation For AI

While AI is a crucial part of businesses, one of the key drivers of its implementation is its ability to make the decision which is usually considered the forte of humans. Integrating decision making systems in work take off the workload and AI makes it that much easier as it relies on data for its decision-making capabilities. “Where humans would have typically relied on gut feelings, the availability of data and using AI on it makes it more efficient and reliant,” said Lader. 

Taking us through the early journey of analytics where it was first used in 1880 by the US Census Bureau to today’s age where it has reduced the time drastically in every process, he spoke about how analytics journey has evolved from descriptive and diagnostic analytics to predictive and prescriptive analytics. It now looks at the current and past data to find trends and patterns to help forecast the probability of situation occurring again in the future. He also spoke about how analytics also suggests decisions, actions and implications from predictive models to improve decision-making.

Wall In Analytics

While predictive and prescriptive analytics are now widely used, Lader shared how there is a wall that comes as a hindrance when it moves from descriptive to predictive analytics. “ While Business Analytics has many domains, it is highly important to understand each one of it to be able to break this wall and move into analysing correlations, root causes, forecasting and optimisation, which essentially are a part of predictive and prescriptive analytics,” said Lader. 

How AI Differs From Humans

Once the above challenge is overcome, another crucial factor while designing AI system is to bring cognitive ability into the systems, which is nothing but the ability to do reseasoning, problem solving, planning, abstract thinking, complex idea comprehension and learning from experience — all of which are unique to humans. “Intelligence is the measure of cognitive capabilities, which if machines show can be considered showing artificial intelligence,” said Lader.

Lader addressed some of the commonly faced questions while adopting AI such as what are basic cognitive operations, what necessary conditions should a formal language fulfil in order to be an adequate tool for describing the world in s precise manner and unambiguous way, can reasoning be automated, how can we construct an AI system.

“While AI systems should be able to emulate human thinking, it should also be able to learn from experience, arrive at conclusions, understand complex real-world use cases, participate in natural-language dialogues with people, have cognition abilities among others,” he said.

He shared that AI can be created in four ways — thinking humanly, thinking rationally, acting humanly and acting rationally. 

While explaining how machines can be made to act humanly, he gave an instance of the Turing test approach, where he developed an operational test for intelligent behaviour. “This test predicted that machine might have a 30% chance of fooling a person for five minutes,” he shared. It also suggested knowledge, reasoning, language understanding and learning to be the major components of AI.

The thinking humanly approach is the cognitive science approach meaning that making machine to think like humans, for which we need to get inside the actual workings of human minds. 

Coming to the rationality part of it he said that AI system needs to have rational thinking where it knows the ‘right thing’. “While talking about autonomous vehicles we often talk about how whether it would be able to differentiate between an old woman and a tree. This is what is rational thinking which many systems currently do not pose,” he said.

Talking about the goal of AI, Lader shared that the goals are driven by two groups — expert system which should demonstrate intelligent behaviour regardless of their resemblance or non-resemblance to human intelligence and model human intelligence which is made with the aim to someday replicate or surpass human-level intelligence, which is still a distant reality. 

Machine Learning & Deep Learning

Another aspect of intelligent machines is machine learning which is a category of algorithms that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. Lader explained that for machines to learn there is a need for the generous sample for algorithms to learn.

The kind of machine learning models are:

• Supervised learning: some of the examples are linear and logistic regression, multi-class classification, neural networks, support vector machines etc. It can be classified into two categories of algorithms – classification and prediction. 
• Unsupervised learning: It is a class of ML technique to find patterns in data without any label. Some of the most common unsupervised learning methods are cluster analysis, autoencoder, GANs.
• Reinforcement learning: It is all about taking suitable action to maximise the expected reward in a particular situation. The reinforcement agent decides what to do to perform a particular task. In the absence of training dataset, it is bound to learn from experience. 

On the other hand, deep learning is a part of a broader family of ML methods based on artificial neural networks with representation learning. Some of the common DL algorithms are CNN, RNN, image recognition, among others.

“All these techniques are extremely crucial in facilitating the intelligent machines that we have today and are used to improve the performance of a system beyond that it provided by other analytics techniques”, he said on a concluding note. 

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Global technology media and purchase intent-driven marketing and sales services company TechTarget, Inc. announced the acquisition of Data Science Central LLC, an independent digital publishing and media company focused on data science and business analytics. Data Science Central provides a wide range of information on topics including AI, advanced analytics, big data and machine learning.

The data science function is a true catalyst for driving data transformation and competitive edge for modern organizations and we are committed to serving these organizations by further enhancing our extensive coverage in this market”

As an addition to TechTarget’s portfolio of market-specific media properties, Data Science Central provides rich editorial coverage, social interaction and the latest technology trends, tools and techniques to one of the largest communities of big data and data science professionals across the globe. Data Science Central generates nearly two million visits per quarter and has over 180k registered members. In addition, it has over 500k LinkedIn group members and over 370k Twitter followers.

“We’re excited to see TechTarget integrate and grow Data Science Central into its extensive network,” said Tim Matteson, Co-Founder, Data Science Central. “Working together we can transform how organizations leverage AI-driven insights for competitive gain.”

DataScienceCentral.com complements TechTarget’s existing portfolio of websites in the information management space, including SearchEnterpriseAI.com, SearchDataManagement.com and SearchBusinessAnalytics.com™. It enhances the company’s coverage in a rapidly growing area. According to Research Dive, the global Data Science Platform market will reach $224.3 billion by the end of 2026, representing a 31% CAGR.

“We anticipate significant market growth to continue in the next 1-3 years, fueled by the rapid adoption of AI, expanded availability of public cloud services, IoT and technologies to support the automation of big data processing,” said Vincent Granville, Executive Data Scientist and Co-Founder, Data Science Central.

TechTarget has been covering the AI, machine learning and data science market extensively over the past few years and has witnessed a significant uptick in traffic and member activity in the past year (e.g. 2 million global activities on data science topics in 2019). One of the key objectives to integrating Data Science Central will be to continue to nurture and strengthen this high-growth area of audience interest with quality content designed to help educate buyers in this market.

“The data science function is a true catalyst for driving data transformation and competitive edge for modern organizations and we are committed to serving these organizations by further enhancing our extensive coverage in this market,” said Eileen Corrigan, VP and Publisher, Information Management Markets, TechTarget. “This acquisition enables us to deliver the largest audience of data science professionals and serve the growing number of vendors seeking to engage with active buyers in the space.”

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December 16, 2019 –Radhica Kanniganti works for a startup company in India, home to some of the world’s most-polluted cities. She hopes to improve lives in her work as an environmental engineer for the Bangalore-based company Devic Earth, testing a device that reduces air pollution by employing radio waves.

Her job – requiring analyses of wind speeds, rainfall, temperatures, and more across India – led her to Virginia Tech’s new India-based noncredit certificate program in business analytics.

“I needed to master the tools of big data,” Kanniganti said. “Our pollution-control device, the Pure Skies 3000, reduces particulates by at least 50 percent – but that’s indoors. When you’re measuring outdoor pollution, the challenges of proof are greater, as we must account for many variables as we collect a wide spectrum of meteorological data.”

Kanniganti became one of 14 students who received a certificate in business analytics and artificial intelligence in the program’s first graduation ceremony in November in Chennai, home of Virginia Tech, India. The certificate program was designed for people like Kanniganti who lack a technical computer-programming background yet need to master the tools of big data.

“At my workplace, we were looking at massive amounts of information using spreadsheets,” Kanniganti said with a laugh. “I needed a better way to visualize and model the data so that we could demonstrate that the reduction in pollution we were seeing is the result of Pure Skies 3000. This can be harder to demonstrate when factors change, such as wind direction or rain, come into the picture, both of which have an impact on air quality.”

Cyril Clarke, executive vice president and provost, said, “Our students, faculty, and alumni in India and around the world are helping to redefine Virginia Tech as a university that is not limited by the physical and programmatic boundaries of a local campus, but rather one that is strengthened and advanced through a global commitment and perspective. It was an honor to join these students at Virginia Tech, India, and to share in their celebration. We look forward to continuing to create new programs and opportunities for our students abroad and to grow our capacity to serve a global community.”

The business analytics program – spearheaded by Guru Ghosh, vice president for Outreach and International Affairs – was developed by Tarun Sen, emeritus professor retired from the Pamplin College of Business, and Roop Mahajan, retired founding director of Virginia Tech’s Institute for Critical Technology and Applied Science and the inaugural Thapar Professor at the Thapar-Virginia Tech Center of Excellence in Emerging Materials. Ghosh said it is Virginia Tech’s first international noncredit certificate.

Program developers had students like Kanniganti in mind – working professionals juggling career demands. During the months-long course, she would hop a half-hour flight from Bangalore to Chennai to spend the weekend studying with fellow students and professors. On alternate weekends, the meetings took place online.

Kanniganti described her coursework as starting with the basics. “It started with, ‘What is data? How is it stored?’ and built from there. We learned how important it is to put our view of the available big-data sets into a business context before applying models. The professors gave us a big-picture understanding of what’s out there and what are the possibilities.”

Her student team’s capstone project employed open-source data related to Amazon’s sales of women’s clothing. The students created and tested models for making relevant buying recommendations to consumers shopping online.

The hard work paid off for Kanniganti, who is collecting data and undertaking field studies for her employer even while the amount of information she must analyze increases exponentially. “We use a lot more modeling and mechanisms to visualize the data better,” she said.

In the U.S., Kanniganti spent two years working with the U.S. Environmental Protection Agency in Chicago, overseeing permits for such projects as a $10 billion manufacturing plant in Wisconsin. Comparing the regulatory systems, Kanniganti said India’s pollution-control laws are almost equally stringent. However, implementation and enforcement are more lax. “Proving the pollution-reduction device works is half the battle,” the engineer said. “Convincing industries to invest in cleaning the air is the other half.”

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