Last week, I was recognized and nominated as VentureBeat’s AI Rising Star, one of the AI Leadership Awards awarded annually:
This award will honor someone in the beginning stages of her AI career who has demonstrated exemplary leadership traits.
Here’s what I learned as a newcomer, leader in AI.
As a newcomer to the industry — fresh out of university and a year in the industry, I’ve been able to shift and mold very rapidly. Not long ago, I was just a Data Science Researcher in my own little lab bench corner of a Particle Accelerator Research Lab while pursuing my undergraduate studies. I shouldn’t say “just”, because data science is a lot of hard work! Little did I know that a year later, I’ll be recognized in the industry as a Leader.
Most importantly, I’ve also been able to grasp and observe the industry with a newcomer perspective. Here’s what I learned in a year of going from a data science researcher to an industry leader.
Most AI leaders today are “transcended data scientists”. They typically have pursued formal training in science, engineering or maths, and then woke up one day and decided they were more interested in leading people.
In all too common situations they find themselves in, the double mastery of technical and leadership skills was earned in series not in conjunction with each other.
They usually fall in one of two buckets:
And typically, AI companies today fall in one of two buckets:
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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.
IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.
With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.
Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.
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Data science is one of the top skills in demand as jobs in the field sees an upward trend, especially after the pandemic. As a matter of fact, many online platforms are providing online data science courses. These programs also offer certifications on their completion, making aspiring data scientists more employable.
Analytics India Magazine has collated some of the top subscription-based ed-tech platforms that provide learning data science in various formats.
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Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.
Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.
Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.
Some Of The Advantages Are Mentioned Below:-
Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.
Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.
Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.
Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.
Also Read: How Data Science Programs Become The Reason Of Your Success
Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-
Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.
Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.
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