Go Programming

Go Programming

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What I Learned as a Data Science Researcher turned AI Leader in a Year

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.

AI requires a completely different perspective

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:

  1. A leader who starts to lose the technical side of things and can only talk about products at a high-level
  2. A leader who is so entrenched in the technology and usually gets too into the weeds of the development

And typically, AI companies today fall in one of two buckets:

  1. A company that pursues abstraction of technology and focuses on the real-world problem at hand (i.e., domain experts)
  2. A company that gets too in the weeds of technology and loses sight of the real-world problem they were trying to solve in the first place (i.e., technology providers)

#leadership #ai #editors-pick #artificial-intelligence #data-science

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What I Learned as a Data Science Researcher turned AI Leader in a Year
Uriah  Dietrich

Uriah Dietrich

1618449987

How To Build A Data Science Career In 2021

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.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

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.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

'Commoditization Is The Biggest Problem In Data Science Education'

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.

#people #data science aspirants #data science course director interview #data science courses #data science education #data science education market #data science interview

Jackson  Crist

Jackson Crist

1618235820

Top 7 Subscription-based Ed-tech Platforms For Data Science

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.

#adasci #association of data scientiists #data science #datacamp #ed tech platforms #learn deep learning #learn machine learning #python #start a career in ai #start a career in data science #top websites to learn ai

Matteo  Renner

Matteo Renner

1618137720

Top 10 Programmers To Follow On Youtube

Youtube has become a major source of information and an educational platform for many. It has an array of lessons and tutorials to learn any subject, including topics in computer science. Unlike subscription-based ed-tech models, most of the content on it is free.

Programmers, enthusiastic about teaching data science, artificial intelligence, machine learning, and deep learning are also very active on the platform and have well-established channels with videos right from the basics of programming to complex subjects of the field.

We enlisted the top programmers teaching these subjects on YouTube. Choosing the right one will depend on your experience in the field and the kind of projects you want to work on.

#opinions #ai programmers #ai tutorials on youtube #data science tutorials on youtube #deep learning #learning ai #learning data science #machine learning #online data science classes #youtube