I would not have a data science career without Kaggle. So if you are looking for a blog post bashing Kaggle, this is not the place. That said, I am not a radical that thinks Kaggle is the ultimate thing that everyone must do in order to become a data scientist. I want to give an honest opinion coming from the perspective of someone that heavily competed but decided to "retire" a few years ago. My career path is very weird, but I think it's an even more compelling case to show how spending time working on Kaggle competitions can help you. I am a law school dropout that didn't want to go back to college and decided it was a good idea to self-learn machine learning even though, at the time, almost all data science job postings required at least a Masters degree in STEM. I saw competing on Kaggle as my only shot at showing I knew what I was doing and compensating for not having academic credentials.

While only about 1% of companies are making the most of their data today, real progress is being made in democratizing the use of AI, and the future of business automation via AI is quite bright, H2O.ai's CEO and founder Sri Ambati said before a pair of H2O World conferences this week. "There's still a long way to go from where we are. It's in the earliest phases of adoption," Ambati told Datanami in an interview earlier this month. "You can see that only 1%, or less than 1%, of the world's companies can truly leverage their data. So that means 99% needs further adoption, simplification, and cultural transformation to use data and AI. It's going to take the next 10 to 20 years."

In episode seven of the NVIDIA Grandmaster Series, you'll learn from four members of the Kaggle Grandmasters of NVIDIA (KGMON) team. Watch this video to learn how they used natural language processing to analyze argumentative writing elements from students and identified key phrases in patient notes from medical licensing exams. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a 4x Kaggle grandmaster. Dr. Christof Henkel, a Ph.D. in mathematics with a focus on probability theory and stochastic processes and is a senior deep learning scientist at NVIDIA.

I would not have a data science career without Kaggle. So if you are looking for a blog post bashing Kaggle, this is not the place. That said, I am not a radical that thinks Kaggle is the ultimate thing that everyone must do in order to become a data scientist. I want to give an honest opinion coming from the perspective of someone that heavily competed but decided to "retire" a few years ago. My career path is very weird, but I think it's an even more compelling case to show how spending time working on Kaggle competitions can help you. I am a law school dropout that didn't want to go back to college and decided it was a good idea to self-learn machine learning even though, at the time, almost all data science job postings required at least a Masters degree in STEM. I saw competing on Kaggle as my only shot at showing I knew what I was doing and compensating for not having academic credentials.

Described as the Airbnb for data scientists, Kaggle is a crowdsourcing platform for aspirants to nurture, train and challenge their learnings. The search for "Kaggle" has increased by 55 percent over five years, and the platform has over 8 million users across 194 countries. While the platform trains several aspirants, it also has many established data scientists. Analytics India Magazine analysed the top 100 Kaggle grandmasters as of April 2022 to explore the top companies represented by them. Here's the latest breakdown of what users do on Kaggle.

Want to know how artificial intelligence will change business in the next decade? World-class Kaggle Grandmasters, top data professionals, and influencers across several industries are gathering to inspire you with the most exciting data science advances. At NVIDIA GTC, they'll show you how to build and optimize AI and data science solutions that are faster, more efficient, and yield better results than previous solutions. Every year, new data science techniques and tools emerge. Some practitioners build their ideas upon published papers. Others try their hand at entirely different methods based on what they learned during their thesis work or even Kaggle competitions.

You need a unique blend of technical skills, mathematical expertise, storytelling, and insight to extract meaningful commercial value from data, said Kaggle Notebooks Grandmaster Karnika Kapoor. A BTech graduate from Kurukshetra University, she believes the possibility of the scope of improvement keeps life exciting. Analytics India Magazine got in touch with Karnika to understand her Kaggle Grandmaster journey and how she made the best of the pandemic-induced lockdowns to upskill herself. Karnika: I have a background in computer-aided design and drafting and computer-aided engineering. However, in terms of learning a programming language, I started learning a bit of the C syntax in my university while pursuing Btech in mechanical engineering.

If you want to create a world-class recommendation system, follow this recipe from a global team of experts: Blend a big helping of GPU-accelerated AI with a dash of old-fashioned cleverness. The proof was in the pudding for a team from NVIDIA that won this year's ACM RecSys Challenge. The competition is a highlight of an annual gathering of more than 500 experts who present the latest research in recommendation systems, the engines that deliver personalized suggestions for everything from restaurants to real estate. At the Sept. 22-26 online event, the team will describe its dish, already available as open source code. They're also sharing lessons learned with colleagues who build NVIDIA products like RAPIDS and Merlin, so customers can enjoy the fruits of their labor.

At H2O.ai, it is our goal to democratize AI by bridging the gap between the State-of-the-Art (SOTA) in machine learning and a user-friendly, enterprise-ready platform. We have been working tirelessly to bring the SOTA from Kaggle competitions to our enterprise platform Driverless AI since its very first release. The growing list of Driverless AI features and our growing team of Kaggle Grandmasters and industry expert data scientists can be seen as our effort and commitment to achieve that goal. Today, we are excited to announce the availability of our latest Driverless AI release 1.9 which comes with tons of new features. This article is the first of the 1.9 release blog series.

The challenges in the real world get more complex and competing than an online competition. Hackathon might not paint the exact picture, and the success at these competitions should not be mistaken for expertise at the industry level. However, Kaggle, one of the world's finest platforms for data scientists, gives aspirants the best possible introduction into the tricky world of data. Analytics India Magazine has been exclusively covering the stories of top Kagglers, and today we compile a few nuggets of wisdom from those interviews that can guide an aspirant. "A right proportion of hard work, dedication, persistence, never giving up attitude and luck are the most important ingredients that helped me," said Abhishek Thakur when asked about his Kaggle success and what made him the world's first 4x grandmaster. When asked about what it takes to get to the top, Darragh, a Kaggle grandmaster, recollecting Jermey Howard, said that the best practitioners in machine learning all share one particular trait in common; they're very, very tenacious.