Data science is all around us these days from academia to major industries and tech giants, the demand has been growing exponentially. So, what is data science all about anyway? And who are the data scientists to follow online for expertise in this fast-growing field? Here is a list of the leading thinkers and doers who are most responsible for popularizing the field today. Without them, the tech giants that dominate our digital world—Google, Amazon, and Twitter, to name a few—would be nowhere near the progress they’ve made thus far in AI and machine learning.
“Machine learning is an approach to automating repeated decisions that involves algorithmically finding patterns in data and using these to make recipes that deal correctly with brand new data.”
Talking about Data Science the first name that came to my mind is Cassie and I am a big fan of her Medium articles. Cassie is a technology evangelist and has been called a data science thought leader. She has been a keynote speaker at large conferences, including Web Summit, the world's largest technology event. She publishes data science articles on her blog, and her writing has also been featured on Harvard Business Review and Forbes. She was selected by LinkedIn as the #1 Top Voice in Data Science and Analytics for 2019 and appeared on the cover of the Forbes AI data science issue.
Her research involves the neural processing of value and economic preferences. After completing her graduate studies in the decision sciences, Kozyrkov studied data science but was recruited by Google before she completed her PhD in mathematical statistics. She began her studies in economics and mathematical statistics at Nelson Mandela University at the age of fifteen and transferred to the University of Chicago to complete her undergraduate degree. After graduation, Kozyrkov worked as a project manager and researcher at the University of Chicago Center for Cognitive and Social Neuroscience. Later, she enrolled in graduate studies at Duke University in psychology and cognitive neuroscience with a focus on neuroeconomics.
She founded the field of Decision Intelligence at Google, where she serves as Chief Decision Scientist.
Sebastian Thrun, founder of Google X, a research project intended to investigate far-off technologies and possibilities. Google X is a top-secret experimental laboratory situated at Google’s Mountain View HQ. It was conceived around 2010, emerging from a single ‘moon shot’ idea that we’ve since become quite familiar with – the driverless car, Google glass. Currently, Thrun works as a researcher at Stanford University and is the founder of Udacity. He is running several free courses such as Intro to Machine Learning, Artificial Intelligence and various other data related fields.
|May 14, 1967
|University of Bonn
University of Hildesheim
|National Science Foundation CAREER Award(2003)
DARPA Grand Challenge(2005)
|Google X Lab(founder)
Carnegie Mellon University
|Explanation-Based Neural Network Learning: A Lifelong Learning Approach
|Armin B. Cremers
Andrew Ng is among the most prolific specialists in AI and machine learning. Also, he co-founded and led the Google Brain project and was the vice president and chief scientist at Baidu, leading its AI group. He is also a pioneer in education, co-founding the popular Coursera platform. He is commonly cited as one of the major catalysts for the recent revolution in the field of deep learning. From 2011 to 2012, he worked at Google, where he founded and directed the Google Brain Deep Learning Project with Jeff Dean, Greg Corrado, and Rajat Monga.
Google Brain as the name suggests is meant to replicate the Human Brain. It is an Artificial Intelligence system based on open learning which captures all the headlines across the world. They tried to conduct a basic simulation of human communication between three AIs: Alice, Bob and Eve. The purpose was to have Alice and Bob communicate effectively – without Bob misreading Alice’s messages and without Eve intercepting them or with Bob and Alice carrying out proper encryption and decryption, on their respective parts. The study showed that for every round where the 3 AI's failed to communicate properly, the next round showed a significant improvement in the cryptographic abilities of the two AIs.
In 2014, he joined Baidu as Chief Scientist, and carried out research related to big data and A.I. There he set up several research teams for things like facial recognition and Melody, an AI chatbot for healthcare (like Siri or Amazon's Alexa). In March 2017, he announced his resignation from Baidu.
In January 2018, Ng unveiled the AI Fund, raising $175 million to invest in new startups the education startup Deeplearning.ai, the AI Fund startup studio for building AI companies and Landing.ai, which helps enterprises (and especially manufacturing companies) use AI.
April 18, 1976
|University of California, Berkeley(PhD)
Massachusetts Institute of Technology(MS)
Carnegie Mellon University(BS)
Raffles Institution(High School)
|Artificial Intelligence,Deep Learning,MOOC,Education technology
|Carol E. Reiley
|2007Sloan Fellowship, 2015. World Economics Forum Young Global Leaders
|Artificial intelligence,machine learning,natural language processing,computer vision
|Shaping and Policy Search in Reinforcement Learning
|Michael I. Jordan
Intelligence about baseball statistics had become equated in the public mind with the ability to recite arcane baseball stats. What [Bill] James's wider audience had failed to understand was that the statistics were beside the point. The point was understanding; the point was to make life on earth just a bit more intelligible; and that point, somehow, had been lost. 'I wonder,' James wrote, 'if we haven't become so numbed by all these numbers that we are no longer capable of truly assimilating any knowledge which might result from them.'-Dean Abbott
Dean Abbot is the co-founder of Smarter HQ, a firm specializing in personalized AI, where he is the chief data scientist. Mr Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world data-intensive problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology. Mr. Abbott is also Co-Founder and Chief Scientist of Smarter Remarketer, a startup company focusing on behaviorally- and data-driven marketing attribution and web analytics. He has authored many books on data science, including “Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst .”If you are interested in reading more, his blog has an archive of much of his work.
His LinkedIn says a lot about him, he mentioned "Applying data mining, data preparation, and data visualization methods to business and research problems since 1987. Data mining course instructor for a wide variety of audiences, including analytics novices, seasoned analytics professionals, statisticians, and academics."
He specializes in Fraud Detection (tax compliance/avoidance, invoice fraud, credit card fraud), Association rules for automatic business rule discovery, Customer Relationship Management (inbound/outbound response modeling, customer segmentation, survey analysis, lifetime planned giving) in insurance, non-profit, and banking industries. Non-profit donation models; lifetime planned giving models. Understanding and explanation of algorithms--linear regression, logistic regression, neural networks, GMDH, decision trees.
Kirk Borne is a data scientist who is considered to be one of the most influential people in the space today. Recently called the “#1 digital influencer” for data science by the IPFC web agency, he specializes in AI and big data. Speaking at conferences around the world, he is also well-versed in the field of astro-informatics. He has previously worked at NASA and with the Hubble Space Telescope data team.
His writings on Medium are making complex Data Science and Deep Learning concepts easier to understand. He is creative enough to visualize the data science concept in a way it seems like a story.
I think of 'data science' as a flag that was planted at the intersection of several different disciplines that have not always existed in the same place. Statistics, computer science, domain expertise, and what I usually call 'hacking,' though I don't mean the 'evil' kind of hacking. -Hilary Mason.
She has introduced herself in one of her blogs as :
Hi, I’m Hilary.
Simply: I make beautiful things with data.
I’m the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel. Previously, I was the Chief Scientist at bitly. I also co-founded of HackNY, co-host DataGotham, and am a member of NYCResistor.
I believe technology should give us superpowers.
At Fast Forward Labs, we are building those superpowers.
I’m a Data Scientist in Residence at Accel, where I get the chance to advise companies large and small on their data strategy.
I spent four years as Chief Scientist at bitly, where I led an amazing team that studied attention on the internet in realtime, doing a mix of research, exploration, and engineering.
I co-founded HackNY, a non-profit that helps talented engineering students find their way into the startup community of creative technologists in New York City.
I’m an enthusiastic member of the larger conspiracy to evolve the emerging discipline of data science.
I’m a native New Yorker and I love this city and the technology community here.
I am an advisor to a few organizations that I adore, including Mortar, knod.es, collective[i], and Data Kind. I’m a mentor to Beta spring, the Providence, Rhode Island-based startup accelerator, and the Harvard Business School Digital Initiative.
I was a member of Mayor Bloomberg’s Technology and Innovation Advisory Council, which was a fascinating way to learn how government and industry can work together.
I’ve received a few honors, like the Tech Fellows Engineering Leadership award, and was on the Forbes 40 under 40 Ones to Watch list and Crain’s New York 40 under Forty list. I’ve also been in Glamour, the WSJ, Fast Company, Scientific American, and more, which has made my mother very happy.
I like to give talks, and have spoken about how to replace yourself with a very small shell script, on e-mail hacking, machine learning: a love story, and more.
I also keep a fun mostly photo-oriented blog of mostly New York adventures at hilary.nyc
Mason received the "Tech Fellows Engineering Leadership award" in 2012 worth $100,000. She was also on the Fortune 40 under 40 Ones to Watch list in 2011, as well as Crain's New York 40 under Forty list.
She was named within the Top 100 most creative people in business by Fast Company.
[Technology Evangelist, Author, Engineer, Rocket Scientist, and Global Subject Matter Expert, Information Governance, Analytics and eDiscovery]
Chris Surdak is a self-proclaimed “Big data guy.” A writer and literal rocket scientist, he is an expert in technology strategy and (not surprisingly) big data. He currently operates his own consulting business and writes books, but he previously worked at companies like HP, Dell, and Citibank. Mr. Surdak began his career with Lockheed Martin Astro-space, where he was a spacecraft systems engineer and rocket scientist. His focus has always been on how to best leverage the potential of the digital economy.
Mr. Christopher is author of "Data Crush: How the Information Tidal Wave is Driving New Business Opportunities", published by AMACOM Publishing.
Mr. Surdak holds a Juris Doctor from Taft University, an Executive Masters In Technology Management and a Moore Fellowship from the University of Pennsylvania and a BS in Mechanical Engineering from Pennsylvania State University. He also holds a CISSP Master’s Certificate from Villanova University. With a law degree and MS from Wharton Business School, Surdak applies his broad interests across a variety of industries that are exploring big data and AI.
Fei Fei Li is a professor of Computer Science at Stanford. She teaches the Stanford course CS231n on "Convolutional Neural Networks for Visual Recognition", whose 2015 version was previously online at Coursera. She has also taught CS131, an introductory class on computer vision. Also co-director of Stanford's Human-Centered AI Institute, Li is one of the pioneers in AI, machine learning, and cognitive neuroscience. She is a prolific writer and researcher, having published about 180 peer-reviewed papers. In 2007, as an assistant professor at Princeton University, she led a team of researchers to create the ImageNet project, a massive visual database to be used with software that recognizes visual objects. That work influenced the "deep learning" revolution over the next decade. While serving as director of the Stanford Artificial Intelligence Lab (SAIL) from 2013 to 2018, she co-founded the nonprofit AI4ALL, which strives to increase diversity and inclusion in the field of AI.
Li has been described as an "AI pioneer" and a "researcher bringing humanity to AI".
Li was elected as a member of the American Academy of Arts and Sciences in 2021, the National Academy of Engineering, and the National Academy of Medicine in 2020.
In May 2020, Li joined the board of directors of Twitter as an independent Director.
Li contributed one chapter to Architects of Intelligence: "The Truth About AI from the People Building it" (2018) by the American futurist Martin Ford.
Wes McKinney is an American software developer and businessman. McKinney is the founder of the data library Pandas, intended for the Python coding language. The author of books on his library and on Python more broadly, McKinney is a staple at many data conferences around the globe. His twitter may be hard to follow if you’re not a coding whiz, but his insight is significant. He is also the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language. Moreover, he has authored two versions of the reference book Python for Data Analysis. He was the CEO and founder of technology startup Datapad, software engineer at Two Sigma Investments and also founded Ursa Labs.
Datapad is a Python library for processing sequence and stream data using a Fluent style API. Data scientists and researchers use it as a lightweight toolset to efficiently explore datasets and to massage data for modeling tasks. It can be viewed as a combination of syntactic sugar for the Python itertools module and supercharged tooling for working with Structured Sequence data.
Ursa Labs is an industry-funded development group specializing in open source data science tools. It is dedicated to advancing the state of the art in high-productivity, high-performance, cross-language software for data scientists. Ursa Labs is now Voltron Labs, part of Voltron Data. They opened with the objective of helping make Apache Arrow a robust and reliable next-generation computational foundation for in-memory analytics and data science
There you have it. These were some of the celebrities of the Data Science world. Their careers are an inspiration to all of us.