5 data scientists share how they got in the industry — and how you can, too

5 data scientists share how they got in the industry — and how you can, too

You have a bulk of data on the internet, and someone who interprets that data into meaningful content for running up a business is known as a data scientist. Good know-how of statistics is a life of data scientist. They are the one nerdy employee who spends day and night on collecting, merging, and cleaning data. And as we all are well aware that data is never clean, data scientists should mean to find a diamond from the garbage.

Programming skills are requiring, no matter what. A data scientist is pre-assumed to have excellent programming skills on his fingertips, everything from the query, a language like R and Python, SQL, etc.

A data scientist has super math and statics skills. Because company CEOs and stakeholders are relying on you, also one must also join data science Bootcamp to handle difficulties in this field.

Of course, data scientists are employees of huge companies, so they must be dealing with massive data. These mega-companies run data-driven products (e.g., Uber and Google). A data scientist has made machine learning his favorite IT study, and this can include things like k-neighbors, random forest, methods, etc.

5 famous data scientists and their share in the industry today (800)

Whatever business is running in today’s world, they are creating unlimited data that are increasing exponentially. If you want that vast data turned into unprecedented opportunities, it will influence technology that will store data and analyze it from time to time, and then you must follow data scientists. Data scientist finds unique patterns in data and makes an increasingly accurate prediction that will maximize your future business state. Hence following the steps of a data scientist is very important.

Below is the list of 5 famous data scientists in today’s industry and their details and why one should follow their steps.

  • Dean Abbott
  • Yann can
  • Geoffrey Hinton
  • Kenneth Cukier
  • Geoffrey Hinton

1.     Dean Abbott

He is a data scientist at SmartHQ and president of Abbott Analytics (San Diego). He is famous for having an intellect in data mining and predictive analysis. He has experience of two decades about

  • fraud detection
  • risk modeling
  • text mining, personality assessment
  • survey analysis
  • intensive problem solving
  • planned giving
  • toxicology, and other applications.

Whoever wants to follow his steps should also take the above career fields seriously.

He founded algorithms for use in pattern recognition and commercial data mining, including two networks; polynomial and neural and clustering algorithm along with radial base function. He also convinces data mining software houses, to provide him critiques and assessments for present and future development.

He also conducts seminars, taught data mining tutorials to live audiences like AAAI, DAMA, KDD, and IEEE. He is an expert in explaining data mining courses to audiences like data analysts, analytics novices, statisticians, and business professionals. If you want to enter the industry, then attend these seminars and learn.

One needs to be like him and follow his steps to be a data scientist.  He is good at math and statistics; you should also excel in these to enter the data science industry because it helps to predictive models and advanced analytics solutions.


2.     Yann can

He is a French-American scientist on the computer. He primarily works in machine learning, robotics of mobile, computer vision, and computational neurosciences. Yann is also a professor at Courant Institute of Mathematical Sciences, which is located in New York, and he is famous for being vice president of Facebook.com.

He has popularly worked on several in-depth learning projects. Yann holds a degree of Ph.D. in computer science from Université Pierre et Marie Curie

Tips to follow about Yann can

  • Choose any one from; machine learning, mobile robotics, and computational neurosciences, also computer vision, and make it your career field.
  • Study doctorate degree in computer sciences.


3.     Geoffrey Hinton

He is also a computer scientist. He is a famous data scientist and famously known for his work in artificial neural networks.

He introduced the word “dark knowledge” to this world. The core purpose of in-depth knowledge was to train large models by actual information and knowledge. We will first train large models; after that, we will extract in-depth knowledge from it.

Most of the information and knowledge are in minimal probabilities that have literally zero influence on the cost function. This results in no influence on training or test performances too. Hence Geoffrey is widely known for his efforts in artificial neural networks.

He completed his study from Edinburgh University in artificial intelligence.

He is a co-inventor of “Backpropagation” he designed neural nets and deep learning algorithms.

Whoever wants to enter the data scientist industry must follow his footsteps by learning more about Deep Learning, artificial intelligence, and neural networks. Mainly he must focus on neural nets. One must also thoroughly go through his research work “how neural nets can be taught to learn without human help or teacher.”

4.     Kenneth Cukier

Everyone has heard about “The economist” Kenneth is a data editor there.  He edits data and finds usefulness out of it. He makes sure edited data must be error-free, consistent, and coherent. He is a co-author of a popular data science book named “Big Data: A Revolution That Will Transform How We Live, Work, and Think.” He believes big data is definitely better data.

He was a technology editor in the Wall Street Journal.

One who wants to imitate his data scientist career must read his writings and learn more about data scientists.

5.     Yoshua Bengio

Yoshua Bengio is a computer scientist he is most noted for his work in two fields; deep learning and artificial neural networks. He also teaches at Université de Montréal (subjects; computer science and operation research). He started his career from the Canadian Institute for Advanced Research, where he was the co-founder of brain project and machine learning. In October 2016, he co-founded  Element AI, an AI incubator. Which was used to turn AI research into a business application?

Then in 2019, he joined “Botler AI,” which was a tech startup in Montréal.

In one of his interviews, he said people should artificial intelligence and machine learning are core fields to go for if in today's era. He also said to learn more about natural language understanding.


Corinna Cortes

She is famously known as the head of Google Research in New York City. She studied masters in physics from Copenhagen University. Then, later on, she joined AT&T Labs, then worked 10+ years there as a researcher. She studied Ph.D. in computer sciences from Rochester. Corinna Cortes studied machine learning and artificial intelligence. Except for these two, she also worked in theory, general sciences, and algorithm.

She is a mother of two and hardworking women.

In her one the seminars, she shares some tips for beginners,

  • Get most out of graduation time
  • Learn many skills as you want
  • Follow experts in Machine learning and Artificial intelligence.



Some important skillset that will definitely get your pass in the data scientist industry is Programming, Statistics, Machine Learning, Linear Algebra and Calculus, Data Visualization, Communication, Data Wrangling, Software Engineering, and Data Intuition. Also, one must join data science Bootcamp to excel as a data scientist.

Few famous data scientist you can learn from are

  • Dean Abbott
  • Yann can
  • Geoffrey Hinton
  • Kenneth Cukier
  • Geoffrey Hinton

Most of them held their research work in machine learning and Artificial intelligence.

About The Author
Associate Instructor

Owais Rashidi

Owais is an associate instructor at QuickStart having prior experience of doing projects in .Net, SQL Server, SSIS, Data warehousing and Business Intelligence. He has done Bs in Enterprise Resource Planning (ERP) which is a unique blend of both Software Engineering and Business Administration. And is also configuration and implementation of SAP core modules.