# Top 8 Data Science Innovators & Inventors in History (2020)

In case you thought learning data science is difficult or maybe machine learning is mind boggling or even deep neural networks aren’t your cup of tea — refer to the inventors and innovators that molded an industry. Following these good names in the field of Data Science can inspire you the same way it has inspired the the creation of the World Data Science Institute.

Having a role model in the field of Data Science greatly increases your chances of success in a field that is much more than the promoted Data cleansing.

You have to admire the people that came before and their contributions to an industry that is just beginning to flourish into it’s magnificent possibilities. If individuals from humble beginnings can become innovators and industry leaders, you can as well. The first step to becoming an innovator and industry leader yourself, is to study those before you!

**⦁Katherine Johnson — African American Mathematician (Born 1918)**

She was so Famous they made a movie after her** “Hidden Figures” (starring Taraji P. Henson, Octavia Spencer, and Janelle Monae)**. She is famously known and highly respected for her Mind! Her nickname was “The

*Human Computer”*.

**Katherine Johnson was the original Data Scientist!** She spent years working with Big Data in the field of Aeronautics with Nasa performing High Level Data Analysis. **According to NASA**, Katherine specifically performed “mathematical calculations that transformed raw data that had been obtained using instrumentation into final engineering parameters.”

Katherine is literally the person that had helped the United States land on the moon. According to NASA, she is credited with literally **creating the blueprint of Rocket Science**!

Katherine inspired a nation by being the best possible Scientist she could be. In 2015, she was awarded with the “Presidential Medal of Honor” for her accomplishments in STEM (Science, Technology, Engineering, and Mathematics).

**⦁Elbert Frank Cox — African American Mathematician (Born 1895)**

The first African American in United States History to receive a PHD in Mathematics (from Cornell University).

He is a major figure in the African American community known for his incredible teaching skills. He eventually became **Department Chair at the most prestigious African American University in the World “Howard University” **(ranked as the #1 Historically Black College University).

His ability to explain complex mathematical processes in a exciting way caused a stampede of African Americans to pursue careers in the field of Science and Technology.

Elbert beat incredible odds as an African American man becoming an invited member to the American Math Society and the Mathematical Association of America during a time when segregation was at it’s peak.

**Deep Learning and Machine Learning concepts that are used now are based on some of the subjects Elbert specialized in and published writings about**; such as predictive analysis utilizing difference equations. He is also known for **his contributions of interpolation methods that are currently the basis for Artificial Intelligence**.

**⦁ Thomas Bayes — English Statistician (Born 1701)**

We are sure every Data Scientist have heard of **one of Machine Learning’s most accomplished algorithms “Naive Bayes”**. This guy was so famous he has his own algorithm.

While current Bayesian statistics is a lot more exaggerated than a simple application of Bayes’ theorems, we can trace it back to the brilliant Minister and Statistician Thomas Bayes.

To get a good understanding of Bayes’ theorems, we recommend learning and practicing with the chain rule of conditional probability.** More than 300 years later, his theory is directly linked to Machine Learning applications **(Image Classification, Email Filtering, Loan Approval, and many more).

**⦁ Geoffrey Hinton — British Canadian Computer Scientist (Born 1947)**

If you know anything about Deep Learning, you will have heard the term **‘Back Propagation’ **(or Feedback Network). He is generally known for his work on Artificial Neural Networks. He and his research team have been the main impetus behind the revival of neural networks and deep learning originally introduced in the 1960s by **Henry J. Kelley**.

Hinton can be credited with **creating and innovating several neural networks (Boltzmann machines, distributed representations, time-delay neural nets) that are heavily used in deep learning simulations**.

Hinton is a definitive genius and has dedicated his life to learning as much as he can in the field of Artificial Intelligence.

⦁ **Ronald Fisher — British Statistician (Born 1890)**

He is highly credited with improving upon and creating several processes in relation to Probability Estimation & Statistics. **Most famously he is known for the invention of the analysis of variance (usually known as ANOVA)**. A procedure used to test and analyze means of data samples (most commonly used in Hypothesis testing).

His concepts are being used right now in the practice of Scientists all over the world to come up with a vaccine for the Coronavirus. **The application of testing comparing the victims and the variables that determine life or death is the concept behind his invention of the analysis of variance**.

For these significant contributions, he has been profoundly respected throughout the history of modern statistics. He also is widely regarded as creating the foundation of population genetics and also crowned with the title as the **“Father of Business Statistics”**.

⦁ **Carl Friedrich Gauss — German Mathematician (Born 1777)**

A brilliant Mathematician and Physicist, Gauss was productive to the point that he regarded his **invention of Statistical Regression**, a central tool in present-day statistics and data science, too trivial to even report when he discovered it. In particular, **Gauss designed least squares regression or what we call today Linear Regression**, a technique used to calculate a straight line, as well as outliers that best fit numerical data.

In the Modern days, it is fundamentally **utilized for understanding the connection between variables in Supervised Learning in predictive analytics **(real estate prices, stock prices, scores, betting, and etc).

For instance, you could use regression to measure how Car Price (Y dependent variable) is affected when considering one or more (X independent) variables (such as mpg, engine size, and etc).

Gauss is highly respected for his contributions within statistics, algebra, geometry, mechanics, astronomy, Chemistry, and Physics.

**⦁ Andrey Markov — Russian Mathematician (Born 1856)**

Markov is stellar in his contribution to Machine Learning related processes, specifically Reinforcement Learning. **Nearly 20 years ago his contributions are appreciated in present day services like GPS, Google Maps, Robotics, and popular Natural Language Processing applications** (Alexa, Siri, and etc).

His ideals are heavily applied in processes that deal with probability, data modeling, and decision making within Deep Learning processes.

The way search results are weighted, the response on Google translations, and even the answer **Alexa all directly related to Markov Chains**. If I listed all of its current use cases I would still be typing!

The processes behind **Markov’s brilliance are vital in Artificial Intelligence and Machine Learning to the this very day**.

**Kunihiko Fukushima — Japanese Computer Scientist (Born 1936)**

Credited with becoming the **first person to successfully use a Convolutional Neural Network**. Convolutional Networks are a type of Deep Learning architecture used heavily for precise Image Classificaion, Object Recognition, and Video Analysis.

One of **his proudest moments was creating a Convolutional Neural Network that allowed a computer to successfully identify visual patterns**. This is considered High Level Artificial Intelligence. Think of this as a robot that can differentiate a It doesn’t get more complex than what this Genius has created.

The complexity of **his model called “Neocognitron” built using a Convolutional Neural Network has paved the way for several Robotic Security products**.