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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

24 Ultimate Data Scientists To Follow in the World Today

A
Analytics Vidhya Last Updated : 11 Dec, 2015
11 min read

Introduction

Having a hero / heroine helps you navigate through the difficult times. You look up to them and then think that the problems you thought were difficult are actually trivial in nature. If people can solve and deliver at a much larger scale, you can too!

If you thought learning data science is difficult or deep neural nets is not your cup of tea – look up to the role models who created them. Following these role models provides you a daily inspiration, a motivation to find bigger purpose in life and to achieve it.

Role models set goals for you and try to make you as good as they are. Role models are important.

– Kasey Zacharias

In this article, I’ll introduce you to a league of ultimate data scientists in the world. I think these data scientists have not only done awesome work, they have all left a legacy behind the work they have done. So, here is a small intro and tribute to these role models.

ultimate data scientists to follow

These data science maestros have inspired and guided millions of candidates across the world through their freely accessible blogs, tutorials, videos etc. They are the ones’ who invented the term ‘data scientist’, ‘dark knowledge’ and unlocked the mystery behind deep learning and neural nets.

I have broadly classified these role models in three categories. I know that might sound silly – a follower classifying the role models, but I have done it in interest of providing some structure to the list. Here are the classes I have categorized people in:

  1. Research oriented Data Scientists – Data Scientists who have been or have chosen to become a Professor / researcher to focus on innovation.
  2. Data Scientists Turned Entrepreneurs – These are data scientists who have used data science to create products or services.
  3. Data Scientists in Action – Obviously, this does not mean that the above 2 are not in action. This essentially refers to the fact that these data scientists are leading the action in data science through their contributions.

I know many of you would be keen to connect / follow with these data scientists. Hence, for your convenience, I have provided the links of their respective LinkedIn / Twitter profiles.

 

Research oriented Data Scientists

Geoffrey Hinton

geoffreyYou must have heard the term ‘Back Propagation’? He is the brain (co-inventor) behind this algorithm for training neural nets and deep learning simulations. Moreover, Geoff invented the term ‘Dark Knowledge’. Its inspired by the idea that most of the knowledge is in the ratios of tiny probabilities that have virtually no influence on the cost function used for training or on the test performance. He is widely known for his work on Artificial Neural Networks. In 2013, he joined Google and led its AI team. Geoff holds a PhD in Artificial Intelligence from Edinburgh. He and his research group have been the driving force behind the resurgence of neural networks and deep learning.

 

Yann Lecun

Yann-LeCunYann is currently working at Facebook as Director of AI Research wing. He is the founding director of NYU Center for Data Science. He has worked on several deep learning projects and has 14 US patents registered. He’s also  associated with New York University as a Professor from the past 12 years. Yann holds a PhD in Computer Science from Université Pierre et Marie Curie (Paris VI). His interest area lies in Machine Learning,  Deep Learning, Computer Vision, Computational Neuroscience.

 

 

Yoshua Bengio

YoshuaYoshua is the Founder and R&D Guru of ApSTAT Technologies. He’s also associated with Université de Montréal as a Professor from past 22 years. Previously, he worked with AT&T & MIT as a machine learning researcher. His contributions to Deep Learning and Artificial Intelligence have got him world’s attention. Amongst his numerous accomplishments, he holds Canada Research Chair in Statistical Learning Algorithms, NSERC Chair and many other. He holds PhD in Computer Science from McGill University.

 

 

Jurgen Schmidhuber

JuergenSchmidhuberTriggered by an aim to build a self-improving Artificial Intelligence(AI) smarter than himself, Jurgen continual research works on RNN, Deep Learning & Computer Vision, Machine Learning is currently in use at Google, Microsoft, IBM, Baidu and many other companies. He has published 333 peer-reviewed papers, earned seven best paper/best video awards, the 2013 Helmholtz Award of the International Neural Networks Society, and the 2016 IEEE Neural Networks Pioneer Award.

 

 

Alex “Sandy” Pentland

alexFrom the past 29 years, Alex has been associated with MIT as a professor. During this period, he co-founded several companies such as IDcubed.org, Sense Networks, Cogito Health, Ginger.io and many others. Among all his achievements, he was named as the ‘World’s Most Powerful Data Scientist’ by Forbes. He is also appointed as an advisor in various MNCs such as Nissan, Motorola, HBR, Telefonica etc. His interest area lies in machine learning, artificial intelligence, human computing etc.

 

 

Peter Norvig

Peter NorvigStarted his career as a research scientist, Peter is currently appointed as the Research Director at Google.  Prior to Google. he worked for 6 years with NASA as Head of Computational Science Division. Infact, he’s been awarded with NASA Exceptional Achievement Award, ACM Fellow, AAAI Fellow and many others to mention. He holds a PhD degree in Computer Science from University of California, Berkeley. His interest area includes artificial intelligence, natural language processing, machine learning etc.

 

 

Corinna Cortes

CorinnaCortesCorinna Cortes is the Head of Google Research, NY. Corinna received her MS degree in Physics from University of Copenhagen and joined AT&T Bell Labs as a researcher. She has also worked at AT&T Labs, formerly AT&T Bell Labs for more than 10 years as a researcher. She received her PhD in Computer Science from the University of Rochester. Her research areas include Artificial Intelligence and Machine Learning, General Science and Algorithms & Theory. She is a competitive runner and mother of two.

 

 

Micheal I Jordan

Michael-JordanMichael is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at UC Berkeley. His research in recent years has focused on Bayesian non parametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in signal processing etc. He has earned several prestigious honors such as, he was named a Neyman Lecturer and a Medallion Lecturer by Institute of Mathematical Statistics (IMS).  He holds a PhD in Cognitive Science from University of California. He was a professor at MIT from 1988 to 1998.

 

 

 

Data Scientists Turned Entrepreneur

Andrew Ng

andrew ngAndrew Ng co-founded Coursera with Daphne Koller. He is currently working as a Chief Scientist at Baidu where he’s involved in carrying out researches in Deep Learning and in scalable approaches to Big Data and AI. In addition, he’s associated with Stanford University as an Associate Professor. He’s also the Founder and Lead of Google’s Deep Learning Project. Through Coursera, along with other disciplines, he made the data science knowledge accessible to all for free! Above all, he’s widely known for his Machine Learning Course.

 

 

Daphne Koller

daphnekollerDaphne is the President and Co-Founder of Coursera. She hold a PhD degree in Computer Science from Stanford. Over the years, Daphne has been honored with various awards such as ONR Young Investigator Award, ACM Infosys Award, MacArthur Foundatin Fellowship etc. She served as a Professor at Stanford University for almost 18 years. Her interest areas lies in Machine Learning, Artificial Intelligence, Pattern Recognition etc.

 

 

Hilary Mason

hilarymasonHilary is the Founder of Fast Forward Labs. She also co-founded hackNY.org and DataGotham.  Previously, she worked as a Chief Scientist at Bitly, and Assistant Professor at Johnson & Wales University. She got featured in the list of Fortune 40 under 40 ones to watch 2011, Craig’s 40 under Forty 2012. Along with many other awards, she was the recipient of TechFellow Engineering Leadership Award 2012.  Her interest areas lies in Machine Learning, Data Mining, Python.

 

 

Sebastian Thrun

sebastian-thrunSebastian is the Founder and CEO of Udacity. Previously, he founded Google[x] and worked as VP, Fellow at Google for 7 years. In addition, he’s associated with Stanford University as a Research Professor. At Udacity, he aims to democratize education so that everyone in this world can have world class education. He dreams of making this world a better place by empowering all people in the world through better education. His key areas include Machine Learning and Artificial Intelligence.

 

 

Jeff Hammerbacher

Jeff HammerbacherHe along with DJ Patil coined the term ‘Data Scientist’. Jeff is the Founder and Chief Scientist of Cloudera. Previously, he led the Data Team at Facebook. The Data team was responsible for driving many of the applications of statistics and machine learning at Facebook. In addition to his present commitments, he’s also an Assistant Professor at Mount Sinai School of Medicine. He holds a Bachelor Degree in Mathematics from Harvard University. His interest areas lies in Big Data, Machine Learning, Hadoop, Data Mining.

 

 

Jeremy Achin

Jeremy AchinJeremy is the Co-founder of Data Robot. Prior to Data Robot, he worked for 5 years as Director of Research and Modeling at Travelers Insurance. He’s performed outstandingly in Kaggle Competitions. He managed to secure rank in top 10% participants in all the competitions he has participants.  His interest areas lies in Predictive Modeling, Data Mining, Machine Learning etc. Data Robot is known for employing the best data scientist across the world. Since inception, Data Robot has emerged to become one of the fastest growing companies in US.

 

 

Carla Gentry

carlaCarla Gentry is a Data Scientist & Founder of Analytical Solution. She holds a graduate degree in Maths & Economics from University of Tennessee. She carries an invaluable experience of over 15 years which includes working for Fortune 500 companies like Hershey, Kraft, Johnson & Johnson, Kellogg’s and Firestone. She is one of the most popular personality in Big Data community to follow on Twitter. In 2013, the Information Week named her one of “10 IT Leaders to Follow on Twitter.”

 

 

 

Data Scientists in Action

DJ Patil

DJPatilDJ Patil along with Thomas.H.Davenport,  authored the popular HBR article – Data Scientist: The Sexiest Job of 21st Century”. Currently, he’s employed as US Chief Data Scientist at White House Office of Science and Technology Policy. Previously, he worked as VP of RelateIQ and Head of Data Products and Chief Scientist at Linkedin. Many patents have been filed under his name. He was elected as 2014 Young Global Leader by the World Economic Forum. He hold a PhD in applied mathematics from University of Maryland.

 

 

Adam Coatesadam coates

Adam holds PhD in Computer Science from Stanford University. Currently, he’s appointed as the Senior Director at Baidu Silicon Valley AI Lab. His interest areas lies in Machine Learning, Deep Learning, Control & Robotics.

 

 

Monica Rogati

Monica rogatiMonica is currently working as Data Science Advisor with Insight Data Science. Previously, she worked with Linkedin as Senior Data Scientist, and was Vice President of Jaw Bone and held many more responsible positions. Her passion lies in turning data into products, actionable insights and meaningful stories. Her interest areas lies in Machine Learning, Statistical Text Mining, Recommender Systems etc. She holds a PhD in Computer Science from Carnegie Mellon University.

 

 

Oliver Grisel

oliver griselHave you ever used Scikit-learn ? He’s one the brains behind contributing to Scikit-Learn. Oliver is currently working as a Software Engineer with Inria Parietal to work on improving scikit-learn and related tools. He holds a MSc in Advanced Computing from Imperial College of London. He’s specialized in machine learning with applications to natural language processing and knowledge extraction. He’s also known to be involved in delivering talks and tutorial sessions on scikit-learn and predictive modelling.

 

 

Owen Zhang

Owen ZhangCurrently, working as Chief Product Office at Data Robot, Owen is the current World Rank 1 at Kaggle. He proved his mettle by winning and emerging as runner up in several kaggle competitions. Prior to Data Robot, he was serving AIG as the Vice President, Science and Sr. Director, Analytics and Research at Travelers Insurance. He hold a master’s degree from University of Toronto. His interest area lies in Predictive Modeling, Data Mining.

 

 

Sergey Yurgenson

Sergey YurgensonSergey is currently working as a Data Scientist at Data Robot. Began as a physicist, Sergey realized his love for analytics later down the years. And then, there was no looking back. He emerged to be one of the Top 10 Data Scientist in 2012. Currently, ranked at 16th, Sergey has won several kaggle competitions. He loves to solve challenging problems that require innovation and unconventional solutions. Prior to Data Robot, he worked for 13 years as a Research Associate at Harvard Medical School. He holds a PhD in Physics from St. Petersburg State University

 

 

Stanislav Semenov

Stanislav SemenovCurrently, the World Rank 3 in Kaggle, Stanislav has won various competitions including Otto Group Product Classification Challenge. Currently, he works as a Data Scientist Consultant. In addition, he is engaged as a Professor at Yandex School of Data Analysis. He holds a master’s degree in applied mathematics and informatics from National Research University (Russia).

 

 

Gilberto Titericz Jr.

Gilberti JrThis Electronics Engineer turned Data Scientist is currently positioned at World Rank 2 in Machine Learning and Data Mining Competitions hosted by Kaggle. Currently, he is employed as Automation Engineer at Petrobras in Brazil. Prior to this, he worked at big multinationals like Siemens and Nokia. After devoting 8 years in electronics, Gilberto discovered his love for data science in 2008 and since then, he has never looked back.

 

 

Kirk Borne

Kirk BorneKirk is appointed as a Principal Data Scientist at Booz Allen Hamilton. Not only he’s a data scientist, but also he’s an Astrophysicist and Space Scientist. He’s named as 2014 IBM Big Data & Analytics Hero. He’s also got featured in a Ted Talk named ‘Big Data, Small World’. In addition to Booz Allen, he’s associated with many companies as their advisory board member. Kirk holds a PhD degree in Astronomy from California Institute of Technology

 

 

Doug Cutting

dougDoug is the daddy of Hadoop. He’s the reason behind Apache Lucene, Nutch, Hadoop and Avro open source projects into existence. Currently, he’s working as Chief Architect with Cloudera. Prior to joining Cloudera, he worked with several MNCs like Apple, Yahoo etc. From the past 14 years, he’s associated with Apache Software Foundation. He holds a bachelor degree from Stanford University.

 

 

End Notes

By now, I am sure you would also have noticed a common trait among all these data scientist. All these people are either driven by a vision or their entrenched love for data science. So if data science is your love, I would suggest to follow these role models closely and you’ll be on your path to greatness!

In this article, I’ve listed the best data scientist across the world. These people are masters in their own discipline and avidly seek opportunities to spread data science knowledge around the world. If you think, I’ve missed out on any, please feel free to share in the comments section below with your views.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page

 

 

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Responses From Readers

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Julius Mkumbo
Julius Mkumbo

Where is Michael L. Jordan of machine learning in the category of Research oriented Data Scientists?

123

Analytics Vidhya Content Team
Analytics Vidhya Content Team

Thanks for your suggestion. Michael has been added to this list !

123
Prithvi
Prithvi

You missed the pioneer DJ Patio..... He invented the term called data science...

123

Analytics Vidhya Content Team
Analytics Vidhya Content Team

Hi Prithvi DJ Patil is already there.

123
Lev Kuandykov
Lev Kuandykov

In Europe, you missed Scandinavian Institute of Business Analytics SCANBA, its alumni and founders

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