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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

Top 12 Data Science Leaders to Follow in 2025

Pankaj Singh
Pankaj Singh Last Updated : 09 Dec, 2024
10 min read

Introduction

Data science has been a hot topic for a few years now, captivating the minds of techies and playing a significant role in the growth and development of businesses. As we step into the automation-centric future, the potential of data science is boundless, and the leaders in the field will be the torchbearers of future innovation. Join us as we cast a spotlight on some of the greatest leaders in data science, celebrating their exceptional expertise, visionary leadership, and substantial contributions within the field. This blog lists the top 12 data science leaders to follow for the latest updates and innovations in the field.

Table of contents

Top 12 Data Science Leaders to Watch in 2024

As we edge closer to 2024, here is a list of distinctive individuals who have showcased remarkable expertise, leadership, and noteworthy contributions in the field of data science. This list aims to acknowledge these individuals as data science leaders to follow in 2024.

Whether it entails progress in predictive analytics, advocacy for ethical AI practices, or developing cutting-edge algorithms, the individuals highlighted in this list are poised to influence the terrain of data science in the coming year.

Andrew Ng

“A lot of the game of AI today is finding the appropriate business context to fit it in. I love technology. It opens up lots of opportunities. But in the end, technology needs to be contextualized and fit into a business use case.”

Dr. Andrew Ng is a British-American computer scientist with Machine Learning (ML) and Artificial Intelligence (AI) expertise. He is the Founder of DeepLearning.AI, the Founder & CEO of Landing AI, a General Partner at the AI Fund, and an Adjunct Professor at Stanford University’s Computer Science Department. He also served as a Chief Scientist at Baidu, where he mentored a 1300-person AI group and developed the company’s AI global strategy. Moreover, he was the founding lead of Google Brain – the deep learning artificial intelligence research team under Google’s AI umbrella.

Andrew Ng is also actively involved in the field of education. He co-founded Coursera and offered Machine Learning (ML) courses to over 100,000 students. He also led the development of MOOC (Massive Open Online Courses) at Stanford University. Being a pioneer in ML and online education, he holds degrees from Carnegie Mellon University, MIT, and the University of California, Berkeley. Moreover, he co-authored over 200 research papers in ML, robotics, and related fields. Acknowledging all of his contributions to the field, Time Magazine listed him as one of the 100 most influential people in AI in 2023.

Website | Twitter

Andrej Karpathy

andrej karpathy, data science leaders

“We were supposed to make AI do all the work, and we play games, but we do all the work, and the AI is playing games!“

Andrej Karpathy, a Slovak-Canadian PhD holder from Stanford, is building a kind of JARVIS at OреոΑӏ. He was the Director of AI and Autopilot Vision at Tesla. Karpathy is most passionate about deep neural nets. He started his journey from Toronto with a double major in Computer Science and Physics, after which, he went to Columbia for further studies. There, he worked with Michiel van de Panne on learning controllers for physically simulated figures.

He also worked with Fei-Fei Li during his Ph.D. at Stanford Vision Lab, where he worked on the Convolutional Neural Network and Recurrent Neural Network architectures and their applications in Natural Language Processing and Computer Vision. He designed the course CS 231n: Convolutional Neural Networks for Visual Recognition and was its first primary instructor. Moreover, he is an enthusiastic blogger, a developer of deep learning libraries, and a passionate Data Science expert.

Website | Twitter

Amina Anandkumar

Amina Anandkumar is an India-born Bren professor at Caltech and serves as a Senior Director of AI Research at NVIDIA. She was previously a Principal Scientist at Amazon Web Services as well. Anandkumar holds degrees from the Indian Institute of Technology (IIT) Madras and Cornell University and is a fellow of ACM, IEEE, and the Alfred P. Solan Foundation.

She is an influencer with over 150k followers and shares updates on large-scale machine learning, non-convex optimization, and high-dimensional statistics. Her work in developing novel artificial intelligence has accelerated AI’s scientific applications, including scientific simulations, weather forecasting, and drug design. Her HPC-based COVID-19 Research was recognized at NeurIPS and was awarded the ACM Gordon Bell Special Prize.

Website | Twitter

Fei-Fei Li

“I believe in the future of AI changing the world. The question is, who is changing AI? It is really important to bring diverse groups of students and future leaders into the development of AI.” 

Fei-Fei Li is a Co-director of Human-Centered Artificial Intelligence and the Vision & Learning Lab at Stanford Institute. She is the inaugural Sequoia professor in their computer science department as well. She has also worked as Vice President at Google and Chief Scientist of AI/ML at Google Cloud. Over her years of experience, Li has worked in diverse areas ranging from cognitively inspired AI, and deep learning to machine learning, computer vision, and AI in healthcare.

Talking about her research, she has published over 200 scientific articles at conferences and significant journals in the relevant fields. ImageNet, developed by Fei-Fei Li, is a revolutionary project in the latest frontiers of artificial intelligence and deep learning.

She has received many awards for her work, including the ELLE Magazine’s 2017 Women in Tech, a Global Thinker of 2015 by Foreign Policy, and the prestigious “Great Immigrants: The Pride of America” by Carnegie Foundation in 2016. As an industry leader, she also stands as a flag bearer for diversity in AI and STEM at the national level.

Stanford Profile | Twitter

Yann LeCun

“AI is an amplifier of human intelligence & when people are smarter, better things happen: people are more productive, happier & the economy strives.”

With expertise in research, technical consulting, and scientific advising, Yann LeCun is the Chief AI Scientist at Facebook. He is the Founding Director of the NYU Center of Data Science and headed the Image Processing Research department. He is known globally for his work in mobile robotics, machine learning, computer vision, and computational neuroscience.

LeCun founded convolutional nets and contributed to OCR and computer vision projects using convolutional neural networks. These biologically inspired networks were applied to optical and handwriting recognition, creating a bank check recognition system. This system was adopted by NCR and other companies and processed 10% of all U.S. checks in the late 1990s and early 2000s.

LeCun is one of the primary creators of DjVu and received the Turing Award in 2018 along with Yoshua Bengio and Geoffrey Hinton for their contributions to deep learning.

Website | Twitter

Ian Goodfellow

“Even today’s networks, which we consider quite large from a computational systems point of view, are smaller than the nervous system of even relatively primitive vertebrate animals like frogs.”

Ian Goodfellow is an American computer scientist, best known for his research work in Machine Learning. He serves as a Director of Machine Learning at Apple. Under the supervision of Andrew Ng, he recieved a B.S. and M.S. in Computer Science from Stanford University. He also got a Ph.D. from Université de Montréal under the supervision of Yoshua Bengio and Aaron Courville.

Ian Goodfellow initially worked as a Research Scientist at Google Brain. After that, he joined Open AI in their initial years, and then returned to Google research. While at Google, he created a system facilitating the automatic transcription of addresses from Street View car photos for Google Maps. Additionally, he exposed vulnerabilities in machine learning systems.

Goodfellow has also researched and written the textbook “Deep Learning,” which gained prominence for inventing generative adversarial networks (GAN). In 2017, the MIT Technology Review recognized him among the 35 Innovators Under 35, and in 2019, Foreign Policy included him in their list of 100 Global Thinkers.

Website | Twitter

Clément Delangue

data science leader

Clement Delangue is the CEO and Co-founder of Hugging Face, an open-source machine learning platform where researchers worldwide share their AI models, datasets, and best practices. With his expertise in Machine Learning, Hugging Face raised $160 M from Sequoia, Betaworks, Salesforce, Kevin Durant, and other industry leaders.

He started off as the Co-Founder & CEO of VideoNot.es, a leading note-taking platform for the digital age. He then built a marketing and growth department for Mention – a leading European startup in 2014. His first startup experience was with Moodstocks, where he worked on building machine learning models for computer vision, which was later acquired by Google.

Talking about his academic background, he studies “Introduction to Computer Science and Programming Methodology” at Stanford University. Now, with over 120k followers on LinkedIn, he is definitely a voice in data science worth listening to.

Twitter

Jay Alammar

With years of experience and research interest in ML, NLP, AI, and software, Jay Alammar is the Director and Engineering Fellow of Cohere. He started as a Partner in Machine Learning Engineering and helps developers solve business problems with cutting-edge AI & NLP models. Now, he advises enterprises and developers on using large language models to solve real-world language processing use cases.

Alammar holds a degree in “Executive Education, Influence, and Negotiation Strategies” from Stanford, and has gone on to assist over 10,000 learners on complex machine-learning topics. He also runs an English tech blog website for Machine Learning R&D, where he publishes all about NLP, ML, and AI. So, if you are looking for a data science leader to follow, Jay Alammar is one of your best bets.

Website | Twitter

Sam Altman

“AI will probably most likely lead to the end of the world, but in the meantime, there’ll be great companies.”

Sam Altman is a Co-founder of Apollo Projects and has been the CEO of OpenAI since 2019. He attended Stanford University but dropped out after two years. In 2005, at 19, Altman co-founded Loopt, a location-based social networking app, securing over $30 million in venture capital as CEO. Despite the acquisition by Green Dot for $43.4 million in 2012, Loopt struggled.

Altman then joined Y Combinator in 2011, becoming its president in 2014, overseeing a total valuation of $65 billion for companies like Airbnb and Dropbox. In 2016, he expanded his role to include YC Group. Altman initiated YC Continuity and YC Research, funding mature companies and a research lab. In 2019, he transitioned to Chairman at YC, later focusing on Tools For Humanity, a 2019 venture providing eye-scanning authentication and Worldcoin cryptocurrency for fraud prevention.

Website | Twitter

Yoshua Bengio

“AI will allow for much more personalized medicine.”

Renowned globally for his expertise in artificial intelligence, Yoshua Bengio is a trailblazer in deep learning, honored with the prestigious Turing Award in 2018, alongside Geoffrey Hinton and Yann LeCun. Bengio is a Senior Fellow in the CIFAR Learning in Machines & Brains program and the Scientific Director of IVADO. Serving as a Full Professor at Université de Montréal, he founded and led the Mila – Quebec AI Institute.

Notably, he received the Killam Prize in 2019 and, in 2022, achieved the status of the world’s most-cited computer scientist. Bengio, who is actively involved in addressing the societal impact of AI, has also contributed to the Montreal Declaration for Responsible Development of Artificial Intelligence.

Website | LinkedIn

Jeremy Howard

data science leaders

“Data science is not software engineering. There’s a lot of overlap…but what we’re doing right now is prototyping models.”

Jeremy Howard is an Australian data science leader, entrepreneur, and educator. He was the founding CEO of Enlitic, past president of Kaggle, Co-founder of Masks4All, Distinguished Research Scientist at the University of San Francisco. He commenced his career in management consulting at McKinsey & Co and AT Kearney, spending eight years before venturing into entrepreneurship.

Howard has also founded two successful startups: an email provider, FastMail (acquired by Opera Software), and an insurance pricing optimization company, Optimal Decisions Group (ODG, developed by ChoicePoint). FastMail was among the pioneers in enabling users to integrate their desktop clients.

Howard contributed notably to open-source projects, playing a key role in developing the Perl programming language, Cyrus IMAP server, and Postfix SMTP server. As the chair of the Perl6-data working group and author of RFCs, he significantly influenced the evolution of Perl.

Website | LinkedIn

Demis Hassabis

data science leaders

“I would actually be very pessimistic about the world if something like AI wasn’t coming down the road.“

Demis Hassabis is a British computer scientist, artificial intelligence researcher, and entrepreneur. He is a polymath, renowned for his groundbreaking contributions to the field of AI. Born in 1976, Hassabis displayed prodigious talent in chess, becoming a Grandmaster at just 13. Transitioning to academia, he pursued computer science at Cambridge.

Hassabis later co-founded the pioneering video game company Elixir Studios. In 2010, he founded DeepMind, an AI research lab acquired by Google in 2014. Hassabis’ work at DeepMind has led to significant advancements in machine learning, particularly in the realm of deep reinforcement learning. His endeavors underscore a commitment to pushing the boundaries of AI’s capabilities, making him an industry leader to look out for.

Website | Twitter

Conclusion

In 2024, staying at the forefront of innovation in data science is crucial, and the 12 leaders mentioned above are the trailblazers to follow. These industry leaders, pioneers in big data analytics, and experts in data science, continue to shape the landscape with their visionary insights and groundbreaking contributions. From navigating complex algorithms to leveraging the power of machine learning, these data science leaders are steering the course for the future. Following their guidance provides an unparalleled opportunity to stay abreast of the latest trends and advancements in data science, making them indispensable figures for anyone navigating the dynamic world of data analytics.

Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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