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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

Pioneering Data Science in Latin America with Favio Vazquez

Nitika Sharma
Nitika Sharma Last Updated : 06 Dec, 2023
3 min read

In this edition of Leading with Data, we feature Favio Vazquez, a physicist turned computer engineer and data scientist. Armed with a master’s degree in physics and a profound interest in cosmology, Favio has not only generated over 2M USD in new business for H2O.ai but has also been instrumental in securing 20+ customers in the Latin America. Join us as we delve into Favio’s journey, uncovering profound insights at the crossroads of physics, computation, and the impactful realm of data science.

You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcasts, and Apple. Pick your favorite to enjoy the insightful content!

Key Insights from our Conversation with Favio Vazquez

  • Transitioning into data science is often unexpected but rewarding for those with a background in scientific computation.
  • A data science career demands a balance of technical expertise and business acumen, emphasizing practical experience.
  • Generative AI is set to change the future of data science, but the basics of machine learning will still be important.
  • Data scientists must prioritize continuous learning and staying updated on industry trends to remain competitive and innovative.

Join our upcoming Leading with Data sessions for insightful discussions with AI and Data Science leaders!

Now, let’s look at the questions asked by Favio Vazquez in the session and how he responded!

How did your journey in Data Science begin?

My journey into data science was quite serendipitous. Originally from Venezuela, I pursued physics and computer engineering with a deep interest in cosmology and astrophysics. I foresaw the growing importance of computation in physics, which led me to learn about programming and large-scale computational problems. My first real exposure to data science came during an internship where I was required to perform data analysis. Although I was familiar with machine learning from a scientific perspective, applying it to business was new territory for me. This experience piqued my interest, and I began to delve deeper into data mining and machine learning, marking the start of my data science career.

What were the early challenges in building a Data Science ecosystem in Mexico?

When I moved to Mexico, I realized the need to build a data science community in Latin America. Along with others who shared this vision, we began organizing conferences, sharing knowledge, and connecting with professionals across the region. We were essentially laying the groundwork for the first wave of data scientists in Latin America. To keep up with global trends, I turned to platforms like KDnuggets, Towards Data Science, and Analytics Vidhya, which were some of the few resources available at the time.

How did you transition from academia to a full-fledged Data Science career?

The transition was gradual. While completing my master’s degree in physics, I was already dabbling in data science projects for fun. However, the demand for data science expertise in Mexico was growing, and companies began reaching out to me for collaboration. Eventually, I decided to go for a PhD in physics and fully commit to a career in data science. This decision led me to work with some of the largest companies in Mexico, where I played a strategic role in establishing their data science capabilities.

What kind of projects have you worked on, and how did they shape your career?

I’ve tackled different projects, predicting customer behavior, finding the best spots for stores, and assessing risk in banking. These experiences turned me into a generalist, helping in my current role at H2O.ai, where I handle projects in various industries. My advice for newcomers: specialize in a specific area in data science. It’s tough to be seen as an expert if you try to do too much.

How has your role evolved at H2O.ai?

At H2O.ai, my role is end-to-end, encompassing pre-sales activities like demos and proof of concepts, as well as working closely with customers to integrate AI tools, build models, and develop data science strategies. This has given me a broad understanding of various industries’ needs and how AI can be applied effectively. I believe my future lies in taking on more strategic leadership roles, guiding teams to success in the data science and AI space.

What are your thoughts on the future of Data Science and your career path?

Generative AI is the current buzzword, and I believe it will dominate the field for the next few years. However, classical machine learning models will not become obsolete anytime soon. As for my career, I aim to move into more strategic roles, leveraging my extensive experience to lead teams and help companies transform through data science. I also emphasize the importance of staying informed about the latest trends by reading research papers, attending conferences, and following industry reports.

Summing Up

Favio Vazquez’s journey shows how things just fell into place as he moved from studying to a successful career in data science. His role at H2O.ai highlights his dedication to shaping the future of data science. Even with the excitement about Generative AI, Favio stresses that old-school machine learning is still crucial.

For more engaging sessions on AI, data science, and GenAI, stay tuned with us on Leading with Data.

Check our upcoming sessions here.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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