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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

The Catalyst Behind Leondra R. Gonzalez’s Data Science Career

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Guest Blog Last Updated : 23 Mar, 2024
4 min read

At Analytics Vidhya, we believe in the power of stories to inspire, uplift, and empower. That’s why, throughout March, we’re embracing women in AI by sharing their remarkable journeys and achievements on our blog. Today, we’re honored to feature Leondra R. Gonzalez, a trailblazer in the field of AI with an impressive track record. Join us as we delve into her non-linear journey to data science, from a music major to a Senior Data & Applied Scientist at Microsoft.

Let’s look at her story of inspiration! 

Leondra R. Gonzalez

Leondra R. Gonzalez’s Story in her own Words

Non-Linear Journey to Data Science

I am a data and information scientist with a passion for data and enabling data cultures. My journey to the data science world was complex, non-linear, and untraditional, until one day it wasn’t. I always grew up enjoying math and even considered becoming an actuarial science major, but the idea was swiftly detoured by the thought of going into a field with which I have 0 familiarity and no community or familial success stories to reference. At the time, most of my family were tradesmen and blue collar workers, so my mind blocked out the prospect of even considering a career in STEM.

From Music Major to Data Scientist

Despite being a numbers girl with great grade and even an internship at the Ohio Department of Insurance under my belt, something felt off. Furthermore, attending a 4 year college alone was a spectacular achievement in my community. In undergrad, I was eager to double down on a channel of success that I’d seen in media for people who look like me. Thus, I decided to become a music major. Thankfully, my mom urged me to minor in something more practical for 9-5 jobs, and so I minored in business and added a focus in management.

It wasn’t until being accepted into Carnegie Mellon University for a Masters in Entertainment Industry Management (MEIM) was I exposed to the world of tech. That is, luckily for me, the MEIM program was based in Heinz College of Information Systems. I not only aced, but also enjoyed passing statistics and probability classes. I was able to keep up with the tech majors in Information Systems, Data Analytics, Public Policy, and others at the school. It wasn’t until this moment did I finally ask,  “why not me?”.

Education and Growth

 I was also exposed to a richly diverse student body from all over the world, including those that looked like me. There was no barrier between my interest in data and technology, and a career in tech, except my own disbelief. This new found confidence eventually led to my enrollment in a data science program at Harvard University, completing an MBA with a concentration in Statistics at Quantic School of Business and Technology, and eventually becoming a PhD candidate in IT with a Specialization in AI at University of the Cumberlands. That’s quite the turnaround for someone who thought a career in data and tech was “unreachable”.

Journey to Leading Strategies in ML and AI

Now with 10+ years of experience in data science, I’ve created or led strategies on various applications of ML and AI across multiple industries including media/ entertainment, digital marketing / advertising, operations research and consulting. In the process, I have had the honor to experience much success, as well as tough lessons that have helped me become a better rounded data scientist and researcher. For example, I’ve learned the challenges of navigating career changes (planned or otherwise), how and when to skill up, solving complex problems, asking the right questions, and coping with Imposter syndrome.

PhD Studies and Industry Experience

Meanwhile, I have also had the privilege of researching AI as a PhD candidate, stretching my research capabilities as a computer science research apprentice at Google, practicing my skills as an intern at Amazon Prime & Video, leading teams in implementing AI strategy and governance, and shared my thought leadership by speaking at conferences, podcasts, and publishing a book on mastering data science interviews. I am now a Senior Data & Applied Scientist at Microsoft, a leading company in the AI space. Sometimes I look back and wonder how I got this far. Was it really just a realization that I’m just as capable as everyone else?  While that was certainly the catalyst, someone gave me some much needed advice at a lower point in my professional life.

The Best Advice: Overcoming Doubt and Embracing Learning

The best piece of advice I’d ever received was that if I don’t know something, it is simply because I haven’t learned it. It’s that simple. That is, the only barrier between those we admire and ourselves is often giving something a try, not unlike my exposure to data science at CMU. A good friend, who later became my travel buddy and maid of honor at my wedding, and also works in the analytics space, gave me this advice. It gave me the confidence to identify and address gaps in my knowledge by tackling them bravely as opposed to doubting myself at every turn. In doing so, I became more confident with every learned skill. 

And so I pass this advice along to other women who ever have self doubt. Know that ignorance is not a death sentence for your career but a blessing for career development. Embrace the unknown and use it to help you thrive in the next stop in your data science journey!

About Leondra R. Gonzalez

Leondra R. Gonzalez is a data and information scientist with a diverse background, from a music major to a tech enthusiast. With over 10 years of experience in data science, she has led strategies in ML and AI across various industries. Currently a Senior Data & Applied Scientist at Microsoft, Leondra is passionate about empowering women in data science and sharing her journey to inspire others.

Check out her LinkedIn profile here.

Summing-up

Leondra R. Gonzalez’s journey in data science is a testament to resilience and determination, showcasing the power of overcoming challenges to achieve excellence. Her story serves as an inspiration to embrace our identities and strive for success in the field of AI.

You can also share your story or nominate someone who has inspired you by filling out this form. 

Let’s continue to uplift and empower each other in the journey towards a more inclusive and innovative future.

tech womanwomen daywomen in AI
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Guest Blog

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