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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

Sole Galli’s Journey From Research to Data Science

Ayushi Trivedi
Ayushi Trivedi Last Updated : 13 Mar, 2024
5 min read

At Analytics Vidhya, we are thrilled to dedicate March to honoring women in data science. Join us as we showcase the incredible journey of Sole, a pioneer in the field. Get ready for her inspiring story and more empowering narratives all month long!

Let’s look at her story of inspiration! 

Sole Galli’s Story in her own Words

Hey! I am Sole, the leading instructor at Train in Data, where we teach intermediate and advanced machine learning courses to those of you who want to boost your skills and excel in data science. But that’s not where the story starts. So, let’s start from the beginning!

Discovering Data Science

Nine years ago, I was a research scientist at a prestigious university in London. In fact, I’d worked as a researcher for 14 years! And I thought that was the only thing I was good at.

However, despite my passion for research, the routine of laboratory work began to feel monotonous, and the lack of career progression was very frustrating. But what else could I do? 

I always had a fondness for mathematics, statistics, and data analysis, yet I wasn’t sure how to leverage these interests outside of the academic sphere. Plus, my background was in biology. Would anyone consider a biologist to work on stats?

It was during this period of uncertainty that I stumbled upon the burgeoning field of data science, thanks to LinkedIn. The buzz surrounding data science piqued my curiosity, offering a glimmer of hope and a potential pathway towards a new and exciting career journey.

First Steps into the World of Data Science

To work as a data scientist, LinkedIn told me that I needed knowledge of math (check), problem solving (check), programming in R or Python, Git, SQL, machine learning (uhh?). That left me quite puzzled.

So how should I start? I had no clue. Randomly, I discovered former scientists who had transitioned into data science roles. Great! They surely had a secret recipe. I reached out to them to understand more about what they did as data scientists, and how they managed to make that transition. Their advice was quite unanimous: start with online courses.

Seriously? I had dedicated five years to university and completed a four-year PhD. Do people really change careers thanks to online courses? Spoiler alert; yes, they do. But I was very skeptical. Nevertheless, I decided to take the plunge and enroll in online courses to kickstart my journey into data science. I mean, what else could I do, right?

Lessons Learned

The beginning was challenging. My knowledge of programming was borderline zero. So, for example, I spent 1 hour trying to work out an if/else loop in R. I am not kidding! But slowly, in fact, very slowly, it got easier and easier.

I was told that I had to put my recently acquired knowledge into practice using public datasets or through data science competitions. So I did that, and it turns out that was the most valuable thing I’ve done towards transitioning into data science.

Now the time has come. Learning about data science was fun. But convincing someone that I could be a data scientist, given that my experience in the field was just six months, seemed ludicrous. Nevertheless, I put together a CV and started making applications.

I sent dozens of applications. I got no reply from most of them. But 2 or 3 did. I couldn’t stress more how valuable it was to have hands-on experience by working on some projects. The online courses gave me theoretical knowledge. The projects helped me cement those learnings and confronted me with challenges that I would face in the role and at the interview! So I was prepared!

Eventually, I was offered a role as a data scientist. I couldn’t believe it. I was over the moon. In just six months, I went from science to data science, in what at the beginning seemed an insurmountable odyssey.

Throughout these months, I learned that at the beginning, it always seems harder than it actually is, practice is key, and I should not be discouraged if things don’t always go as I wish.

Navigating the Field with Skepticism and Curiosity

Despite grappling with imposter syndrome, I persevered on my journey into data science. Colleagues began to address me as an expert, though I barely considered myself one. 

When I was asked to speak at PyData, I was taken aback. Seriously? I’d been analyzing data for less than a year. What could I possibly say? Nevertheless, I gave the talk. To my surprise, people loved it. Attendees asked numerous questions, and the talk garnered significant attention on YouTube in the years to come

This experience prompted a shift in perspective for me. In the scientific community, we often deem ourselves experts only after many years of experience. In tech, we’re all in the same boat. We constantly learn from each other. Everyone brings something valuable to the table. 

This realization motivated me to create my first online course, initially as a hobby and experiment. If I had earned from people like you and me, who had transformed my life, couldn’t support others in their journey?

Work in Progress: Growing Stronger Everyday

Fast forward a few years, and we now offer several online courses, some taught alongside my former colleagues, with enrollment reaching 55k+ people worldwide. The positive feedback has been overwhelming. 

But that’s not all. As part of my course curriculum, I developed an open-source Python library for feature engineering that supplements scikit-learn with additional methods. Feature-engine has gained significant traction, currently with 150k monthly downloads and 1.7k stars on GitHub, and many contributors adding new functionality.

Reflecting on my journey, I never imagined I would end up here when I first embarked on data science. From struggling with a simple if/else loop to maintaining an open-source library, it’s like, wow, how was that even possible?

It’s been an incredible journey. And it goes to show that, with determination, a lot is possible. Good luck with your career – I am cheering for you!

About Sole Galli

Sole Galli, the leading instructor at Train in Data, embarked on a remarkable journey from a research scientist in London to a thriving data scientist and educator. With a background in biology, Sole discovered a passion for mathematics, statistics, and data analysis, leading to a transition into data science. Starting with online courses and hands-on projects, Sole honed their skills, eventually landing a role as a data scientist within just six months. Now, with several online courses and an open-source Python library for feature engineering under their belt, Sole continues to inspire and empower aspiring data scientists worldwide.

Click on her LinkedIn profile here.

Summing-up

Sole Galli’s journey shows the transformative power of determination and learning. From struggling with programming basics to creating impactful online courses and an open-source library, Sole’s story inspires us to embrace challenges and persevere in the pursuit of our passions.

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.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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