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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

Ruchi Bhatia’s Journey from Triple Kaggle Grandmaster to Lead Data Scientist

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

At Analytics Vidhya, we’re shining a spotlight on women in data science this March. Dive into the inspiring journey of Ruchi Bhatia, a leader in the field. Stay tuned for her story and more empowering narratives all month long!

Let’s look at her story of inspiration!

Ruchi Bhatia

Ruchi Bhatia’s Inspiring Journey into Data Science

My Data Science journey, as a woman in tech, was fueled by curiosity and a desire to explore the world through the lens of data. And it’s been an incredible ride. I began coding when I was 13 years old, which built my interest in Computer Science well before I chose to study Computer Engineering in college. I was keen to learn about all areas related to Computer Science, motivated by my eagerness to challenge myself and find the area that clicked with me the most. This search eventually led me to Data Science.

I jumped into Data Science by taking courses, doing lots of research, and working on my own projects. Unlike some who learn as they work, I spent most of my internship time on projects that were all about Data Science. This helped me deepen my knowledge and love for this field.

Becoming a Triple Kaggle Grandmaster

Learning theoretical concepts and practicing with simple datasets is one thing, but applying those skills to solve real-world problems with data from various domains is another level entirely. Kaggle played a significant role in my journey since it helped me showcase my work on a global scale and motivated me to stay consistent.

Participating in Kaggle competitions was daunting yet thrilling. My initial attempts may not have been successful by traditional measures, but they represented significant learning milestones and helped me overcome the fear of failure. The exposure offered by the platform was tremendous in terms of helping me upskill my knowledge.

One of my proudest achievements was becoming the youngest Triple Kaggle Grandmaster. The countless hours spent crafting datasets, creating public notebooks, and engaging in discussions culminated in a tangible recognition of my dedication. 

From Data Enthusiast to Data Science Global Ambassador

My active contributions on Kaggle led me to become a Data Science Global Ambassador at HP. Through this program, I got access to state-of-the-art workstations and GPUs, which significantly enhanced my data science workflows.

These resources allowed me to conduct numerous experiments simultaneously, a boon in an era where workflows are compute intensive and efficiency is paramount. The high-quality equipment not only boosted my productivity but also facilitated my professional growth and enabled collaborations with some of the brightest minds in the field. It served as a catalyst for my continued engagement and contributions to the community.

Bridging Technology and Business as a Lead Data Scientist

The next defining period of my journey was my tenure at OpenMined. Here, I not only refined my technical skills but also pursued my passion for ethical AI. I spearheaded significant projects, including a pivotal collaboration with Twitter on algorithmic accountability and transparency, and made contributions to the UN Privacy Enhancing Technologies Lab (UN PETs Lab). I played a key role in a groundbreaking project with Twitter, where I led efforts to create synthetic datasets derived from Twitter data.

The objective was to identify and quantify biases within the data, a challenge that required both innovative problem-solving and meticulous attention to detail. The ability to translate complex data science concepts into actionable strategies garnered recognition from senior leadership at Twitter, marking a significant milestone in my career and highlighting the critical nature of our collaboration.

My contributions to this project and the success of our initiatives led to me becoming a Lead Data Scientist. A major aspect of my leadership involved contributing to the UN Privacy Enhancing Technologies Lab (UN PETs Lab). Here, I was responsible for developing and presenting innovative data science demonstrations on a weekly basis, employing state-of- the-art techniques. This played a crucial role in enhancing our software’s capabilities and improving the user experience.

Lessons Learned along the Way

Over my past few years in the industry, I’ve learnt some invaluable lessons:

  • Perseverance is key: Data science is challenging. There will be times when the code doesn’t work, or the model performs poorly. The key is to keep trying, learning from failures, and moving forward.
  • Community matters: The data science community is incredibly supportive. Engaging in forums, attending meetups, and participating in hackathons can provide support, inspiration, and opportunities for collaboration.
  • Never stop learning: The field of data science is ever-evolving. Staying curious, keeping up with the latest trends, and continuously learning is crucial to success.
  • Embrace diversity: Diverse perspectives enrich data science. As women, we bring unique viewpoints and approaches to problem-solving, which is vital for innovation and creativity in the field.

A Personal Note to my Fellow Women in Tech

If you’re contemplating a journey into data science, remember that your perspective, resilience, and creativity are invaluable assets. The path may be challenging, but it is also filled with opportunities for growth, learning, and making an impact. When I first chose Computer Engineering as my undergraduate major, I quickly noticed the gender gap in the tech space, and it was even more evident in Data Science. Yet, this observation never stopped me. Instead, it fueled my determination. I’m truly glad I didn’t let anything deter me from pursuing my passion in this field.

Often, just a little encouragement is all it takes to move forward when facing obstacles. I firmly believe that Data Science should be accessible to everyone, regardless of their background or gender. After all, it’s the diversity in our perspectives that enriches our work and leads to groundbreaking discoveries. As we celebrate Women’s Day, I want to inspire you to take a step forward in your learning journey and chase your dreams. Let’s embrace the adventure and the endless possibilities that the fascinating world of Data Science holds.

About Ruchi Bhatia

Ruchi Bhatia is a Computer Engineer with a Master’s degree in Information Systems Management from Carnegie Mellon University. Currently, she is leveraging her skills as a Product Marketing Manager in the rapidly evolving field of Data Science and AI at HP. She takes pride in being the youngest triple Kaggle Grandmaster across the Notebooks, Datasets, and Discussion categories.

Her previous role as the Leader of Data Science at OpenMined allowed her to refine her leadership skills while guiding a team of data scientists to create groundbreaking solutions. Furthermore, as one of the 21 Data Science Global Ambassadors for Z by HP, she had the opportunity to share her knowledge and insights with the broader data science community.

Click on her LinkedIn profile here.

Summing-up

Ruchi’s passion for coding started at a young age and has only grown over time, particularly in the areas of Data Science and Data Analysis. Her experience working in the technology divisions of multinational corporations such as Sony and Colgate-Palmolive has provided her with invaluable industry insights. As the integration of businesses and information technology continues to grow, she is committed to leveraging her expertise to bridge the gap between technology and business effectively

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.

G
Guest Blog

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Responses From Readers

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Sai Sudha tatikonda
Sai Sudha tatikonda

Wow this story suddenly came up as I was browsing and its so inspiring! I am a 44 year old woman and a mother of 2 kids. I began my transition journey about 2 years back but I havent managed to achieve any significant milestones yet. Time is my biggest enemy. And I often wonder if it was the right decision. However reading about Ruchi has sonehow motivated me to stay here and keep trying. Our circumstances might be totally different but perseverance and resilience that Ruchi showed are something that I am truly inspired to hang on to.

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