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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

Kaggle Grandmaster Series – Exclusive Interview with Kaggle Quadruple/4x Grandmaster Rohan Rao

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Analytics Vidhya Last Updated : 12 Feb, 2021
6 min read

Ever seen a data scientist with more than 2 Kaggle Grandmaster titles? Well, don’t stop at 3 now as we bring to you the thoughts of a Quadruple Kaggle Grandmaster!!

In the 20th edition of the Kaggle Grandmaster Series, we are honored to be joined by Quadruple Kaggle Grandmaster- Rohan Rao.

Rohan Rao

Rohan ranks 100th in Kaggle Competitions, 6th in Datasets, 12th in Notebooks, and 12th in Kaggle Discussion category with 8,8,15 and 56 gold medals to his name respectively.

Rohan currently works as a Data Scientist at H2O.ai. He has a Masters Degree in Applied Statistics from IIT Bombay. He is also a 17-time National Sudoku/Puzzle Champion

You can go through the previous Kaggle Grandmaster Series Interviews here.

 

In this interview, we cover a range of topics, including:

  • Rohan’s Education and Work
  • Rohan’s Kaggle Journey
  • Rohan’s Advice to Beginners in Data Science
  • Rohan’s Inspiration

So without any further ado. Let’s begin.

Rohan’s Education and Work

kaggle grandmaster series education and work

Analytics Vidhya(AV): You hold your Master’s degree in Applied statistics. How has your educational background in Applied Statistics helped you in your professional career as well as in Data Science hackathons/competitions?

Rohan Rao(RR): Mathematics & Statistics form the roots of many data science workflows and machine learning algorithms. Having a solid understanding of the fundamentals helped me learn and grow faster, professionally as well as competitively.

The Master’s degree and experience gave me more exposure to the field and also opened up a lot of opportunities in different industries.

 

AV: Currently, you’re a Data Scientist at H2O.ai. So what is your day-to-day job role there? Also, what tips would you like to give everyone to land the position of Data Scientist at some good company?

RR: I work in between our products and customers, constantly engaging and helping users of our platform while building and enhancing our products in parallel. I enjoy this kind of role because it gives me an opportunity to contribute to different aspects of the business.

It’s important to know and understand the exact role of a Data Scientist because it can vary widely across different companies and then prepare and apply for the ones that best suit your skillset and interests.

 

Rohan’s Kaggle Journey from Scratch

data science interview kaggle grandmaster series

AV:  You’ve recently become Kaggle Quadruple Grandmaster, a big congratulations on this achievement!! You’re one of three Kaggle Quadruple Grandmasters and now we have this privilege to ask questions from someone who has aced all the four categories of Kaggle i.e Competitions, Datasets, Notebooks, and Discussions. So please tell us in detail about the challenges you faced in each aspect and also how did you overcome them?

RR: Thank You! It has been one of the most exhilarating experiences of my life and I’m glad I could accomplish this feat.

Competitions are the only category that I find having a meaningful ranking & points system while the other three are more for learning and networking. It’s also the primary reason why I enjoy spending time on Kaggle. What helped me most here was to try out and work on as many different types of competitions as time permitted in my early days and those experiences helped me accrue some successful results in subsequent competitions.

Datasets were the hardest category to reach the KGM tier. What helped me was to constantly work on dataset ideas that would be useful for projects or competitions and publish extremely clean datasets that users can use conveniently.

Notebook is a great category to showcase novel visualizations or models or pipelines. I don’t like writing EDA notebooks much so most of my efforts have gone into interesting model pipelines or utility scripts or tutorials.

Discussions are fun and easy for me because I enjoy writing ☺

 

AV: What was the order of getting your Grandmaster titles? Which was the hardest to achieve and why?

RR: Competitions (in 2016), followed by Notebooks, Datasets, and Discussions in order (all in 2020).

Datasets were the hardest and most likely due to lower activity and visibility compared to the other three categories on the platform. So it involved more effort and investment of time.

 

AV: What is your strategy/framework to tackle any ML problem given in any competition?

RR: Start with the basics and tweak as you go.

Spend the majority of the time exploring the data. Read every discussion and scan every notebook. Be open to approaches. Often the craziest of ideas can help you rank well.

Team up to learn more and ensemble better, it is often under-rated.

 

AV: Can you tell us about your top three favorite competitions/notebooks so far that have shaped your Kaggle journey? And what were the challenges you faced particularly while coming up with their solutions?

RR: ASHRAE competition is my most memorable competition as we had a great team and it was my first prize-winning competition on Kaggle.

Large Datasets Tutorial notebook is my most popular notebook and in fact, I had deleted that notebook because I did not like it. Fortunately, I had shared the notebook with a couple of friends who gave me extremely positive feedback and convinced me to republish it.

Santander Product Recommendation was the competition that gave me my 5th gold medal to become a Competitions Grandmaster. My team-mate, Sudalai, and I were struggling to make our ensemble work until we chose different weights for different rows, some of them not even summing to 1. Non-sensical idea but worked.

 

AV: Since you’ve earned 8 gold medals in Datasets, please tell us what is your whole procedure for creating a dataset? And according to you how much data is enough for a good dataset?

RR:

  • Pick a dataset that is new to Kaggle and hard to compile. That’s where you create value that users will appreciate.
  • Publish the cleanest format of the data and maintain it regularly.
  • Document as many details as possible and share as much information as you have.
  • Publish an introductory notebook showcasing what the dataset contains and how it can be used.

 

AV: What are the three things that one should keep in mind if he/she wants to achieve a higher level in all the four categories- Competitions, Datasets, Notebooks, and Discussions ranking respectively?

RR:

  • Be open to learning new things, facing criticism, and trying out as many ideas as possible.
  • Focus on quality over quantity.
  • Don’t cheat, don’t spam

 

Rohan’s Advice to the Beginners in Data Science

Kaggle grandmaster series data science interview work

AV: Can you suggest some competitions that you think are best for beginners to try their hands on, especially for tackling specific tasks like regression classification, NLP-related tasks?

RR: Most of the knowledge competitions on Kaggle are specifically meant for beginners to get started and they are the best place to start and learn stuff.

 

AV: Do you prefer to use a specific rig at your end to solve DL problems or you prefer using tools like Kaggle or Google Colab?

RR: I use a combination of both. I try to make the maximum use of freely available resources before moving to my personal servers (which are sometimes costly).

 

AV: Can you tell us some lesser-known yet very helpful ML and DL frameworks/libraries according to you?

RR: python-datatable is a good alternative to pandas especially while working on large datasets with limited resources.

Recently I quite enjoyed using PyTorch in R, it’s a great wrapper for DL-practitioners who want to stick to R.

 

AV: How do you keep yourself updated with the latest technologies/framework related to DL and ML? And how do you use them in the industry as well as in the hackathons?

RR: Kaggle is the best place to keep oneself acquainted with all the latest happenings in ML/DL and ArXiv for reading research papers. The more you spend time working on these hands-on, the better you get at it and the easier it becomes to apply them to hackathons and real-world industry problems.

 

End Notes

It was a pleasure for us to interview such a multi-talented person. His thoughts and words are enough to get anyone to begin and stay focused on their data science journey.

This is the 20th interview in the Kaggle Grandmasters Series. You can read the previous few in the following links-

What did you learn from this interview? Are there other data science leaders you would want us to interview for the Kaggle Grandmaster Series? Let me know in the comments section below!

Kaggle Grandmasterkaggle grandmaster series
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Analytics Vidhya

Analytics Vidhya Content team

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