Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Exclusive Interview with SRK, Sr. Data Scientist, Kaggle Rank 31 (DataHack Summit – Workshop Speaker)

Kunal Jain
Kunal Jain Last Updated : 21 Sep, 2017
6 min read

Introduction

It took him just 2 years to secure a rank in Kaggle Top 30 from scratch

Mr. Sudalai Rajkumar a.k.a SRK, Lead Data Scientist at Freshdesk and previously worked as Sr. Data Scientist, Tiger Analytics has become a huge inspiration for aspiring data scientists around the world. I’ve seen lots of people, driven by the spark of becoming a data scientist, but tend to develop disinterest when faced with difficulties.

 

Sudalai Rajkumar and Bishwarup Bhattacharjee are taking 8-hours intense workshop during DataHack Summit 2017 on The MasterClass: How To Win Data Science Challenges. Attend this workshop at DataHack Summit 2017, 9 – 11 November in Bengaluru.

The path to become a data scientist isn’t easy as it looks. Even though, you are fortunate enough to have access to free online courses, but it requires unshakable determination to succeed. And, when it comes to securing rank in Top 30 kagglers, you can’t achieve it without hours of coding and developing logical understanding.

We decided to catch up with SRK to know about his success recipes and source of motivation, which has kept him going all these years. As we know, a journey without struggles isn’t worthy of success, we found out his ways of overcoming with struggles.

interview data scientist data science

Below is my complete conversation with SRK:

KJ: First of all, I would like to thank you for devoting time to us. I’m sure, this interview would act as a motivational booster for young aspiring data scientists around the world. Let’s start.

 

KJ: You are currently Ranked 25 on Kaggle. How did your journey begin?

SRK: My kaggle journey started two years back when I started learning data science / analytics through MOOC courses. I was already working in analytics domain then. But, didn’t get any opportunity to use the advanced analytical techniques (which I learnt from the courses) in my work. Hence, I started looking for opportunities (projects) where I could use those techniques and subsequently came to know about Kaggle through my friends.

Like every other aspiring data scientist, I started with the classic “Titanic” problem for first couple of weeks. Later, I realized that I was unable to give my best efforts since there were no time constraints, being a knowledge competition. Perhaps, I work best with deadlines.

Then, I started working on the “StumbleUpon Evergreen Classification Challenge”. This challenge required building a classifier to categorize webpages as evergreen or non-evergreen. It was a classic binary classification problem. It involved good amount of text mining as well, which made it lot more intriguing for a newbie like me. Not to forget, those benchmark codes really helped me learn a lot in the competition and kicked my Kaggle journey.

 

KJ: It’s being said, ‘Success never comes to you unless you learn to embrace failures’. Did you have hard time in dealing with data science / analytics as a beginner?

SRK: Yes. Initially, I found it really hard to secure respectful position in the competitions. It took me a year to get my first top 10% finish. Then, almost another year to get the “Kaggle Master” status.

As a beginner, I tried many approaches while building models but most of them failed to give good results. I felt helpless and lost. But, when I look back in time, I realize that they are not actually failures. But, much needed lessons which helped me to perform better in the future competitions.

 

KJ: What helped you to overcome these difficulties?

SRK: Undoubtedly, Kaggle forums have helped me a lot. I’ve learnt from other people’s views, codes, results and implemented them to improve my mistakes. Like Analytics Vidhya, it is a superb destination to learn lots of new things about analytics & data science. Personally, I’ve learnt techniques including XGBoost, Deep learning, Online learning, t-SNE etc., from Kaggle forums.

Above all, one more difficulty hampered my way. The thing was, some of the competitions turn out to be highly frustrating at times, especially when I used to put in lot of efforts into certain approach yet didn’t get any improvement in results. Amidst such difficulties, I didn’t give up! Instead, discovered a way.

It is essential to stay afloat during those times. I’d generally switch to work on a different competition for the next couple of days and then come back to the earlier one. This break helped me clear my mind and gave a fresh perspective on the problem.

In addition, I used to set short term targets such as improving the score in the next couple of days by a certain margin, improving my rank in the competition and so on and keep working towards them.

 

KJ: How do you decide in which Kaggle competition you should participate?

SRK: I knew this was coming!  When I started, I participated in almost all the competitions that came up. Even now I think I am doing the same, with a slight difference.

The difference is that, in early days, I used to focus equally on all the competitions. But now, I try to select the competitions best suited to my caliber and focus more on them. I believe I’m best suited for competition which challenges my knowledge and needs good amount of effort in feature engineering.

I still need to learn multi-level stacking. Hence, I generally refrain myself from taking part actively in such competitions.

 

KJ: In recent past, you’ve quickly climbed up in rankings on Kaggle. What’s your next target?

SRK: Of course getting into top 10 in Kaggle rankings. And, if possible, I’d love to secure the No.1 spot 😉  But I think it is still a long way to go !!

 

KJ: Do you prefer to work in teams or as in individual ? Which one do you recommend ?

SRK: I prefer working in a team given that one has got seriously smart partner(s). Typically, in a team, each member tries to think of different ideas / solutions and combining all these ideas potentially provides a better solution. This is analogous to ensemble modeling, where a number of varied models combine together to produce a single strong model.

It is generally advisable to form team at a later stage of the competition so that individuals have their own ideas / models before merging.

But it is also important to work as an individual in few competitions for self-assessment. It will help us to know our strong and weak areas so as to tailor our learning plan accordingly.

 

KJ: Tell us 3 things which you’ve learnt in life working as a Data Scientist ?

SRK: Here are my 3 cents:

1. Understanding the problem – It is really important to have a thorough understanding of the problem that we are trying to solve. Only after we’ve understood the problem clearly, we can derive suitable insights from data to tackle the problem and obtain good results. This applies to real life as well.

2. Structured Thinking – It’s a unique way of thinking through the problems. Being a data scientist, one needs to be more structured in his/her thinking in order to obtain good results. Else, we might end up shooting in the dark as the number of options are way too many in most of the cases.

3. Effective communication of results – Effective communication of derived results is as important as performing the data analysis. At times, it becomes difficult to communicate the nuances of final analysis in simple language to business people. As a Data Scientist, one must learn the art of effective communication.

 

KJ: Is there a one for all road map for predictive modeling? According to you, what is an ideal approach (to work on a data set) to derive the best result ?

SRK: I myself, is eager to know that road map if one such exists for predictive modeling. It would be much easier that way. However, here is the approach that I follow (in competitions) is:

  1. Understanding the problem and dataset
  2. Pre-processing the data
    • Data cleansing
    • Outlier removal
    • Normalization / Standardization
    • Dummy variable creation
  3. Feature engineering
    • Feature selection
    • Feature transformation
    • Variable interaction
    • Feature creation
  4. Selecting the modeling algorithm
  5. Parameter tuning through cross validation
  6. Building the model
  7. Checking the results by making a submission

Once you’ve executed these 7 steps, a basic framework will be ready to do more experimentation. Further, you can concentrate more on:

  1. Feature engineering – This is where bigger improvements come from most of the times
  2. Building varied kind of models and ensembling them – This will help go that extra mile towards the end

Last but not the least,  we must perform a solid local validation. Else, we might end up over fitting on the public leader board.

 

KJ: Which machine learning algorithms are the most important to learn and practice ? Why ?

SRK: Every algorithm has its own advantages and so it is absolutely necessary to learn as many as we can. However, here are few algorithms which have proved to be extremely helpful to me:

  1. Gradient Boosting Machines : GBMs are generally good for all kind of structured data problems especially XGBoost variant.
  2. Neural Networks : Neural Networks helped me in dealing with unstructured data such as image analysis, text analysis.
  3. Online learning : Online learning algorithms like FTRL perform well when the datasize is huge and / or continuously streaming. It is robust even if there is a change in trend with respect to time.

 

KJ: What is your suggestion / advice for people keen to become a data scientist?

SRK: As a (wanna-be) data scientist

  • One need to be strong at the basic concepts of statistics, modeling
  • Never shy away from writing codes / programing
  • Master at least one tool / language of our choice; more the merrier.
  • Develop a knack for aptitude / logical reasoning
  • Moving forward, it will also be essential to know big data technologies
  • Get hands dirty by taking part in a lot of projects / competitions

KJ: I’m sure your invaluable experience and suggested guidance would help many young data scientists to discover the complicated world of data science and machine learning. Thank you once again.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Karthikeyan
Karthikeyan

Happy to see you featured here SRK. Good luck and may your wish come true.

123

SRK
SRK

Thank you!

123
shashank
shashank

How can we handle huge data sets. From my experience R gui gets stuck when huge dataset is fed to it. Whats the other solution ??

123

SRK
SRK

Hi Shashank, We need to do have bigger ram size if we need to handle bigger datasets in R. Some alternate options are : 1. Building models in a distributed environment using Spark, hadoop etc 2. Using online learning models like FTRL which don't import the whole dataset into ram for building models. Thanks,

123
Arul antony
Arul antony

Useful

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details