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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

Decoding the Data-Driven Journey: Insights from Paisabazaar’s CEO 

Nitika Sharma
Nitika Sharma Last Updated : 01 Dec, 2023
4 min read

Introduction

In this episode of Leading with Data, Kunal Jain engages in a conversation with Naveen Kukreja, CEO of Paisabazaar. Unveil the secrets behind Naveen’s remarkable career, from navigating the banking and financial services domains to pioneering data-centric strategies in India’s evolving digital landscape. Join us as we decode the transformative power of data in business through Naveen’s experiences and leadership at Paisabazaar.

You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcasts, and Apple. Pick your favorite to enjoy the insightful content!

Key Insights

  • Embracing a data-centric business model is challenging yet essential for modern decision-making.
  • Business growth hinges on adapting to digital transformations and leveraging initiatives like India Stack.
  • The significance of data science amplifies as businesses scale, particularly in tailoring products/services to consumer needs.
  • Public listing instills discipline and transparency and influences a balanced approach to growth and profitability.
  • Effective leadership in a data-driven organization requires a harmonious blend of intuition and data-backed decision-making.
  • The future of business is shaped by the profound influence of AI and ML, emphasizing the need for flexibility and upskilling.
  • Transitioning into data analytics demands hands-on experience and a robust technical team.
  • Personal interests and continuous learning contribute to a fresh perspective, fostering professional growth.

Join our upcoming Leading with Data sessions for insightful discussions with AI and Data Science leaders!

Can you share How you got into Banking, Financial Services, and Analytics?

I accidentally stumbled into banking and financial services during campus placements at IIM Calcutta. My interest was in consumer behavior and marketing, but Citibank changed my career trajectory. My real introduction to analytics came at Capital One in the UK, where data-driven decision-making was integral to the business, a stark contrast to the developing digital landscape of India in the early 2000s.

How did you Adapt to a Data-centric Environment at Capital One?

The transition was challenging but invaluable. I moved from Citibank to Capital One to deepen my understanding of the credit card business and learn new skills in a digitally mature market. Managing a team of analysts without a background in data analytics was tough. Still, it honed my decision-making abilities and taught me the importance of data in driving business outcomes.

What Prompted your Return to India and your Move into the Insurance Industry?

The global financial crisis of 2007 and 2009 made me reconsider my career path. Capital One faced tough times, and I saw an opportunity in India’s growing insurance sector. At Aviva, I set up the cross-sell and analytics functions from scratch, which was exciting and aligned with my strengths.

How did your experience at Aviva lead to the Creation of Paisabazaar?

Digital was becoming a significant force in India, and while leading Aviva’s digital business, I partnered with Policybazaar, a key player in our growth. The potential of a financial marketplace in India, similar to mature markets like the UK, was evident. I joined Policybazaar to establish Paisabazaar as a leading credit marketplace.

Can you describe the Journey of Scaling Paisabazaar to over 4000 Employees from Scratch?

It’s been a humbling and gratifying experience. Our success is rooted in the Indian consumer’s need to compare products and make informed decisions. The digital revolution, government initiatives like India Stack, and our leadership team’s stability have been pivotal. We’ve also benefited from the right partnerships and a focus on providing unbiased financial advice.

How has the Decision to go Public Influenced Paisabazaar’s Growth?

Going public has brought discipline and transparency to our operations. It has shifted our focus towards balancing growth with profitability and has unburdened us from worrying about data sharing. The public listing is a testament to the sustainability of our business model.

When did you Start Integrating Explicit Data Science Practices at Paisabazaar?

About a year and a half into our journey, we recognized the need for advanced data analytics to match consumers with suitable lenders. The launch of our free credit score product further amplified the need for robust data infrastructure as we dealt with massive amounts of data.

What Impact has Data Science had on Paisabazaar’s Operations?

Data science has been instrumental in improving our efficiency and effectiveness. It has helped us understand consumer behavior, optimize our associate-customer interactions, and develop tailored financial products. We’re now exploring machine learning to analyze voice data from customer interactions, which holds immense potential for insights and fraud detection.

How has your Leadership Style Evolved with the Growth of Paisabazaar?

My leadership style has always been consumer-centric, but my exposure to data analytics at Capital One has been a game-changer. I encourage a data-driven approach to decision-making and conflict resolution. Even in areas like brand marketing, we’ve successfully integrated data analytics to measure the impact of our campaigns in real time.

Data-Driven Journey

With the Rapid Advancements in AI and ML, what are your Thoughts on the Future of Data in Business?

The AI and ML revolution could be as transformative as the iPhone was for smartphones. Flexibility, upskilling, and attracting talent to leverage these technologies are crucial. While big tech currently dominates the AI space, partnerships with companies like Microsoft on Azure are helping us prepare for the future.

What Advice do you have for Professionals Transitioning into Data Analytics or Business Leadership Roles?

For those aiming to become analytics leaders, there’s no substitute for getting your hands dirty with coding and data. Surround yourself with technically proficient team members to fill any gaps. For business leaders, understanding and utilizing data is no longer optional—it’s imperative for staying competitive.

Outside of Work, What Do You Enjoy Doing?

As a spectator and participant, I’m a sports enthusiast, currently working on improving my golf game. I also enjoy mentoring young entrepreneurs and startup enthusiasts, which is mutually beneficial. Reading—or listening to audiobooks on Audible—is another way I like to broaden my perspective on various topics.

Summing Up

Naveen Kukreja’s journey is a beacon for aspiring leaders and data enthusiasts. From the pivotal role of data science in scaling Paisabazaar to the profound impact of going public on business operations, Naveen shares invaluable insights. Explore the fusion of leadership, technology, and consumer-centricity, proving that the future of business lies in the seamless integration of AI and ML. 

For more engaging sessions on AI, data science, and GenAI, stay tuned with us on Leading with Data.

Check our upcoming sessions here.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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