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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

Inside the Minds of Swiggy, Meta, Uber with David Zakkam

Nitika Sharma
Nitika Sharma Last Updated : 27 Dec, 2023
3 min read

In this Leading with Data session, meet David Zakkam, a leader with 19+ years of experience. David held key roles at Swiggy, Meta, and Uber, currently serving as Uber’s Director of Data Science. He shares insights on data science’s dynamic role in tackling challenges, optimizing customer experiences, and navigating crises like COVID-19. David’s journey, transitioning and solving complex problems, provides valuable perspectives for data enthusiasts and industry pros.

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 from our Conversation with David Zakkam

  • Transitioning from consulting to product companies offers a more integrated and impactful role in applying data science to business.
  • During crises like COVID-19, data science can play a pivotal role in real-time decision-making and recovery.
  • Customizing customer experiences through data-driven insights can significantly enhance engagement and growth.
  • Integrity work in social media platforms involves complex, adversarial problems that require constant vigilance and rapid response.
  • The future of data science in mobility includes improving customer and driver experiences, integrating services, and leveraging AI for creative solutions.

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

Now, let’s look at David Zakkam’s responses to the questions asked in the Leading with Data.

How did your journey in data science begin, and what were your early days like?

My professional life can be divided into three distinct phases: 5 formative years, a decade of data science consulting, and the last 5 years in tech companies. I started as a biochemical engineering graduate from IIT Delhi, working on computational biology, which you could think of as data science for biology. Post-MBA, I transitioned into tech, and after a stint in sales, I formally moved into the data science profession.

What was the transition like from consulting at Mu Sigma to working at product-focused companies like Swiggy?

The transition was exhilarating. In consulting, you don’t have the same level of company integration to make impactful changes. In a product company, you’re part of the entire journey, working with various teams to ensure data science is effectively applied to business. The end-to-end ownership brings higher responsibility and satisfaction. My broad experience was invaluable, especially when dealing with complex, unsolved problems.

Can you share an interesting problem you tackled at Swiggy during the COVID-19 lockdown?

When the lockdown hit, Swiggy’s business dropped by over 90% overnight. We formed a 24/7 WhatsApp group with top company executives to address the crisis. We tackled a range of issues, from understanding district-level lockdown interpretations to tracking migration patterns of our workforce, which impacted our market share. These efforts helped us return to pre-COVID levels within six months.

How did Swiggy use data science to optimize customer experience and restaurant growth?

We used analytics to customize coupons based on customer behavior, encouraging them to increase their order value or frequency. For restaurants, we built a tool to simulate and optimize their spend on various promotional options, providing them with actionable insights to grow their business.

What were the challenges and exciting aspects of working on content integrity at Meta?

At Meta, we dealt with various forms of inappropriate content and behavior, from fake accounts to harmful interactions. The integrity team, consisting of thousands of engineers and data scientists, used sophisticated measurement and sampling techniques to improve our classifiers. The challenge was the adversarial nature of the problems, where attackers constantly evolved their tactics, requiring us to be agile and responsive.

What kind of data science problems are you currently working on at Uber?

At Uber, I lead teams focused on mobility growth, new verticals like high-capacity vehicles and rentals, driver and courier quality, and merchant growth on the delivery side. We’re working on enhancing customer and driver experiences, improving reliability, and ensuring seamless integration of services like taxis with Uber’s platform.

What does the future hold for your team at Uber, and what are your thoughts on generative AI?

While the current hiring plans are uncertain, the long-term goal is to grow the data science team in India to match the 30% tech presence. As for generative AI, I see its potential in creative use cases where it can generate meaningful content. However, most business problems today are deterministic and require optimization techniques rather than creativity.

Summing Up 

David Zakkam’s data science journey, from computational biology to impactful tech roles, tells a compelling story. His experiences highlight data science’s transformative power in critical business decisions, especially during crises. Navigating Swiggy’s challenges in the COVID-19 lockdown, addressing content integrity at Meta, and leading data-driven solutions at Uber, David’s insights reveal diverse data science applications.

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

Check our upcoming sessions here.

AIArtificial Intelligencedata scienceDavid ZakkamGenerative AILeading With Datamachine learningMetaSwiggyUber
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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