anirbanbhattacharyya

Cohort 1

Alpha Cohort

A full ML pipeline for students and educators. Data quality, EDA, hypothesis testing, model training and polished report

Seasoned data scientist with more than 10 years of experience in data science

Anirban Bhattacharyya

Turn any dataset into a working ML model in minutes, for free

Data science mentoring, resume optimization and mock interviews to help you sharpen skills and land your next role

Data Science Interview Coaching for Aspiring and Experienced Candidates

Master Causal Inference to Make Credible Decisions from Messy Real-World Data

Seasoned data scientist & educator

Atlassian

Pinterest

Google

eBay

Dropbox

Anirban Bhattacharyya 

Data scientist and educator with 10+ years in experiments and causal inference

Most data scientists are asked to answer high-stakes “what caused what?” questions long before they have a perfect A/B test: Did this feature actually improve retention? Did the campaign drive incremental revenue? Did policy changes reduce churn, or were users already trending that way?This workshop helps you move beyond surface-level correlations and build the judgment to make credible causal claims from messy, real-world data. You’ll learn how to frame business questions as causal questions, choose the right method when experimentation is limited, identify common sources of bias, and communicate assumptions clearly to stakeholders.By the end, you’ll have a practical toolkit for making better product, growth, marketing, and policy decisions when randomized experiments are unavailable, delayed, or incomplete

Data scientists &amp; analysts who need to answer “what caused what?” using messy product, growth, or business data.

Product, growth &amp; marketing teams making decisions when clean A/B tests are unavailable, delayed, or incomplete.

Students &amp; early-career data pros who want practical causal inference skills for interviews and real-world data work.

Master SQL for real-world data science interviews

Seasoned Data Scientist with 10+ years solving product analytics at scale.

Many aspiring data scientists know basic SQL, but struggle when faced with the kind of messy, ambiguous, business-driven questions that come up in interviews. The challenge is rarely just writing syntax. It is knowing how to translate a product question into the right metric, choose the right grain of analysis, join data correctly, avoid double counting, and explain your reasoning clearly.That gap can hold back otherwise strong candidates. You may understand SQL concepts in theory, but still feel stuck when asked to calculate retention, analyze an experiment, or debug a flawed query in real time.This workshop is designed to close that gap. Instead of teaching SQL as a list of commands, it teaches SQL the way it is actually used in data science interviews: through realistic product, growth, and analytics problems. Students will build confidence solving hands-on exercises that reflect real interview settings, from fundamentals to advanced topics like window functions and recursive CTEs.The goal is to help students move beyond memorizing syntax and become interview-ready problem solvers who can write clean SQL, reason through edge cases, and communicate like strong data scientists.

Aspiring Data Scientists Learners preparing for data science and analytics interviews who want to build strong, practical SQL skills.

Analysts Moving Upmarket Data and business analysts who know basic SQL and want to tackle more advanced, interview-style problems.

Product-Focused Data Professionals Data scientists and product analysts who want to sharpen SQL for funnels, retention, experimentation

Crack SQL for Data Science Interviews

Solve interview-style SQL problems confidently by breaking down ambiguous questions into clear query logic.

Apply advanced SQL patterns including joins, CTEs, window functions, aggregations, funnels, cohorts, retention analysis

Communicate analytical reasoning clearly by explaining assumptions, edge cases, metrics, and tradeoffs

Causal Inference for Real-World Decision Making

Learn when to use DiD, IV, RDD, matching, or synthetic control for real business questions.

Choose the Right Causal Method for the Problem

Spot weak assumptions, run diagnostics, and identify failure modes before trusting results

Validate Assumptions and Diagnose Failure Modes

Present causal findings clearly, explain caveats, and make recommendations stakeholders can act on

Learn directly from anirbanbhattacharyya

anirbanbhattacharyya

Workshops

Causal Inference for Real World Decision Making

SQL for data science interviews

Lightning Lessons

Crack SQL for Data Science Interviews

Causal Inference for Real-World Decision Making

Services

Data Science Interview Coaching for Aspiring and Experienced Candidates

Resources

Turn any dataset into a working ML model in minutes, for free