Archive - Tech Growth Series: PrepVector

AutoGen Unpacked: Conversational Multi-Agent Collaboration for AI Workflows - Part 3

In Part 3 of the AutoGen series, we dive into how multiple AI agents can collaborate seamlessly through structured conversation.

Jul 17 •

and

Part 3A: Measuring the Impact of Free Shipping: a Diff-in-Diff Case Study

Can free shipping truly drive growth? In this case study, we use Difference-in-Differences to measure its business impact.

Jul 16 •

and

Banani Mohapatra

CrewAI Demystified: Building Role-Based Autonomous Agent Teams - Part 2

Understand CrewAI’s core concepts and how to build role-based agent teams. We’ll cover common use cases and practical setup tips.

Jul 15 •

and

A Practical Guide to Agentic Frameworks - Part 1

Discover how modern AI agents are evolving from simple prompts to autonomous systems & why agentic frameworks like LangChain, CrewAI, & AutoGen are the…

Jul 10 •

and

Part 2: From Business Question to ML Problem — Scoping It Right

Turning vague goals into actionable ML problems starts with the right framing. In this post, we break down how to scope business questions into precise…

Jul 8 •

,

, and

Hands-on Guide to Building Real World ML Systems - Part 1

A practical, step-by-step guide to designing and building machine learning systems that work in real-world settings.

Jul 2 •

,

, and

June 2025

Part 2: Implementing Difference-in-Differences (DiD): A Practitioner’s Guide

Learn the practical mechanics of DiD - how to structure your data, validate assumptions, run fixed effects models, and communicate causal impact…

Jun 24 •

and

Banani Mohapatra

Level Up Your Data Game: 15 Pandas Functions Data Scientists Need to Master for Interviews [Part 2]

From aggregations and joins to time series transformations and feature engineering, this guide covers the must-know techniques to ace your next data…

Jun 19 •

,

, and

Sai Kumar Bysani

From Raw Data to Insights: How AI Agents Are Reshaping the Data Workflow

AI agents are transforming how businesses interact with data—automating everything from querying to insight generation—reshaping how teams work with…

Jun 18 •

Coding Under Pressure Isn’t the Same as Coding at Work

Why even strong data scientists struggle in interviews, and how to build the coding and communication skills to get through them with confidence.

Jun 17 •

Part 1: Introduction to Difference-in-Differences (DiD): A Guide for Data Scientists

Learn how to use DiD to estimate causal effects when A/B testing isn’t possible — with a focus on intuition, assumptions, and real-world applications.

Jun 12 •

and

Banani Mohapatra

Level Up Your Data Game: 15 Pandas Functions Data Scientists Need to Master for Interviews

Master the 15 most important Pandas functions every data scientist should know—complete with code, use cases, and interview-ready tips.

Jun 11 •

,

Sai Kumar Bysani

, and

#nojs-banner { position: fixed; bottom: 0; left: 0; padding: 16px 16px 16px 32px; width: 100%; box-sizing: border-box; background: red; color: white; font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; font-size: 13px; line-height: 13px; } #nojs-banner a { color: inherit; text-decoration: underline; } This site requires JavaScript to run correctly. Please turn on JavaScript or unblock scripts