Savitha Namuduri

Data Science Demystified

Zeta Global

DEPT

Co-Founder of NSquare AI Academy

Trying to learn Data Science on your own can be daunting. Let us guide you through an overview of different frameworks.

Major frameworks in Data Science

Learn about some basic steps included in a Data Science project

How to do Data Science

Get an introduction to main DS techniques, and what business problems they solve

Get a view into general DS techniques and business problems

Stop being the “number explainer.” Build the stories, systems, and credibility to lead with clarity and confidence in the age of AI.

Data & AI leader,19yrs of scaling teams

Pier Martin

 Data & AI leader with 19+ years scaling global teams, Executive coach 

Cohort 5

Cohort 7

As a data leader, do you catch yourself asking questions like…- How do I get execs to actually act on my insights instead of tuning out?- How do I influence peers in Product, Ops, and Marketing when they don’t report to me? - How can I build credibility around AI adoption without just demoing tools?- How do I stop my team from being seen as a “ticket shop” and instead as a trusted partner?If you’re not confident about these, this course is for you. This is a hands-on leadership course designed for emerging data leaders, analytics managers, data science managers, and heads of data who want to grow from technical doers into visible, trusted, AI-ready leaders.Before this course, you may feel:- Stuck explaining numbers that don’t lead to action.- Anxious that AI will outpace your team or make your work irrelevant.- Reactive to hype or resistance, rather than steering the narrative.After this course, you’ll:- Turn “data dumps” into compelling stories that executives repeat in the boardroom.- Translate AI pilots into credible business cases that prove ROI, reduce fear, and win support.- Lead your team confidently through change, balancing optimism with realism.

Lead with Data. Inspire with Story. Win with AI.

How to Master Strategic Communication

How to Lead AI Change with Confidence

  How to Build Your Leadership Operating System

Folks who are early in their career, focused only on technical execution, or looking to break into data/analytics should not enroll.

This course is NOT FOR everyone

It is designed for data managers, analytics leads, and heads of data already managing teams and facing leadership challenges.

This is NOT a beginner-level technical course

Overlooked Data Manager delivering dashboards but dismissed in strategy conversations, eager to be trusted for impact.

Stretched Head of Data managing 5-15 people, stuck firefighting, and needing systems to show control and credibility.

Aspiring AI-Ready Data leader, newly promoted from IC, strong technically but lacking storytelling and executive presence.

Walk in with raw data. Walk out with the recommendation

CEO | Top Maven Instructor | 200+ alumni

Microsoft

Chevron

McKesson

Pinterest

Justin Bateh, PhD

CEO @ Projects Right Academy | Top Recognized Maven Instructor | 200+ alumni | 8x Teaching Awards

Cohort 1

SAVE $100 For The Pre-Launch, Use Code: data100The ProblemYou have the data. You can't get to the answer fast enough. Cleaning the file takes a morning. Figuring out what it's telling you takes an afternoon. Writing the summary your boss actually asked for takes another day. By the time you ship, the decision has already been made without you.What I'll show youI'll build the full workflow live in Claude, start to finish, on a real dataset. You'll watch me:<ul><li>Clean a messy dataset in under 15 minutes</li><li>Ask the right questions to surface trends, outliers, and patterns</li><li>Run customer segmentation, sales forecasting, and pricing analysis: no code</li><li>Build a decision tree to weigh options with real trade-offs</li><li>Turn the findings into an executive summary, a slide outline, and a written recommendation</li></ul>You'll leave with every prompt, template, and starter file I used. Drop them into Claude and run the same workflow on your own data.The outcomeYou stop losing your week to cleanup and formatting. You get to the answer the same day the question lands. And when you share it, it's a recommendation people can act on.No coding. No statistics background.

Operations managers, project managers, and team leads who run reports and make calls on data they didn't have time to fully analyze.

Consultants, analysts, and strategists who live in client data. You need to get to the insight faster and make it land.

Founders, CEOs, and marketing, finance, or product managers who don't have a data team and need to make the call themselves.

Master SQL for real-world data science interviews

Seasoned data scientist & educator

Atlassian

Dropbox

Google

eBay

Anirban Bhattacharyya 

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

Alpha Cohort

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

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Principal Data Scientist @ Ontra 

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Data Science Leader |Ex-Microsoft

Grammarly

Superhuman

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Head of Data @ Ontra AI | Ex-LinkedIn

LinkedIn

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

Cohort 1 is in Session, but it's not too late to enroll!! Kick off was April 20. Final registration closes April 27. Everything has been recorded so you don't miss a thing! As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship. Analytical independence - the ability to validate your own ideas and make data-informed decisions - remains the missing skill in most builders' toolkits.AI can conduct the analysis, but it can't tell you what to ask or whether the answer makes sense. That requires analytical judgment.This course teaches you to think like a senior Product Data Scientist. You'll learn the frameworks used at FAANG companies to frame problems, choose the right metrics, diagnose root causes, execute the analysis, and turn insights into decisions.-&gt; <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Join AI Builders Slack Community!!</a> &lt;-What makes this different: No SQL. No stats lectures. Five weeks of hands-on real scenarios. You'll build end-to-end, metric specs, deep dives, experiment plans, stakeholder readouts.The result: You become analytically self-sufficient. The last dependency removed from your toolkit. Make data-informed decisions in hours, not weeks.

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.