Data Analytics
Turn messy data into decisions — SQL depth, statistical judgement and narrative clarity.
- Mode
- Online
- Level
- Beginner to intermediate
- Certificate
- Verifiable
- Stack
- 6+ tools
Every company says it's "data-driven" and almost none of them have enough people who can actually turn data into a decision. That gap is the opportunity — this program trains you to be the person stakeholders trust with the number.
What this program actually does.
Every company drowns in data and starves for decisions. This program trains you to close that gap: pull the right data with SQL, ask it the right questions, and present an answer a non-technical stakeholder will actually act on.
You'll work with data that's realistically messy — missing values, weird outliers, ambiguous definitions — because that's the data you'll actually get on the job, not the clean CSV from a tutorial.
Who should join
- Students entering analytics roles
- Business teams working with data
- Career switchers from non-tech roles
- Founders who need to read their own numbers
Roles this prepares you for
Structured phases, built to compound. Here's exactly what you'll learn.
SQL depth
Joins, window functions, CTEs and query performance.
- Write joins and CTEs that stay readable as queries get complex
- Use window functions for running totals, ranks and cohort logic
- Spot and fix a query that's slow because of how it's written
Analysis
Exploratory analysis, statistics and experiment reading.
- Explore a new dataset systematically instead of poking at random
- Apply core statistics without misusing significance or correlation
- Read someone else's experiment results critically, not at face value
Modelling
Metric design, cohorts, funnels and retention.
- Design metrics that can't be quietly gamed by the team being measured
- Build cohort and funnel views that reveal where users actually drop off
- Model retention in a way leadership can track quarter over quarter
Experimentation
A/B testing fundamentals, significance and common pitfalls.
- Design an A/B test with a real hypothesis and success metric
- Avoid the classic pitfalls: peeking early, underpowered tests, novelty effects
- Communicate a null result as clearly as a positive one
Storytelling
Dashboards, executive summaries and stakeholder review.
- Build a dashboard people actually open more than once
- Write an executive summary that leads with the decision, not the method
- Defend an analysis in a live stakeholder review
Data quality
Cleaning, validation and building trust in a dataset.
- Diagnose why two reports disagree on "the same" number
- Build validation checks that catch bad data before it reaches a dashboard
- Document a dataset well enough that the next analyst trusts it
Technology stack
Projects
- Revenue and retention analysis on a product dataset
- Marketing funnel diagnostic
- A/B test readout with a recommendation
- Executive dashboard with commentary
Learning outcomes
- Answer ambiguous business questions with data
- Design trustworthy metrics
- Read an experiment result correctly
- Present findings that drive action
Online · Live mentorship · Beginner to intermediate
Data Analytics questions.
Ready to start the Data Analytics track?
Talk to our team about the track, internship or institutional program that fits where you are today.