Analytics

What is Analytics?

Analytics turns raw data into useful decisions. You gather data, clean it, explore patterns, and communicate insights so teams choose the next best action. It’s used to understand customers, improve products, reduce costs, and measure outcomes.

Typical problems Analytics solves

• Which marketing channels drive the most qualified customers?
• Where do users drop off in the signup or purchase flow?
• Which products or features improve retention or revenue?
• How should we forecast demand, inventory, or staffing?
• Is a new change actually better? (A/B testing and experiments)

Where Analytics is used

• Consumer apps and SaaS to improve user experience and growth
• E‑commerce for merchandising, pricing, and conversion
• Finance/fintech for risk, fraud, and product metrics
• Healthcare and public sector for operations and outcomes
• Manufacturing/logistics for efficiency and forecasting

Who this is for

• Curious thinkers who enjoy finding patterns and asking “why?”
• People comfortable with spreadsheets and basic math (averages, percentages)
• Strong communicators who can turn numbers into clear stories
• Problem-solvers who like structured, step‑by‑step work

Tip: There’s a quick fit test on this page. Anyone can try it for free. Only logged‑in users have their progress saved.

Prerequisites

• Math: averages, percentages, ratios, basic probability
• Spreadsheet comfort (Excel or Google Sheets)
• Basic logic (IF, AND/OR, filters)
• Willingness to iterate and document your steps

Careers inside this direction

Where you can work

• Tech companies (consumer apps, SaaS, platforms)
• E‑commerce and retail (marketplaces, D2C brands)
• Finance/fintech and insurance
• Healthcare, biotech, and public health
• Manufacturing, logistics, and supply chain
• Consulting firms and analytics agencies
• Government and NGOs

Salary ranges by stage

Varies by country/company; treat as rough ranges.

• Junior: ~$45k–$70k
• Mid‑level: ~$70k–$110k
• Senior/Lead: ~$110k–$160k+

How compensation can grow

• Specialization (experimentation, finance, growth) can command higher pay
• Impact on revenue/costs often leads to bonuses
• Leadership, mentoring, and cross‑team influence accelerate growth

Growth map

Foundation → Junior

• Spreadsheets: cleaning data, pivot tables, charts
• SQL basics: SELECT, WHERE, GROUP BY, JOIN
• BI tools: build simple dashboards
• Communication: concise summaries, clear visuals

Junior → Mid‑level

• SQL proficiency: window functions, CTEs, performance basics
• Product/Business metrics: retention, funnels, cohorts, unit economics
• Experimentation: A/B test design, pitfalls, interpretation
• Stakeholder skills: scoping, prioritization, expectation management

Mid‑level → Senior/Lead

• Problem framing: from ambiguous goals to measurable questions
• Data modeling basics and documentation standards
• Mentoring, reviewing analyses, establishing best practices
• Impact focus: tying insights to decisions, forecasting impact

Tools & stack overview

• Spreadsheets: Excel, Google Sheets
• SQL databases/warehouses: PostgreSQL, MySQL, BigQuery, Snowflake
• BI/visualization: Power BI, Tableau, Looker/Looker Studio, Metabase
• Product analytics: GA4, Amplitude, Mixpanel
• Experimentation: Optimizely, LaunchDarkly, internal frameworks
• Scripting (optional but useful): Python or R for deeper analysis
• Version control & collaboration: Git, shared notebooks, docs

Try this now (5‑minute mini tasks)

• Open a spreadsheet and compute: average, median, and a simple growth rate
• Write a SQL query sketch on paper: which tables, which columns, how to join?
• Sketch a dashboard: 3 KPIs, 1 trend chart, 1 funnel, and 1 breakdown

Beginner roadmap (6 weeks)

Week 1: Spreadsheet fundamentals

• Clean data: remove duplicates, trim text, consistent dates
• Analyze: SUMIF/S, COUNTIF/S, VLOOKUP/XLOOKUP, pivot tables
• Visualize: line, bar, stacked bar; label clearly

Week 2: SQL basics

• SELECT, WHERE, ORDER BY, LIMIT
• Aggregations with GROUP BY, HAVING
• INNER vs LEFT JOIN and when to use each

Week 3: Intermediate SQL + data thinking

• Window functions (ROW_NUMBER, LAG, moving averages)
• CTEs and query readability
• Derive metrics: conversion, retention, LTV proxies

Week 4: BI dashboards

• Connect a dataset and build a KPI dashboard
• Use filters, drilldowns, and segments
• Document metric definitions

Week 5: Experimentation & product metrics

• Design a simple A/B test: hypothesis, metric, sample size rough check
• Analyze results; avoid peeking and p‑hacking
• Explain trade‑offs and next steps

Week 6: Portfolio & storytelling

• Package 2–3 analyses into short case write‑ups
• Create before/after charts; add a 5‑sentence executive summary
• Practice a 3‑minute verbal walkthrough

Learning path

• Start with spreadsheets until you can pivot and chart confidently
• Learn SQL to fetch and shape data from multiple tables
• Build dashboards that answer recurring questions
• Study product/business metrics and basic experiment design
• Create a small portfolio with clear, decision‑ready insights
• Apply for roles and keep improving via feedback

Common mistakes

• Jumping to charts without defining the question → Always write a 1‑line problem statement first
• Measuring too many KPIs → Choose 1–3 primary metrics and supporting ones
• Confusing correlation with causation → Use experiments or strong quasi‑experiments when possible
• Messy SQL → Use CTEs, clear aliases, and comments for readability
• Unclear visuals → Title, axis labels, and callouts are non‑negotiable

Mini project ideas

E‑commerce funnel deep‑dive

• Deliverables: funnel chart, top drop‑off reasons, 3 prioritized fixes
• Skills: SQL joins, cohorts, clear storytelling

Marketing channel ROI snapshot

• Deliverables: CPA/CAC per channel, simple LTV proxy, recommendation
• Skills: data cleaning, pivot tables, unit economics

Product retention analysis

• Deliverables: cohort chart, key drivers, “keep/do/try” actions
• Skills: cohorts, segmentation, visualization

Simple A/B test report

• Deliverables: hypothesis, metric, result interpretation, next step
• Skills: experiment basics, trade‑off communication

Operations dashboard

• Deliverables: KPI panel, trend chart, alert thresholds
• Skills: dashboard design, metric definitions

Next steps

• Pick a starting role from the professions section on this page
• Follow the 6‑week roadmap and complete 2 mini projects
• Create a concise portfolio and ask for feedback from peers