v2 · Industry Edition · 2026–27 Ready

Data Analytics × GenAI × Cloud

~11–13 months · 10–15 hrs/week · Free-first stack (paid options marked)

🎯 Data Analyst 🎯 Business Analyst 🎯 BI / Insights Analyst
Setup→ Sheets+Excel Pro→ SQL→ Python→ Stats & Real Analytics→ BI & Storytelling→ GenAI ⭐→ Cloud & Data Stack ☁️→ Pro Skills 🏭→ Portfolio 🚀
Language Tool Library Platform GenAI/Cloud Practice ✅ Free 🟡 Free tier/trial 💲 Paid
0

Kickoff & Setup Week 1

Understand the role + build your free workspace

Topics

  • DA vs BA vs BI analyst — what differs
  • How companies actually use data (reports, KPIs, decisions)
  • Structured vs unstructured data

Tools

GitHub ✅Kaggle ✅ Google Colab ✅LinkedIn ✅
MilestoneEnvironment ready + study schedule locked
1

Spreadsheets — Industry Level Months 1–2

Companies still live in Excel. Power Query + Power Pivot is the industrial edge.

Topics

  • XLOOKUP, INDEX-MATCH, SUMIFS, IF, TEXT funcs
  • Pivot tables + pivot charts
  • Power Query: import, clean, merge, refresh workflows
  • Power Pivot: data model + basic DAX in Excel
  • Descriptive stats: mean, median, std dev, outliers
  • Conditional formatting & data validation

Tools

Google Sheets ✅Excel Online ✅ LibreOffice ✅

Practice

GCFGlobal ✅ExcelJet ✅ Leila Gharani YT ✅
MilestoneMessy multi-sheet workbook → cleaned, refreshable Power Query report
2

SQL — The #1 Analyst Skill Months 2–3.5

Every analyst job requires it. Industrial reality = joins across 5+ tables, millions of rows.

Core Topics

  • SELECT, WHERE, GROUP BY, HAVING, ORDER BY
  • All JOIN types + self joins, UNION
  • CTEs, subqueries, readable formatting
  • Window functions: ROW_NUMBER, RANK, LAG/LEAD, running totals
  • Date/time & string manipulation, CASE WHEN
  • Keys, schemas, primary/foreign keys

Industrial Extras

  • Query performance basics: filters early, avoid SELECT *
  • Indexes & partitioning (awareness)
  • Handling dirty data: NULLs, duplicates, bad keys
  • Writing SQL others can read (comments, CTE naming)

Tools & Practice

PostgreSQL ✅SQLite ✅ DBeaver ✅BigQuery Sandbox ✅ DataLemur 🟡StrataScratch 🟡 SQLBolt ✅
MilestoneSolve medium–hard SQL interview questions using CTEs + window functions
🏭 Industry tip: practice on datasets with 1M+ rows (BigQuery public datasets) — toy DBs don't build real skills
3

Python for Data Analysis Months 3.5–5.5

Automation, heavy cleaning, APIs — plus the foundation for GenAI work

Topics

  • Python basics: types, loops, functions
  • pandas: filter, groupby, merge, pivot, clean
  • Reading CSV / Excel / JSON / Parquet
  • EDA method (questions → explore → insights)
  • Visualization: matplotlib, seaborn, Plotly
  • Git & GitHub version control (start here, use forever)

Industrial Extras

  • Pull data from REST APIs (requests + JSON) — real analysts do this weekly
  • Automating boring reports (scripts + scheduled runs)
  • Environment basics: venv, pip, requirements.txt
  • Connecting Python ↔ SQL databases

Platforms

Google Colab ✅Jupyter ✅ VS Code ✅Kaggle Notebooks ✅
MilestonePull live data from an API → clean in pandas → save report automatically
4

Statistics + Real-World Analytics Methods Months 5–6.5

These named analyses are what interviewers mean by "real experience"

Stats Core

  • Distributions, sampling, bias
  • Hypothesis tests, p-values, confidence intervals
  • A/B testing design & analysis
  • Linear & logistic regression
  • Correlation vs causation, Simpson's paradox

🏭 Industry Analyses (must-know)

  • Cohort retention analysis
  • Funnel conversion analysis
  • Customer churn analysis
  • RFM customer segmentation
  • Time-series trend + forecasting basics
  • Month-over-month / YoY comparisons

Libraries & Learning

SciPy ✅statsmodels ✅ scikit-learn ✅Prophet ✅ StatQuest YT ✅Khan Academy ✅
MilestoneBuild a cohort retention heatmap + explain findings to a "non-technical" friend
5

BI Dashboards & Data Storytelling Months 6.5–8.5

Dashboards get interviews; storytelling gets offers. Data modeling is the hidden skill.

Topics

  • Dashboard UX & design principles
  • Data modeling: star schema, fact & dimension tables (Kimball basics)
  • DAX measures vs calculated columns
  • Row-level security (RLS)
  • Scheduled refresh & incremental loads
  • KPI definition + executive summaries

Tools

Power BI Desktop ✅DAX ✅ Tableau Public ✅Looker Studio ✅ Canva ✅PowerPoint ✅

Practice

Maven Analytics 🟡Workout Wednesday ✅
Milestone2 polished dashboards with proper star-schema data models, published online
🏭 Industry tip: 60% of BI work is data modeling & refresh setup, not visuals — show that in projects
6

GenAI for Analysts ★ 2026/27 Differentiator

Analysts fluent in AI + analysis will outcompete those who aren't

Topics

  • Prompt engineering: structured, few-shot, iterative
  • LLM basics: tokens, context, temperature
  • Embeddings & semantic search
  • RAG: chunk → embed → retrieve → answer
  • Text-to-SQL / chat-with-your-data
  • Structured JSON outputs for reports
  • Hallucination checking — verify everything
  • Responsible AI: PII, privacy, bias

Tools & Libraries

Gemini API free tier ✅Groq free tier ✅ Ollama (local) ✅Hugging Face ✅ LangChain ✅LlamaIndex ✅ ChromaDB ✅FAISS ✅ Copilot in Power BI/Excel 🟡
MilestoneBuild "Chat with your Data": text-to-SQL or RAG over real business docs
💡 Use AI daily from Phase 1 — but never present output you can't explain
7

Cloud & Modern Data Stack Months 8.5–10 New · Industry Critical

Analyst job listings increasingly say: BigQuery, Snowflake, Fabric, or AWS. Pick ONE cloud deeply, others at awareness level.

Concepts (cloud-agnostic)

  • ETL vs ELT
  • Data warehouse vs lake vs lakehouse
  • Columnar storage, pay-per-query billing
  • Orchestration & scheduled pipelines (Airflow — awareness)
  • Data governance, access control, cost awareness

Recommended Path

  • Primary: BigQuery — most generous free tier, SQL-native
  • Transformation: dbt — the industry standard
  • Secondary awareness: Snowflake, Fabric, AWS (S3/Athena)
  • Ingestion: Airbyte (open-source)
MilestoneLoad a Kaggle dataset into BigQuery → transform with dbt → visualize in Looker Studio — a full cloud pipeline
8

Industrial Professional Skills Parallel · Ongoing What jobs actually demand

The "soft" skills that are actually hard requirements in industry

Work Practices

  • Requirement gathering: turning vague asks ("why did sales drop?") into defined analyses
  • Agile/Scrum basics: sprints, tickets, Jira
  • Stakeholder management & expectation setting
  • Presenting to non-technical leadership

Data Responsibility

  • Data QA: validate before sending to leadership (career-saving)
  • Documentation: data dictionaries, READMEs, query comments
  • Privacy: PII handling, GDPR/CCPA awareness, access controls
  • Knowing when to escalate to data engineers

Tools

Git/GitHub ✅Jira free plan ✅ Notion/Confluence ✅PowerPoint ✅ Slack/Teams ✅
MilestoneWrite a one-page "requirements → approach → findings" doc for each portfolio project
9

Portfolio, Certifications & Job Hunt Month 10 → Hire 🚀

3–5 end-to-end projects beat 20 tutorial clones

Project Formula

  • Business question → SQL (cloud DB) → Python analysis → dashboard → GenAI layer → written insights
  • Use a real industry scenario (sales, marketing, ops)
  • GitHub READMEs that tell the story

Certifications

Kaggle certs ✅ MS Learn paths ✅ DP-900 — often free via MS Virtual Training Days PL-300 Power BI 💲 Google/AWS Cloud certs 💲
MilestonePortfolio live · LinkedIn optimized · 5+ applications/week · mock interviews done

🔄 The Modern Data Stack — how companies actually move data

1. Sources

CRM (Salesforce), ERP (SAP), web analytics (GA4), apps, APIs, CSVs

→
2. Ingestion

Airbyte ✅
Fivetran 🟡
(ETL/ELT tools)

→
3. Warehouse

BigQuery ✅
Snowflake 🟡
Fabric 🟡 / Redshift 💲

→
4. Transform

dbt ✅
SQL models, tests, docs

→
5. BI Layer

Power BI ✅
Tableau/Looker
Metabase ✅

→
6. AI Layer

GenAI copilots, text-to-SQL, RAG apps ⭐

Knowing this pipeline — even at awareness level — puts you ahead of 80% of entry-level analyst candidates

☁️ Cloud Platforms — free vs paid status

Google Cloud (GCP) Best free tier

Recommended primary cloud — SQL-native, no credit card needed

BigQuery Sandbox ✅ Free (1TB query/mo, 10GB storage) Looker Studio ✅ Free $300 credit trial 🟡 (90 days) Cloud Skills Boost 💲 (some free labs)
Focus: BigQuery SQL, datasets, cost control, Looker Studio dashboards

Microsoft Azure / Fabric Best for MS shops

Huge in enterprise — Power BI users are everywhere

Power BI Desktop ✅ Free Microsoft Learn + sandboxes ✅ Free Fabric trial 🟡 60 days Azure free account 🟡 $200 credit Azure for Students ✅ $100, no card (student email)
Focus: Power BI Service, Fabric lakehouse concepts, OneLake awareness

AWS Most job listings

Awareness level for analysts; deeper for analytics engineering

Free Tier 🟡 12 months (S3, Lambda…) Athena 💲 pay-per-query (~$5/TB — pennies for practice) Redshift / QuickSight 💲 (use trial credits) AWS Skill Builder ✅ free courses AWS Educate ✅ free for learners
Focus: S3 + Athena querying, Glue catalog awareness, QuickSight trial

Snowflake Trial-based

Massive in enterprise data teams — learn the concepts

30-day trial 🟡 $400 credits Snowflake University ✅ free courses + badges After trial 💲 (or re-trial with new email — common practice)
Focus: Warehouses, time travel, cloning, Snowflake SQL dialect differences

Databricks Free edition

Lakehouse leader — notebook + Spark skills transfer everywhere

Databricks Free Edition ✅ (successor to Community Edition) Databricks Academy ✅ free learning paths
Focus: Notebooks, Delta Lake awareness, SQL warehouses — optional bonus

Open-Source Stack 100% free

Great for portfolio projects without cloud billing worries

dbt Core ✅ / dbt Cloud dev seat ✅ Airbyte OSS ✅ Apache Airflow ✅ (awareness) Metabase OSS ✅ Apache Superset ✅
Focus: dbt is the single highest-ROI free tool here — learn it properly
💡 Strategy: BigQuery (deep) + Power BI (deep) + dbt (working knowledge) covers most 2026/27 analyst job descriptions. Everything else = awareness level for interviews.

🏭 Reality Check — what a real analyst's week looks like

📊 35% — SQL & queries

Multi-table joins, ad-hoc requests, building datasets for stakeholders. This is the core job.

📈 25% — Dashboards

Building AND maintaining. Fixing broken refreshes, updating models, adding requested fields.

💬 20% — Meetings & requirements

Understanding what people actually need (they often ask for the wrong thing). Framing questions.

🧹 10% — Data cleaning & QA

Missing values, duplicates, bad CRM entries. Always validate before leadership sees numbers.

🎤 10% — Presenting

Executive summaries, PowerPoint decks, answering "so what does this mean for us?"

🤖 Growing % — GenAI workflows

Drafting SQL with AI, summarizing findings, building chat-with-data tools — with human verification.

🏢 Domain KPIs — pick 1–2 industries and learn their language

📣 Marketing

CAC · ROAS · CTR · conversion rate · MQL→SQL · attribution

💼 Sales

Pipeline · win rate · ACV · quota attainment · sales cycle length

📱 Product

DAU/MAU · retention curves · churn · funnel drop-off · NPS

💰 Finance

Revenue · margins · MRR/ARR · forecast variance · burn rate

📦 Ops / Supply Chain

Inventory turnover · fill rate · OTIF · lead time · defect rate

👥 HR (People Analytics)

Attrition · time-to-hire · headcount planning · engagement

Speaking a domain's KPI language in interviews instantly signals "real-world ready"

🧰 Complete Stack Cheat Sheet

Languages

SQL · Python · DAX · (R optional)

Spreadsheets

Excel + Power Query + Power Pivot · Google Sheets

Python Libraries

pandas · NumPy · matplotlib · seaborn · Plotly · SciPy · statsmodels · scikit-learn · requests · Prophet

BI & Viz

Power BI · Tableau Public · Looker Studio · Metabase · Superset

Cloud

BigQuery ✅ · Fabric 🟡 · Snowflake 🟡 · AWS 🟡 · Databricks ✅

Data Stack

dbt ✅ · Airbyte ✅ · Airflow (awareness) ✅

GenAI

Gemini API ✅ · Groq ✅ · Ollama ✅ · LangChain ✅ · ChromaDB ✅ · Hugging Face ✅

Practice & Data

Kaggle · DataLemur · StrataScratch · SQLBolt · BigQuery public datasets · data.gov

Pro Skills

Git/GitHub · Jira · Notion/Confluence · PowerPoint · LinkedIn

☁️ One cloud, done wellBigQuery deeply + awareness of Snowflake/Fabric/AWS beats shallow exposure to all four.
🏭 Name your analysesPortfolio projects titled "Cohort Retention Analysis" or "Churn Deep-Dive" match real job tasks better than "My First Dashboard".
📝 Document everythingEvery project gets a README + requirements doc — it doubles as interview evidence of professional skills.
🛠️ 70/30 rule70% building, 30% tutorials. Tutorials feel productive; projects get you hired.
🌐 Build in publicPost dashboards, dbt models, and AI experiments on LinkedIn — recruiters scout there weekly.
🤖 AI assists, you verifyUse GenAI daily — but never present output you can't explain line by line.

Start Phase 0 today. By 2026/27 you'll have cloud + GenAI + real analytical methods — a combination most candidates won't match. 🚀