Roadmap · Specialise
Data analytics (Tableau)
Opens with creditsFor students and early-career engineers who want to turn data into decisions as a data or BI analyst, whether you start from Excel and Power BI, Tableau or Python. By the end you can take a vague business question, answer it with spreadsheets, SQL, basic statistics and your chosen tool, and present a clear recommendation backed by a portfolio of real projects.
Curated by Tauseef Fayyaz8 stages, 53 topics~11 months at a few hours a weekBy the end, you will be able to
- Turn a vague request into a specific business question with written metric definitions
- Clean and summarise data in Excel or Google Sheets with lookups, conditional aggregation and pivot tables
- Write SQL with joins, CTEs and window functions for rankings, running totals and cohorts
- Explain averages, spread, correlation and an A/B test result with a confidence interval in plain words
- Build a trustworthy model and dashboard in Power BI, Tableau or Python that reconciles with an independent total
- Present a finding as a one-sentence message with clean charts, and publish two or three portfolio projects
The path
8 stages, in the order to learn them. Open one to start.
1How analysts thinkWhat analysts actually do, and the habits that come before any tool: asking a sharp business question, defining metrics precisely, knowing which kind of analysis you are doing and handling data responsibly.6 topics · ~2 weeks
- 1What a data analyst does all dayA data analyst answers business questions with data.
- 2Asking the right business questionMost requests arrive vague: "Can you look at sales?" Before touching any data, turn it into a specific question tied to a decision.
- 3Metrics, KPIs and precise definitionsA metric is a number with a precise definition.
- 4Descriptive, diagnostic, predictive, prescriptiveAnalysis comes in four broad kinds.
- 5The analysis workflow and tidy dataMost analyses follow the same loop.
- 6Personal data, privacy and responsibilityAnalysts often see personal data: names, phone numbers, emails, addresses, salaries, health or payment details.
6 in this stageOpen this stage2Spreadsheets properlyExcel or Google Sheets used like an analyst: structured tables, conditional aggregation, XLOOKUP, text and date cleaning, pivot tables and a workbook layout anyone can audit. Every analyst uses spreadsheets, whatever tool they chose.6 topics · ~5 weeks
- 1References and structured tablesFormulas only work at scale if you understand references.
- 2SUMIFS, COUNTIFS and IF logicSUMIFS, COUNTIFS and AVERAGEIFS add up, count or average only the rows that meet your conditions.
- 3Lookups with XLOOKUPAnalysis often needs a value from another table: a customer's region, a product's category, a price on a certain date.
- 4Cleaning text, numbers and datesReal exports are messy.
- 5Pivot tables for exploringA pivot table summarises a table for you.
- 6A workbook other people can trustA spreadsheet is only useful if someone else can follow it.
6 in this stageOpen this stage- SQL for analysisQuerying data where it lives: filtering and aggregating, joining tables without inflating totals, handling NULLs, CTEs for readable steps and window functions for rankings, running totals and period comparisons. Analysis-focused; the SQL and databases roadmap goes deeper into design and performance.7 topics · ~7 weeksOpens with credits
- Statistics you needThe statistics analysts use every week, at the level of intuition and honest reporting: averages and spread, distributions and outliers, sampling bias, correlation versus causation, and reading an A/B test with a confidence interval.6 topics · ~5 weeksOpens with credits
- Tableau in depthTableau from data to dashboard: connecting and relating tables, dimensions and measures, calculated fields, LOD expressions and table calculations, interactive dashboards, and publishing on the free Tableau Public.6 topics · ~2 monthsOpens with credits
- Cleaning and modelling dataTurning messy, real public data into tables you can trust: profiling, missing values, duplicates and keys, types, consistent categories, the grain of each table, a star schema, ratio measures and reconciling totals.7 topics · ~5 weeksOpens with credits
- Charts, dashboards and storytellingMaking your analysis land: choosing the right chart, removing clutter, using colour and accessibility well, designing dashboards people use, and presenting a finding to stakeholders as a clear story with an honest recommendation.7 topics · ~6 weeksOpens with credits
- Portfolio and first analyst roleProving you can do the work: two or three portfolio projects on public data with clear write-ups, a resume that shows results, the usual analyst hiring rounds, adjacent and internal routes into analysis, and freelance data work without the traps.8 topics · ~2 monthsOpens with credits
6 more stages, 41 more topics, free with an account.
You have seen the open stages of the Data analytics roadmap. Every topic it covers is listed above, and an account opens the rest: what each topic means, a question to check yourself, the courses, the practice and Tauseef's advice for every stage. Early access is open: sign up and your account is active shortly after.
Everything here is free to learn from. A few things, like adding a roadmap, keeping a cheat sheet or asking Tauseef a question, use credits. You get 15 when you join, your first roadmap is free, and if you are learning seriously and run out, you can ask for more. Credits only make sure the platform goes to people who are actually learning.
Related on Sefism
Picked from the other libraries by shared topics.
Best practices
See all- Data Pipeline Best PracticesHow to build data pipelines you can re-run, test and trust: idempotent tasks, orchestrator hygiene, layered transformations, data tests, freshness, ownership and monitoring.
- Data Analysis Best PracticesAn analyst's workflow in SQL, pandas and Polars: framing the question, reading data correctly, joins and grain, aggregation pitfalls, reproducible notebooks, checking results and sharing findings.
- Data Modeling Best PracticesDimensional data modeling for analytics: declaring the grain, fact and dimension tables, slowly changing dimensions, dbt marts, naming, tests, contracts and a semantic layer.
Checklists
See all- Data Quality ChecklistA data quality checklist for warehouse tables: ownership, freshness, volume, schema, nulls, uniqueness, referential checks, anomaly monitoring and data incidents, with how to verify each.
- Analytics Dashboard ChecklistAn analytics dashboard checklist for launch day: purpose and audience, metric definitions, data freshness, correctness, performance, access, accessibility, ownership and adoption.
- ML Model Deployment ChecklistAn ML model deployment checklist: baselines, training and serving consistency, validation, model registry, canary rollout, rollback, drift monitoring and ownership, with how to verify each.
Project structures
See all- dbt project project structureA dbt project that turns raw tables into tested, documented models, using the staging, intermediate and marts layers from dbt's own structure guide. It runs on DuckDB on your laptop, so you can learn analytics engineering for free, and grows into a governed project with checks, contracts, slim CI and a hosted docs site that moves to BigQuery or Snowflake unchanged.
- Apache Airflow project structureFor data engineers scheduling real pipelines with Airflow 3. It starts from
astro dev init(dags, include, plugins, tests and a Runtime Dockerfile that runs Airflow locally in Docker) and grows into dags grouped by business area, logic in a tested include package, asset-driven scheduling, data quality checks, alerts, CI that parses and tests every dag, and dag-only deploys. A self-hosted docker compose and Helm route is shown alongside. - GenAI RAG app (FastAPI) project structureFor developers turning a "chat with your documents" demo into an app people can rely on. A FastAPI backend where every layer has a home: ingestion workers, retrieval, prompts as versioned files, model clients with fallbacks and cost caps, guards, evals that gate CI, and tracing, so a wrong answer can be traced to the step that caused it.
Career questions
See all- What is the difference between a data analyst, a data scientist and a machine learning engineer?Analysts answer business questions with SQL, spreadsheets, dashboards and reports. Data scientists add statistics, experiments and predictive models. ML engineers build and run models inside production software. Titles vary by company, so read the work in the job advert, and pick by the kind of problem you enjoy, not the title that sounds most senior.
- Should I learn Python or R for data science?For most people aiming at industry jobs, learn Python first: it covers analysis, machine learning and production code, and most data job adverts ask for it. Choose R first if you are heading into statistics, academic research, public health or survey work, where it is strong. Either way, SQL comes too, and the concepts transfer.
- How do I move into AI engineering, data science or DevOps?Pick the field by the work you want to do every day, not by the hype. Use what you already know as a bridge: developers usually reach AI engineering or DevOps fastest, analysts reach data science fastest, and a portfolio of one or two serious projects matters more than certificates.
Cheat sheets
See allWork with me
Stuck on something specific?
Writing only gets you so far. If you want an answer to your situation rather than the general case, book a session and we will work through it together. Sessions are free for approved Sefism members, and a few slots open each week.
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