Free roadmap, 2026 edition

Become a Data Analyst in 6 Months, With AI at Every Step

A month by month plan: what to learn, what to build, and where to learn it for free.

  1. Month 1Excel and statistics
  2. Month 2SQL
  3. Month 3Power BI
  4. Month 4Python
  5. Month 5Machine learning
  6. Month 6AI and portfolio
  • 6months, 24 weeks planned
  • 15 to 20hours a week
  • 70%hands on practice
  • 5,000+learners trained by TFA

Six months, three phases, planned week by week

Every month has two blocks of two weeks, each with its own topics and a project. Tap a month to see the plan.

Maz The Analyst presenting the 2026 data analyst roadmapPrefer to watch?This roadmap, explained month by monthMaz The Analyst on YouTube, in Vietnamese

Month 1Phase 1, foundations

Business statistics and Excel

Build a solid base in analytics, core statistics and the Excel skills every analyst uses daily.

  • Excel
  • Google Sheets
Weeks 1 and 2

Analytics foundations and statistics

  • What data analytics is: descriptive, diagnostic, predictive, prescriptive
  • Data types: quantitative or qualitative, structured or unstructured
  • Basic statistics: mean, median, mode, variance, standard deviation
  • Inferential statistics: sampling, hypothesis testing, probability
Weeks 3 and 4

Advanced Excel and visualisation basics

  • Pivot tables, VLOOKUP, XLOOKUP, INDEX MATCH
  • Conditional formatting and data validation
  • Charts and dashboards, Power Pivot, Power Query
  • Choosing the right chart for the question

Mini projectSales performance dashboard in Excel

Month 2Phase 1, foundations

SQL, plus your first AI tools

Query databases with confidence, and let AI assistants speed up how you write and debug SQL.

  • SQL
  • ChatGPT
  • Claude
Weeks 5 and 6

SQL fundamentals

  • Database concepts: tables, rows, columns, keys
  • SELECT, WHERE, GROUP BY, ORDER BY
  • JOINs: INNER, LEFT, RIGHT, FULL
  • Aggregations: COUNT, SUM, AVG, MAX, MIN
  • Subqueries and common table expressions (CTEs)

PracticeSQLZoo and LeetCode SQL problems

Weeks 7 and 8

Advanced SQL for analysis

  • Window functions and views
  • Query performance and tuning
  • Database design for analysts
  • Data transformation and ETL with SQL
  • SQL for BI and dashboards
  • First AI help: ChatGPT or Claude for writing SQL

Mini projectAdvanced analytics use cases with SQL

Soft skills for months 1 and 2

  • Critical thinking
  • Business communication
  • Asking good questions
  • Understanding stakeholder needs

Month 3Phase 2, intermediate

Analytical thinking and Power BI

Frame the business problem first, then answer it with interactive dashboards that refresh on their own.

  • Power BI
  • Tableau
Weeks 9 and 10

Analytical thinking and problem solving

  • Problem framing and business context
  • Hypothesis driven thinking, before touching data
  • Structured problem breakdown with MECE
  • Designing an analysis plan
  • Storytelling that persuades

ProjectData analysis report for a business problem

Weeks 11 and 12

Automatic dashboards with BI tools

  • Power BI: data connections, Power Query, DAX basics
  • Interactive dashboards and reports
  • Tableau: connecting to data, building visuals
  • Dashboard design best practices
  • Publishing and sharing reports

ProjectMarketing campaign performance dashboard

Month 4Phase 2, intermediate

Python for data analysis

Clean, explore and chart data with pandas, then take your first step into machine learning.

  • Python
  • pandas
  • scikit learn
Weeks 13 and 14

pandas and NumPy

  • pandas: DataFrames, Series, indexing, filtering
  • Cleaning: missing values, duplicates, outliers
  • GroupBy and aggregations
  • Merging and joining datasets
  • NumPy arrays and vectorisation

ProjectData cleaning and EDA with pandas

Weeks 15 and 16

Visualisation and first machine learning

  • Matplotlib and Seaborn charts
  • Exploratory data analysis techniques
  • Machine learning concepts
  • Supervised and unsupervised learning
  • Simple linear regression with scikit learn

ProjectSales forecasting with linear regression

Soft skills for months 3 and 4

  • Data storytelling
  • Presenting to technical audiences
  • Turning insights into recommendations
  • Building a portfolio

Month 5Phase 3, advanced

Predictive analytics and machine learning

Build predictive models, judge them properly, and let AutoML and AI agents handle the repetitive parts.

  • scikit learn
  • ChatGPT
  • Claude
Weeks 17 and 18

Machine learning algorithms

  • Classification: logistic regression, decision trees, random forest
  • Regression: multiple and polynomial regression
  • Clustering: k means, hierarchical clustering
  • Evaluation: accuracy, precision, recall, F1 score, ROC AUC
  • Cross validation and hyperparameter tuning

ProjectCustomer lifetime value prediction

Weeks 19 and 20

AutoML and modern AI tools

  • AutoML with H2O.ai, PyCaret, AutoGluon
  • Automated feature engineering
  • Model selection and ensemble methods
  • Deploying a model, the basics
  • ChatGPT or Claude for code, debugging and insights

ProjectAutomated predictive model pipeline with AutoML

Month 6Phase 3, advanced

Industry applications and your portfolio

Apply everything to marketing, sales or risk, then turn your projects into a portfolio that gets interviews.

  • ChatGPT
  • Claude
  • Gemini
  • GitHub
Learn with TFA
AI4DA capstone
Weeks 21 and 22

Domain applications

  • Marketing: RFM segmentation, marketing mix modelling, A/B testing, attribution
  • Sales: forecasting with Prophet or ARIMA, lead scoring, pipeline and territory analysis
  • Risk: credit risk, fraud detection, churn prediction, risk scoring

Major projectAn end to end project in your chosen domain

Weeks 23 and 24

Advanced AI and portfolio building

  • Prompting strategies for data analysis
  • LLM APIs such as OpenAI and Claude for insights
  • AI powered dashboards and reporting
  • NLP basics for text analytics
  • Portfolio on GitHub, with case studies from your projects
  • Resume and LinkedIn for data roles

Final capstoneAn AI enhanced analytics solution

Soft skills for months 5 and 6

  • Executive presentations
  • Stakeholder management
  • Project management
  • Ethics in data and AI
  • Personal branding

Build a portfolio that proves you can do the job

Three domains, three levels each, all on free public datasets. Start the beginner project in month 1 and finish the advanced one in month 6.

Marketing

  1. BeginnerCampaign performance dashboardExcelPower BI
  2. IntermediateCustomer segments for targetingSQLPython
  3. AdvancedPredict who will say yes to the next campaignPythonMachine learning
DatasetBank MarketingUCI, 45,211 rows

Want more data to practise on? Read our guide to trusted free data sources.

The resources worth your time, in one place

Books, courses, channels, practice sites and AI tools, picked for the six stages above.

Combo Roadmap

Prefer a guided path? Take the whole roadmap with TFA

Six Vietnamese courses that cover every month of this plan, with exercises, trainers and a community of 4,200+ learners.

Your six courses

  1. 1Data Analytics 101Month 1Video course
  2. 2AI Powered Excel & Google SheetsMonth 1Video course
  3. 3SQL for Data AnalyticsMonth 2Video course
  4. 4Agentic AI in Power BIMonth 3Live class
  5. 5Python for Data AnalyticsMonth 4Video course
  6. 6AI Agent for Data Analytics & Decision MakingMonths 5 and 6Live class

Combo Roadmap, 6 courses

23,576,000₫

17,689,000₫

You save 5,887,000₫ compared with buying the courses one by one.

See Combo Roadmap

Get the free AI Agent toolkit

Step by step guides for using AI agents in analysis, reporting, data cleaning, finance, HR and sales. Pick the ones you need.

  1. Pick the guides you needOne guide or the whole set.
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  3. Put them to work in months 5 and 6They pair with the machine learning and AI weeks of this plan.
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