Data Analyst Roadmap
Answer questions with data: SQL, pandas and statistics, then charts that make a point.
Three free learning paths aimed at answering questions with data rather than building models. SQL first for five weeks, from a plain SELECT through joins, subqueries, window functions, schema design, and an analytics project on a public dataset. Data Science Foundations for five weeks: what the field is, Python basics for analysis, pandas and NumPy, statistics you will actually use, and visualisation. R for six weeks, syntax for statistical computing, the tidyverse for data wrangling, and ggplot2 for charts. By the end you can pull data, clean it, test whether a difference is real, and make a chart that argues a point.
This is a shorter roadmap at sixteen curated weeks. At ten hours a week, expect eighteen to twenty two weeks. At twenty hours a week, ten to twelve. Data analyst is often the fastest software-adjacent role to reach from zero, four to seven months for someone who is comfortable with spreadsheets and willing to learn SQL properly. The bottleneck is rarely the tools. It is learning to ask a question that data can answer and to say clearly what the answer was.
Do SQL first and do not rush it. SQL is the single most tested skill in analyst interviews and the one you will use every day. Foundations next, because pandas is easier once you think in tables and filters from SQL. R last, and treat it as depth on statistics and visualisation rather than a replacement for the earlier tools. Build the analytics project in the SQL path and the visualisation work in Foundations to a standard you would put in front of a manager, since that is exactly what the job is.
This suits people who like finding the story in a messy spreadsheet, who are comfortable being the person who says the popular idea is not supported by the numbers, and who want a role that touches product, marketing, and finance rather than sitting only in engineering. It is a poor fit if you want to build machine learning systems, which is the data scientist or AI engineer track, or if you dislike stakeholder conversations, because half of analysis is explaining it to someone who did not do it.
What this roadmap covers
SQL
5 WeekGo from your first SELECT to real analytics: joins, subqueries, window functions, designing your own schema, and a full analytics project on a real public dataset.
Start the free learning paths →Data Science Foundations
5 WeekA compact on-ramp to data science: just enough Python, pandas, and statistics to analyze a real public dataset and produce your first mini data report.
Start the free learning paths →R
6 WeekLearn R for real statistical analysis: dplyr, the tidyverse, ggplot2, then ship a deployed R Shiny dashboard and real analysis projects on real datasets.
Start the free learning paths →The week-by-week plan
Week 1: SQL Fundamentals: Queries & Databases
SQLLearn to query a relational database: SELECT, WHERE, GROUP BY, and aggregate functions.
Watch
- SQL Tutorial - Full Database Course for Beginners - freeCodeCamp.org
Milestone Write SELECT statements with filtering and sorting
Week 2: Joins & Subqueries
SQLCombine data across multiple tables with joins, and nest queries with subqueries.
Watch
- Intermediate SQL Tutorial | Inner/Outer Joins | Use Cases - Alex The Analyst
- SQL JOINS Tutorial for beginners | Practice SQL Queries using JOINS - Part 1 - techTFQ
- Subqueries in MySQL | Intermediate MySQL - Alex The Analyst
Milestone Understand INNER, LEFT, RIGHT, and FULL joins
Week 3: Window Functions for Analytics
SQLLearn window functions to rank, partition, and run running totals over result sets.
Watch
- SQL Window Functions in 10 Minutes - Colt Steele
- SQL Window Functions You MUST Know - Absent Data
Milestone Understand the OVER() clause and PARTITION BY
Week 4: Project: Design a Relational Database Schema
SQLDesign a normalized schema from scratch: entities, relationships, and an ER diagram.
Watch
- How to Design a Relational Database Schema (ERD, Normalization & SQL) - Tech with Monir
- Database Design: ER Modeling & Normalization (1NF, 2NF, 3NF) Tutorial for Beginners - Tech with Monir
Milestone Model entities and relationships as an ER diagram
Project checkpoint Design a normalized schema from scratch: entities, relationships, and an ER diagram.
Week 5: Project: Analyze a Real Public Dataset
SQLShip a full SQL analytics project: joins, aggregation, and window functions, against a real-world public dataset.
Watch
- Data Analyst Portfolio Project | SQL Data Exploration | Project 1/4 - Alex The Analyst
Milestone Explore a real, messy public dataset with SQL
Project checkpoint Ship a full SQL analytics project: joins, aggregation, and window functions, against a real-world public dataset.
Week 6: What Is Data Science?
Data Science FoundationsGet oriented on what data scientists actually do day to day, and why Python is the field's default toolkit.
Watch
- What REALLY is Data Science? Told by a Data Scientist - Joma Tech
- Intro to Data Science - Crash Course for Beginners - freeCodeCamp.org
Milestone What a data scientist's job actually looks like day to day
Week 7: Python Basics for Data Science
Data Science FoundationsBuild the core Python skills -- variables, loops, functions, data structures -- you'll lean on for every task ahead.
Watch
- Python Full Course for Beginners - Programming with Mosh
- Python for Beginners - Learn Coding with Python in 1 Hour - Programming with Mosh
Milestone Python syntax: variables, data types, and operators
Week 8: Pandas & NumPy Fundamentals
Data Science FoundationsLearn to load, clean, and manipulate real data with pandas and NumPy, and make your first charts with Matplotlib.
Watch
- Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby) - Keith Galli
- Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping) - Keith Galli
- Intro to Data Visualization in Python with Matplotlib! (line graph, bar chart, title, labels, size) - Keith Galli
Milestone Loading data into pandas from CSV and Excel files
Week 9: Basic Statistics for Data Science
Data Science FoundationsCover the statistics vocabulary -- averages, spread, and distributions -- that every data analysis leans on.
Watch
- Statistics - A Full University Course on Data Science Basics - freeCodeCamp.org
- Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!! - StatQuest with Josh Starmer
- StatQuest: Histograms, Clearly Explained - StatQuest with Josh Starmer
Milestone Descriptive statistics: mean, median, mode, and spread
Week 10: Project: Analyze a Real Public Dataset
Data Science FoundationsPut it all together: clean a real-world dataset, explore it with pandas, and produce a simple visual report of your findings.
Watch
- Solving real world data science tasks with Python Pandas! - Keith Galli
- Data Cleaning in Pandas | Python Pandas Tutorials - Alex The Analyst
- Matplotlib Tutorial (Part 1): Creating and Customizing Our First Plots - Corey Schafer
Milestone Working through open-ended questions on a real dataset
Project checkpoint Put it all together: clean a real-world dataset, explore it with pandas, and produce a simple visual report of your findings.
Week 11: R Fundamentals: Syntax & Statistical Computing
RInstall R and RStudio and learn the basics of the language for statistics and data work.
Watch
- R Programming Tutorial - Learn the Basics of Statistical Computing - freeCodeCamp.org
Milestone Install R and RStudio and navigate the IDE
Week 12: Data Wrangling with dplyr & the Tidyverse
RLearn to clean, filter, and reshape data using dplyr's core verbs.
Watch
- Dplyr Essentials (easy data manipulation in R): select, mutate, filter, group_by, summarise, & more - Dataslice
Milestone Select and filter columns and rows
Week 13: Data Visualization with ggplot2
RBuild clear, layered visualizations of your data using ggplot2's grammar of graphics.
Watch
- Crash Course in ggplot2 - A Beginners Guide to Visualizations in R - Daniel Bonneau
- ggplot2 Tutorial | ggplot2 In R Tutorial | Data Visualization In R - ProgrammingKnowledge
Milestone Understand ggplot2's grammar of graphics
Week 14: Project: Statistical Analysis on a Real Dataset
RRun t-tests and linear regression on real data and interpret the results.
Watch
- R programming for beginners - statistic with R (t-test and linear regression) and dplyr and ggplot - Global Health with Greg Martin
Milestone Run and interpret a t-test in R
Project checkpoint Run t-tests and linear regression on real data and interpret the results.
Week 15: Project: Exploratory Data Analysis
RExplore a real dataset end-to-end: cleaning, summarizing, and visualizing to find patterns.
Watch
- R Tutorial | Exploratory Data Analysis of the Penguins Dataset - Colin Quirk DS
Milestone Structure an EDA workflow from raw data to insight
Project checkpoint Explore a real dataset end-to-end: cleaning, summarizing, and visualizing to find patterns.
Week 16: Project: Build an R Shiny Dashboard
RShip an interactive R Shiny web app that lets users explore a real dataset.
Watch
- Easily Build Your First Dashboard in R (R Shiny) - Chris
- R Shiny Dashboard Portfolio Project | 1973 USArrests Dashboard | R Shiny Tutorial for Beginners - Abhinav Agrawal
Milestone Understand Shiny's UI/server structure
Project checkpoint Ship an interactive R Shiny web app that lets users explore a real dataset.
Resources and structure are drawn from the free Dropout Developer learning paths.
Get this as a dated plan
Run the same roadmap through the generator with your weekly hours and a target date to get a dated schedule you can copy, download, or print.
FAQ
Do I need Python and R, or is one enough?
One is enough to get hired, and SQL plus either Python or R covers most listings. This roadmap includes both because R is strong for statistics and clean charts, and seeing the same task in two tools makes the concepts stick.
Is data analyst easier to break into than developer roles?
Often faster from zero, four to seven months, because the tool surface is smaller and spreadsheet experience transfers. It is not easier in the sense of less thinking. The hard part is judgement about which questions matter.
What does a data analyst portfolio look like?
Two or three analyses of real public datasets, each with the question, the SQL or code, the chart, and a short plain-language conclusion. A dashboard is a nice addition. Reviewers care most about whether your conclusion follows from your data.
How much statistics do I actually need?
Descriptive statistics, distributions, correlation versus causation, basic hypothesis testing, and confidence intervals. You do not need measure theory. You do need to know when a difference is noise and to resist being talked out of that.
What is the step up from data analyst?
Analytics engineering, which adds data modelling and pipelines, or data science, which adds machine learning. Both build directly on strong SQL and statistics, so the time spent here is not wasted whichever way you go.