Learning path

Data Analyst Roadmap for Beginners

Free courses in the order an analyst actually picks the tools up: spreadsheets first, because that is where the data is, then SQL to get it out of a database, then Python with NumPy and Pandas to do what a spreadsheet cannot. Every lesson runs in your browser and the Excel, SQL, Python, Pandas and R courses end with a free certificate.

START LEARNING(it's free)

2,347,853+ codders enrolled across these courses

470 lessons440 challenges1,998 quiz questions

Beginner friendly
AI-assisted coding help
Hands-on interactive lessons
Audio narration on every lesson
Quizzes to test your knowledge
8 free certificates along the path

The data analyst training path, step by step

9 courses7 sections470 lessons440 challenges1,998 quiz questions

Each step is an existing Coddy course. Take them in order, or jump into the one you are missing - your progress is saved per course either way.

1
Step 1
Spreadsheets and ExcelDedicated page
Start this stepStartCells, ranges, formulas, lookups, pivot tables and the functions analysts use every day. Most of the world's data lives in spreadsheets and most analyses are first shared in one, so this is where the path begins.Start Dedicated page
2 sections, in order
1. Fundamentals42 lessons34 challenges235 questions
2. Formulas and Data Analysis28 lessons48 challenges100 questions
70 lessons82 challenges335 questions
2
Step 2
SQL for data analysisDedicated page
Start this stepStartSelect, filter, join, group and aggregate real tables, then window functions and subqueries. SQL is the most requested skill in data analyst job postings and the fastest one to become useful in.Start Dedicated page
2 sections, in order
1. Fundamentals72 lessons72 challenges360 questions
2. Beyond the Basics27 lessons28 challenges167 questions
99 lessons100 challenges527 questions
3
Step 3
Python fundamentalsDedicated page
Start this stepStartVariables, control flow, functions and collections - just the two sections an analyst needs before NumPy and Pandas make sense. Classes and objects can wait; a working analyst rarely writes them.Start Dedicated page
2 sections, in order
1. Fundamentals77 lessons98 challenges341 questions
2. Logic & Flow78 lessons77 challenges329 questions
155 lessons175 challenges670 questions
4
Step 4
NumPy: fast numbersDedicated page
Start this stepStartArrays, vectorized arithmetic, indexing, aggregation and broadcasting. NumPy is the engine under Pandas and every scientific Python library, and understanding it is what makes DataFrame operations stop being magic.Start Dedicated page18 lessons34 questions
5
Step 5
Pandas: tables in PythonDedicated page
Start this stepStartDataFrames, cleaning, filtering, grouping, merging and reshaping real datasets. This is the step where Python overtakes the spreadsheet: everything you did in Excel, repeatable on data too large to open.Start Dedicated page19 lessons
6
Step 6
Getting data in: files, JSON and APIs
Start this stepStartRead and write files, parse JSON, call a web API and handle what goes wrong. Real data is never handed over as a clean table; an analyst who can fetch their own data is not waiting on an engineer.Start
3 courses, in order
31 lessons8 challenges
7
Step 7
R, when a job asks for itDedicated page
Start this stepStartOptional. The same analysis ideas in the language of statistics teams: vectors, data frames and data manipulation. Skip it unless a role you want lists R, then take it knowing every concept transfers from the Python steps.Start Dedicated page78 lessons75 challenges432 questions

What you get

Everything you'll use to learn to code

Learn by Doing

Write real code, query databases, build websites, and master AI prompts. Our interactive lessons cover every skill modern developers need.

playground.js

Code Editor

1const greeting = "Hello, Coddy!"

2function sayHi(name) {

3 return greeting + " " + name

Test #1

Test #2

Test #3

Input

"Alex"

Output

"Hello, Coddy! Alex"

Build Your Coding Streak

Stay consistent and watch your progress grow! Track your daily coding habit, protect your streak with freeze days, and earn rewards for showing up every day.

12 days streak

Return tomorrow to keep your streak!

January 2026

Double or Nothing

Day 5 of 7

Streak Freeze

2 left

Code Anywhere, Anytime

Take your coding journey on the go! No setup, no downloads - just open and start coding. Available on iOS, Android and Web with 4.9 star ratings.

250

Variables

CONTINUE

4.9

Rating

You're Not Alone in This

Compete on global leaderboards, invite friends to earn rewards, and celebrate each other's wins. Coding is better with friends!

Challenger LeagueTop 7 advance

Alex7+ Days

2840

Jordan7+ Days

2650

Sam7+ Days

2420

Casey

2180

Morgan7+ Days

1950

Promotion zone

Every way to learn

Read, listen, test yourself, ask the AI, or look up anything you've already covered. Every lesson meets you where you are.

Intro to Variables

Audio

A variable is a named container that stores a value you can reference later in your program.

In Python, you create one by writing the name, an equals sign, then the value you want to store.

The value can change over time - reassigning the name simply points it to a new value.

1xSarah

Prove Your Skills

Earn certificates for every course you complete. Add them to your LinkedIn profile and resume to showcase your coding expertise to employers.

Certificate of Completion

This certifies thatJohn Doehas successfully completed

Python Fundamentals

DateJan 2026

Add to LinkedIn

Why learn data analysis on Coddy

The order the job uses. Most data analyst courses open with Python; most analyst days open with a spreadsheet and a SQL query. This path teaches the tools in the order you will reach for them, so each step pays off before the next begins.
Real data, real queries. You write SQL against real tables, transform real Pandas DataFrames and build real spreadsheet formulas in the browser, nothing installed, nothing simulated with slides.
AI hints that read your query. When a join returns the wrong rows or a DataFrame operation throws, Bugsy looks at what you wrote and nudges you toward the fix without handing over the answer.
Free certificates, each verifiable at its own URL, for the Excel, SQL, Python, Pandas and R courses. An Excel certificate, a SQL certificate and a Pandas certificate are three lines on a CV that a hiring manager can test for in ten minutes.

Frequently asked questions about becoming a data analyst

How do I become a data analyst with no experience?

Learn the three tools every analyst posting lists - spreadsheets, SQL and one of Python or R - in that order, then build two or three analyses of real public datasets that answer a question someone would actually ask. That is this path plus a small portfolio. Experience with data is easier to manufacture than most careers: every public dataset is a project waiting to be written up.

What skills does a data analyst need?

Spreadsheets for quick work and sharing, SQL for getting data out of a database, a programming language for anything a spreadsheet cannot do (cleaning, joining large tables, repeatable pipelines), enough statistics to know what a mean hides, and the writing skills to explain a chart to someone who will not read the numbers. This path covers the first three directly; the last two you practise on every analysis you write up.

Should I learn Excel, SQL or Python first for data analysis?

Excel first, SQL second, Python third, which is the order of this path. Spreadsheets teach you to think in tables at no cost. SQL is the single most requested analyst skill and takes three weeks to become useful. Python only pays off once you have data big or messy enough that a spreadsheet struggles, so learning it first means learning it without a reason.

Is Python or R better for data analysis?

Python, for most analyst jobs, because it is asked for far more often and the same skill transfers into automation and engineering work. R remains the language of statistics departments and some research and pharma teams. This path teaches Python with NumPy and Pandas and keeps R as an optional final step, so you can add it when a job asks for it.

How long does it take to become a data analyst?

Around four to six months at an hour a day to reach a junior level. On this path Excel takes two to three weeks, SQL three to four, the Python basics six, and NumPy and Pandas two to three weeks together. The remaining time is what matters most: writing up analyses of real datasets until you can explain a result to someone who did not ask for the method.

Does this data analyst path cover Tableau or Power BI?

No, and we would rather say so than pad the page. Dashboarding tools are a common job requirement and a short study once you can already shape the data underneath them, which is what this path teaches. When a visualization course ships on Coddy it joins this path as a new step.

Is a data analyst certificate worth anything?

On its own, little; attached to skills a hiring manager can test, quite a lot. The Excel, SQL, Python, Pandas and R courses here each issue a free certificate at its own verifiable URL, and each names one concrete skill. Those are the lines an applicant tracking system searches for, and the three questions an interviewer will ask you to prove.

Other learning paths

The same courses, arranged for a different role. Progress carries over: a course finished on one path counts on every path that includes it.

All learning paths

Coddy programming languages illustration

Start the Data Analysis path for free

Start learning