Essential tips for new coders: from first language to first code review

Choose your first language by what you want to build

Your first language matters less than it feels like it does. Variables, conditionals, loops and functions work much the same way in every mainstream language. Harvard's CS50x is built on that idea: it moves from C to Python, SQL, HTML, CSS and JavaScript, and says its aim is to teach you to program in general and to teach yourself new languages. What your first language should do is get you to something you care about building, quickly.

The usage numbers point the same way. In Stack Overflow's 2025 Developer Survey, Python was the most used language among respondents learning to code, at 71.8%, ahead of HTML/CSS at 66.6% and JavaScript at 62.8%; across all respondents, JavaScript led at 66%. Python's use grew 7 percentage points from 2024, which Stack Overflow ties to AI, data science and back-end work. On GitHub, TypeScript (typed JavaScript) overtook both Python and JavaScript in August 2025 as the language with the most contributors, and GitHub's Octoverse report notes that nearly every major front-end framework now starts new projects in TypeScript. For the web, that means JavaScript first and TypeScript soon after. If the web is your goal, design basics matter as much as code; our web design tips for beginners cover them.

For phone apps, the platform decides. Google has described Android development as Kotlin-first since Google I/O 2019, and its Android Basics with Compose course starts with Kotlin basics; its prerequisites are basic computer and math skills and a computer that can run Android Studio. Apple's Develop in Swift Tutorials teach Swift with Xcode, which runs on a Mac. Cross-platform frameworks such as Flutter, which uses the Dart language, and React Native, which uses JavaScript and React, build one app for several platforms; they are easier once one language feels familiar. If you want to build augmented reality apps, see our guide to augmented reality programming.

Whichever you pick, stay with it until you have finished a few projects. Switching languages every time a tutorial gets hard feels like progress and is not: the hard part is usually the concept, and it will be waiting in the next language too.

Learn the fundamentals before frameworks

The fundamentals are the ideas every later tool is built from. CS50's Python course lists them plainly: functions, arguments and return values; variables and types; conditionals and Boolean expressions; loops; exceptions; libraries; classes and objects; reading and writing files. Add the core data structures, lists (arrays) and dictionaries (maps), and you can read most beginner code in any language.

Frameworks such as React or Ruby on Rails sit on top of those ideas. Learned first, they feel like magic to memorize. Learned after the fundamentals, they read as ordinary code someone else wrote. A practical test: if you cannot write a loop that adds up a list of numbers without looking it up, it is too early for a framework.

Follow one course from start to finish instead of sampling five. Jumping between beginner tutorials, often called tutorial hell, repeats the easy first chapters and skips the part where you build something alone. The structured courses are designed around that part: CS50x ends with a final project of your own choosing, and The Odin Project places projects throughout its curriculum.

Practise deliberately with small projects

Reading and watching create the feeling of understanding; writing code creates the skill. In a 1993 paper, the psychologist K. Anders Ericsson and his colleagues called the practice that builds expertise deliberate practice: effortful activity designed to improve performance, as opposed to repeating what you can already do. For a new coder, that means projects slightly beyond your current ability, with quick feedback from the computer, a test or a reviewer.

Three habits turn tutorials into practice:

1. Rebuild without the video. After a lesson, close it and write the same program from a blank file. Where you get stuck is what you have not learned yet.
2. Change the requirements. Add something the tutorial did not have: save the data to a file, handle empty input, sort the output.
3. Finish small things of your own. Pick projects you would actually use, and small enough to finish in days, not months.

Good first projects, by goal:

• Web: a personal page, a form that checks its input before sending, a to-do list that survives a page reload.
• Data: a script that reads a CSV export, cleans it and prints a summary; a program that renames a folder of files.
• Apps: a tip calculator (the second unit of Google's Android Basics course builds one), a habit tracker, a flash-card quiz.

Practise in regular, shorter sessions rather than rare long ones, and end each one with a line in a notes file about where you stopped, so the next session starts where this one ended.

Read error messages before you change anything

Beginners often treat an error as a verdict. It is a report: it says what went wrong and where. Here is a small Python program with a common bug:

def average(scores):
    return sum(scores) / len(scores)

scores = input("Scores, separated by commas: ").split(",")
print(average(scores))

Type 70,80,90 and Python 3.14 prints:

Traceback (most recent call last):
  File "/home/you/scores.py", line 5, in <module>
    print(average(scores))
          ~~~~~~~^^^^^^^^
  File "/home/you/scores.py", line 2, in average
    return sum(scores) / len(scores)
           ~~~^^^^^^^^
TypeError: unsupported operand type(s) for +: 'int' and 'str'

Read it from the bottom up. The Python tutorial describes this layout: the last line says what happened, and the lines above it are the stack traceback, the context where it happened.

1. The last line says what. A TypeError: Python tried to add a whole number (int) to text (str).
2. The frame just above says where. Line 2, inside average, in the call to sum().
3. The frames above that say how you got there. Line 5 called average with scores.

Now the cause is visible. input() returns what you typed as a string, so split(",") produces a list of strings such as "70", and sum(), which starts counting from the number 0, cannot add text to it. Convert the values where they come in: scores = [int(s) for s in input("Scores: ").split(",")]. The lesson generalizes: data from users, files and networks arrives in a form you did not choose, so check and convert it at the edge of your program.

Debug methodically, not by guessing

When the message alone does not explain the problem:

1. Reproduce it. Find the exact input that causes the failure, every time.
2. Shrink it. Remove code until the problem disappears, then put the last piece back. That piece is where to look.
3. Form one hypothesis and test it. Change one thing, run again, compare.
4. Look at the values, not your assumptions. Use a debugger: set a breakpoint, pause the program on that line and inspect every variable. Chrome DevTools' guide makes the case that console.log() works but breakpoints are faster, because they show every value at that moment. In Python, calling the built-in breakpoint() drops you into the pdb debugger at that line.
5. Explain the code out loud, line by line. Programmers call this rubber duck debugging (CS50 even named its AI helper the duck). Saying what each line should do is often enough to hear where it does something else.

Read the documentation, not only tutorials

Documentation is where working programmers learn. In Stack Overflow's 2025 survey, technical documentation was the most used resource for learning to code, used by 67.8% of respondents in the past year. A tutorial teaches one path through a tool; the documentation tells you everything the tool does and what it promises.

Start from the official source: MDN Web Docs for HTML, CSS and JavaScript, docs.python.org for Python, developer.android.com for Android and Apple's developer documentation for Swift. When you look up a function, read in this order:

• The signature: what it takes and what it returns.
• The errors it raises and the edge cases it mentions, such as empty input or a missing file.
• The example. Run it, then change it and predict the result before you run it again.
• The version the page describes, compared with the version you have installed.

Make one habit of it: when a forum answer or an AI assistant uses a function you do not know, open its documentation page before you use it. It takes a minute, and it is how you find out that a function was removed in your version, or never existed.

Use Git from the first day

Git records snapshots of your project, called commits, so you can see what changed, undo a bad change and try an idea on a branch without risking the version that works. Beginners often put it off until a project is big. Start on day one instead, while projects are small and the commands are few. The Pro Git book by Scott Chacon and Ben Straub is free to read online under a Creative Commons licence.

These seven commands cover the first months:

git init                 # start tracking the current folder
git status               # what changed, what is staged, what is untracked
git add scores.py        # choose what goes into the next commit
git commit -m "Convert scores to numbers before averaging"
git log                  # the history, newest commit first
git diff                 # edits you have not staged yet, line by line
git restore scores.py    # throw away unstaged edits to this file

Commit every time something works, with a message that says what changed and why. Small commits are what make going back cheap: git restore throws away edits you have not committed, and git revert records a new commit that undoes an earlier one. Either way, the last version that worked is one command away, not an evening of undoing by hand.

Before your first commit, add a .gitignore file that lists what Git should never record: your virtual environment (.venv/), build output, and any file that holds secrets, such as .env. GitHub maintains recommended .gitignore templates for many languages and environments. Push the repository to GitHub or another host, and it becomes both a backup and a portfolio.

The same habits, with input validation and dependency updates, are covered in our guide to secure coding best practices. They are easier to learn now than to unlearn later.

Write simple tests for your own code

A unit test is a few lines that call one function with known inputs and check the answer. It turns "I think it works" into a command that says yes or no, and it keeps checking after every later change. In Python, pytest is the usual first tool. Install it inside a virtual environment, so each project keeps its own packages:

python3 -m venv .venv
source .venv/bin/activate        # Windows PowerShell: .venv\Scripts\Activate.ps1
pip install pytest

Put the function in stats.py, deciding what should happen with no scores at all:

def average(scores):
    if not scores:
        raise ValueError("average() needs at least one score")
    return sum(scores) / len(scores)

Then write the tests in test_stats.py:

import pytest

from stats import average


def test_average_of_three_scores():
    assert average([70, 80, 90]) == 80


def test_one_score_is_its_own_average():
    assert average([55]) == 55


def test_no_scores_is_an_error():
    with pytest.raises(ValueError):
        average([])

Run pytest in the folder: it finds every file named test_*.py or *_test.py and reports each test as passed or failed, with the line that failed. The third test is the valuable one. The first version of average divided by zero on an empty list and crashed with a ZeroDivisionError; writing a test for that case forced a decision about empty input, and the function now fails with a message that says what went wrong.

In JavaScript, Node.js includes a test runner in the node:test module, stable since Node.js 20. Whatever the language, run your tests before every commit, and when you fix a bug, first write a test that fails because of it. That test stops the bug from coming back. When you move on to web apps, our overview of test automation in 2026 covers the next layer, end-to-end tests in a browser.

Ask good questions and join a community

Getting stuck is normal; staying stuck alone for days is wasted time. Search first: Stack Overflow's own guidance points out that many common questions have already been answered. When you do ask, make it easy to help you:

1. A title that states the specific problem, including the error message or the function involved.
2. What you expected and what happened instead. "It doesn't work" gives a helper nothing to test.
3. A minimal reproducible example: the smallest complete code that still shows the problem, run once more to confirm it does.
4. The exact error, pasted as text. Screenshots of code and errors cannot be searched, copied or run.
5. What you already tried, and why the answers you found did not help.

Writing the question often answers it: shrinking the code to a minimal example is the same divide-and-conquer as the debugging steps above.

The big free courses each have a community to ask in. CS50 runs question-and-answer communities on Discord, Ed, Reddit and other platforms; The Odin Project's maintainers run a Discord server for its learners; freeCodeCamp describes itself as a community of people learning to code together. Study with someone at your level when you can. Pair programming, two people working on one problem at one keyboard, makes you explain your reasoning, and that is where gaps show.

Get your code reviewed, and read other people's

Code review is someone other than the author examining a change before it is merged. Google's engineering practices describe it as how Google maintains the quality of its code and products. For a beginner it does what no tutorial can: it shows you what an experienced reader notices in your code, from unclear names to a missing edge case.

How to get reviews while you are learning:

• Share small pieces. Google's guide to small changes favours one self-contained change at a time, because small changes are reviewed more quickly and more thoroughly. One function gets a careful review; a whole project gets a polite one.
• Use pull requests, even on your own repository. A pull request proposes merging changes from one branch into another and gives reviewers a place to discuss them, with automated checks such as tests running against the change.
• Ask your course community to review a finished project, with a note on what you want feedback on.
• Contribute to open source. Maintainers tag approachable tasks with the good first issue label so newcomers can find them.

Review other people's code too. Reading code you did not write, and predicting what it does before you run it, is the skill you need to check code an AI assistant wrote.

The professional version of this loop is stricter but the same shape. Our custom software development page lists the gates a change passes on a business system: reviewed pull requests, tests against a real database, and secret, code and dependency scanning on every change. The habits in this guide are the small-scale version of that pipeline.

Use AI coding assistants without skipping the learning

AI assistants are part of learning to code now. In Stack Overflow's 2025 survey, 39.5% of respondents learning to code used AI tools daily, and 73% used them at least occasionally. GitHub's Octoverse report says 80% of new developers on GitHub use Copilot in their first week. Learners also trust the output more than professionals do: 6.1% of learners said they highly trust the accuracy of AI tools, against 2.7% of professional developers. What AI means for the job you are training for is covered in the future role of software engineers.

The research on novices says the tool is not the problem; how you use it is:

Across these studies, AI used to understand kept learning intact, and AI used to avoid thinking cost it. The 2026 study, published by Anthropic researchers as an arXiv preprint and not yet peer-reviewed, found the same split within its AI group: participants who handed the coding or the debugging to the AI averaged below 40%, while those who asked for explanations or only asked conceptual questions averaged 65% or more. Five rules follow from that:

1. Write the first attempt yourself. Ask the assistant once you have a draft or a specific error.
2. Ask for explanations and hints, not solutions. "Why does this raise a TypeError?" teaches you something; "write the average function" does not.

3. Set the tool up as a tutor. GitHub's guide for learners suggests turning off Copilot's inline suggestions in practice projects and giving Copilot Chat standing instructions to teach rather than solve. The first part is one setting in .vscode/settings.json:

   {
     "github.copilot.enable": { "*": false }
   }
   

   The second is a file at .github/copilot-instructions.md, in your own words, for example: "I am a beginner learning Python. Act as a tutor. Explain concepts, give hints and point me to the official documentation, but do not write the code for my exercises." Other assistants have similar custom instructions or learning modes.

4. Never commit code you cannot explain line by line. Read AI code as a reviewer would, run it and write a test for it. Check every function and package it uses in the documentation: in a 2025 USENIX Security study, code-generating models recommended packages that do not exist in at least 5.2% of cases for commercial models and 21.7% for open-source ones.

5. Keep secrets and private data out of prompts. API keys, passwords and other people's data do not belong in a chat window.

Course rules come first. CS50's academic honesty policy, for example, allows its own AI tool, the CS50 Duck, but treats other AI software that suggests or completes answers (it names ChatGPT, Claude, Copilot and Gemini) as not reasonable. If your course has a policy, follow it: its exercises only teach if you do them. For where this is heading, and what it means for careers, see our look at the future of AI in computer science.

Free resources to learn to code in 2026

Each of these is free to follow on its official site, as checked in September 2026:

If data and AI are your goal, our first AI project in Python picks up where the Python basics end.

Where to start this week

1. Write down what you want to build, and pick the matching language from the first table.
2. Choose one course from the list above, and plan to finish it before starting another.
3. Set up your tools: the course's browser environment or a code editor, plus Git. Create a repository for your course work today.
4. In every session, learn one concept, write code without copying it, and commit when it works.
5. When something breaks, read the last line of the error first, then the frames above it.
6. After a few weeks, build one small project of your own, write tests for its core functions and ask someone to review it.
7. Keep AI in tutor mode until you can write and explain the code without it.

Expect it to take time. freeCodeCamp tells its learners plainly that becoming job-ready can take several years of practice. The loop above is how those years add up to skill, not to a pile of finished tutorials.