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

> Tips for new coders: choose a first language by goal, practise on small projects, read errors, use Git and tests early, and use AI as a tutor.

- URL: https://computese.com/essential-tips-for-new-coders/
- Author: Duong Quan Nguyen, CEO, Computese
- Published: 2024-10-04
- Updated: 2026-09-25
- Topics: Web development, Custom software

## In short
- Pick your first language by what you want to build: HTML, CSS and JavaScript for the web, Python for data and automation, Kotlin for Android, Swift for Apple platforms. With no goal yet, Python is the safe default.
- Skill comes from writing code yourself: small projects just past what you can already do, with errors read from the last line up and a debugger instead of guesswork.
- Use Git and simple tests from the first week. Commits let you return to the last version that worked; a test proves a function still does what you meant after every change.
- Ask precise questions with a minimal reproducible example, and get your code reviewed in small pieces.
- Use AI assistants as a tutor, not a ghostwriter: studies of novices found that handing whole tasks to AI hurt later performance, while asking for explanations kept learning intact.

The essential tips for new coders fit in one loop: pick a first language for what you want to build, learn the fundamentals, and write small projects yourself. Read error messages from the last line up, save your work with Git, prove it with simple tests, ask precise questions, and [use AI as a tutor](https://computese.com/ai-in-education-learning-and-beyond/), not a ghostwriter.

This guide takes each step in order, with the commands and examples a beginner actually needs: how to choose between JavaScript, Python, Kotlin and Swift, how to practise so it sticks, how to debug, how to start with Git and tests, how to get useful help and code review, and what research on novices says about AI coding assistants. It ends with free courses that are open in 2026 and a plan for your first week. If websites are what you want to build, our [web development guide](https://computese.com/web-development-guide/) maps how the front end, back end and hosting fit together.

## 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](https://cs50.harvard.edu/x/) 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.

| If you want to build          | Start with                         | Add next                          | Official place to start     |
| ----------------------------- | ---------------------------------- | --------------------------------- | --------------------------- |
| Websites and web apps         | HTML, CSS and JavaScript           | TypeScript, then a framework      | MDN Learn web development   |
| Data analysis, automation, AI | Python                             | SQL, then libraries for data      | The Python Tutorial, CS50P  |
| Android apps                  | Kotlin                             | Jetpack Compose                   | Android Basics with Compose |
| iPhone, iPad and Mac apps     | Swift                              | SwiftUI                           | Develop in Swift Tutorials  |
| Not sure yet                  | Python, or CS50x's route through C | Whatever your first project needs | CS50x                       |

The usage numbers point the same way. In [Stack Overflow's 2025 Developer Survey](https://survey.stackoverflow.co/2025/technology), 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](https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/) 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](https://computese.com/top-design-tips-for-2024-for-starter/) cover them.

For phone apps, the platform decides. Google has described Android development as [Kotlin-first](https://developer.android.com/kotlin/first) since Google I/O 2019, and its [Android Basics with Compose](https://developer.android.com/courses/android-basics-compose/course) 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](https://developer.apple.com/tutorials/develop-in-swift) teach Swift with [Xcode](https://developer.apple.com/xcode/), which runs on a Mac. Cross-platform frameworks such as [Flutter](https://flutter.dev/), which uses the [Dart](https://docs.flutter.dev/learn/pathway) language, and [React Native](https://reactnative.dev/), 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](https://computese.com/augmented-reality-enhancing-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](https://cs50.harvard.edu/python/) 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](https://www.theodinproject.com/about) 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](https://psycnet.apa.org/record/1993-40718-001): 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:

```python
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:

```text
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](https://docs.python.org/3/tutorial/errors.html) 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`.

![A laptop shows a program as grey lines. Beside it, a stack of three linked cards traces the calls that led to an error; the bottom card is orange and points to the exact failing line.](https://computese.com/images/blog/essential-tips-for-new-coders/traceback.bab9d0ec6c-1536.webp)

*Read a traceback from the bottom: the last frame is where the program failed, and each frame above it is how the program got there.*

Now the cause is visible. [`input()`](https://docs.python.org/3/builtins/functions.html) 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](https://developer.chrome.com/docs/devtools/javascript) 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](https://survey.stackoverflow.co/2025/developers), 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](https://developer.mozilla.org/en-US/docs/Learn_web_development) for HTML, CSS and JavaScript, [docs.python.org](https://docs.python.org/3/tutorial/index.html) 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](https://git-scm.com/book/en/v2) book by Scott Chacon and Ben Straub is free to read online under a Creative Commons licence.

These seven commands cover the first months:

```bash
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`](https://git-scm.com/docs/git-restore) throws away edits you have not committed, and [`git revert`](https://git-scm.com/docs/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.

![A row of saved project versions sits on a timeline. The newest one holds a broken gear, and a curved arrow carries the project back to an earlier orange version marked with a check.](https://computese.com/images/blog/essential-tips-for-new-coders/commits.a7380935ed-1536.webp)

*Commit each working step: when a change breaks the project, the last version that worked is one command away.*

Before your first commit, add a [`.gitignore`](https://docs.github.com/en/get-started/git-basics/ignoring-files) 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.

> [!WARNING]
> API keys, tokens and passwords never go in code. GitHub's [guide to storing secrets](https://docs.github.com/en/get-started/learning-to-code/storing-your-secrets-safely) says to keep them in environment variables or a secret manager, and to treat a secret as compromised, and revoke it at once, if it was exposed even for a second. Deleting it in the next commit is not enough, because the earlier commit still holds it.

The same habits, with input validation and dependency updates, are covered in our guide to [secure coding best practices](https://computese.com/best-practices-for-secure-coding/). 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](https://docs.pytest.org/en/stable/getting-started.html) is the usual first tool. Install it inside a [virtual environment](https://docs.python.org/3/library/venv.html), so each project keeps its own packages:

```bash
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:

```python
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`:

```python
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`](https://nodejs.org/api/test.html) 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](https://computese.com/navigating-the-future-unveiling-10-cutting-edge-automation-testing-trends-of-2024/) 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](https://stackoverflow.com/help/how-to-ask) 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](https://stackoverflow.com/help/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](https://google.github.io/eng-practices/review/) 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](https://google.github.io/eng-practices/review/developer/small-cls.html) 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](https://docs.github.com/en/pull-requests/get-started/about-pull-requests) 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`](https://docs.github.com/en/communities/setting-up-your-project-for-healthy-contributions/encouraging-helpful-contributions-to-your-project-with-labels) 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](https://computese.com/services/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](https://survey.stackoverflow.co/2025/ai), 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](https://computese.com/the-future-role-of-software-engineers/).

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

| Study                                                                                            | Who                                                               | What it found                                                                                                                                                                   |
| ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Kazemitabaar et al., CHI 2023](https://arxiv.org/abs/2302.07427)                                | 69 novices aged 10 to 17, 45 Python tasks, half with OpenAI Codex | With the AI: 1.15 times the completion rate and 1.8 times the scores, with no loss on later tasks done by hand. One week later, slightly better, but not significantly          |
| [Kazemitabaar et al., Koli Calling 2023](https://arxiv.org/abs/2309.14049)                       | 33 learners aged 10 to 17, learning Python with Codex             | Learners who had the AI write the whole solution in one prompt scored highest on writing tasks and lowest on the modification tasks that followed                               |
| [Prather et al., ICER 2024](https://arxiv.org/abs/2405.17739)                                    | 21 novice programmers, observed and interviewed in lab sessions   | Students who already knew what to write got faster. For students who struggled, AI compounded their difficulties, and they often believed they had done better than they had    |
| [Shen and Tamkin, 2026 preprint](https://www.anthropic.com/research/AI-assistance-coding-skills) | 52 mostly junior developers learning a new Python library         | The AI group averaged 50% on a follow-up quiz against 67% for those coding by hand, with the widest gap on debugging. Those who asked conceptual questions kept their scores up |

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](https://arxiv.org/abs/2601.20245) 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.

![A laptop sends a question about its code to an AI assistant, which returns an orange lightbulb card, a hint, instead of a finished page of code; the completed page stays parked beside the assistant.](https://computese.com/images/blog/essential-tips-for-new-coders/tutor.9cecdbe7b7-1536.webp)

*Ask for the hint, keep the keyboard: explanations build the skill, while finished code only builds the project.*

3. **Set the tool up as a tutor.** GitHub's [guide for learners](https://docs.github.com/en/get-started/learning-to-code/setting-up-copilot-for-learning-to-code) 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`:

   ```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](https://arxiv.org/abs/2406.10279), 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](https://cs50.harvard.edu/x/honesty/), 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](https://computese.com/the-future-of-ai-what-lies-ahead-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:

| Resource                                                                                           | Best for                          | What you get                                                                                                                                                        |
| -------------------------------------------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [CS50x](https://cs50.harvard.edu/x/) (Harvard)                                                     | A broad start in computer science | Eleven weeks of lectures and problem sets in C, Python, SQL, HTML, CSS and JavaScript, free through OpenCourseWare; the 2026 edition's work is due by June 30, 2027 |
| [CS50P](https://cs50.harvard.edu/python/) (Harvard)                                                | Learning Python specifically      | Ten weeks of Python, including exceptions, debugging and unit tests; only a web browser is required                                                                 |
| [MDN Learn web development](https://developer.mozilla.org/en-US/docs/Learn_web_development)        | Front-end web development         | Structured modules from "never coded before" to comfortable, written by the MDN community and last updated in August 2025                                           |
| [freeCodeCamp](https://www.freecodecamp.org/)                                                      | Web, Python and databases         | Interactive courses and projects from a 501(c)(3) charity; courses, projects and certifications are all free                                                        |
| [The Odin Project](https://www.theodinproject.com/about)                                           | Full-stack web development        | A free, open-source curriculum: a Foundations course, then a JavaScript or a Ruby on Rails path, with a Discord community                                           |
| [The Python Tutorial](https://docs.python.org/3/tutorial/index.html)                               | Python from the source            | The official introduction, part of Python's own documentation                                                                                                       |
| [Pro Git](https://git-scm.com/book/en/v2)                                                          | Git                               | The whole book online, under a Creative Commons licence                                                                                                             |
| [Android Basics with Compose](https://developer.android.com/courses/android-basics-compose/course) | Android apps in Kotlin            | Google's self-paced course, from Kotlin basics to apps with Jetpack Compose                                                                                         |
| [Develop in Swift Tutorials](https://developer.apple.com/tutorials/develop-in-swift)               | Apps for Apple platforms          | Apple's tutorials for Swift, SwiftUI and Xcode                                                                                                                      |

If data and AI are your goal, our [first AI project in Python](https://computese.com/artificial-intelligence-with-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.

## Key terms
- **Deliberate practice**: Effortful practice designed to improve one specific part of your performance, a little beyond what you can already do, with quick feedback. The term comes from K. Anders Ericsson's research on expert performance.
- **Traceback**: The report a program prints when an unhandled error stops it: the chain of function calls that led to the failure, ending with the error type and message. Python prints the most recent call last.
- **Breakpoint**: A marker that pauses a running program at a chosen line so you can inspect every variable at that moment, in a debugger such as Chrome DevTools or Python's pdb.
- **Git commit**: A saved snapshot of your project in Git's history, with a message that says what changed. Commits let you compare versions and return to one that worked.
- **Unit test**: A few lines of code that call one function with known inputs and check the result, so you find out at once when a later change breaks it.
- **Minimal reproducible example**: The smallest complete piece of code that still shows your problem, tested so that anyone who runs it sees the same error. Stack Overflow asks for one with every debugging question.
- **Pull request**: A proposal to merge changes from one branch into another, where other people can comment line by line and automated checks such as tests run before the change is accepted.
- **Code review**: Someone other than the author reading a change before it is merged, looking for bugs, unclear code and missing tests.
- **AI coding assistant**: A tool such as GitHub Copilot, ChatGPT, Claude or Gemini that suggests or completes code, explains errors and answers programming questions in your editor or a chat window.

## Common questions

### What is the best programming language for beginners?

The one that builds what you want: JavaScript with HTML and CSS for websites, Python for data and automation, Kotlin for Android and Swift for Apple platforms. With no goal yet, choose Python: it was the most used language among people learning to code in Stack Overflow's 2025 survey, at 71.8%.

### How long does it take to learn to code?

You write working programs within the first weeks of a structured course: CS50x is organized as eleven weeks of material and CS50P as ten. Becoming employable takes far longer. freeCodeCamp tells its learners that, realistically, it can take several years of practice to learn the skills well enough to get a job as a software engineer.

### Can I learn to code for free?

Yes. Harvard's CS50x and CS50P, MDN's Learn web development, freeCodeCamp, The Odin Project, the official Python tutorial and the Pro Git book are all free to use, as are Google's Android Basics with Compose course and Apple's Develop in Swift Tutorials.

### Should beginners use ChatGPT or Copilot to learn programming?

Yes, as a tutor. In a 2023 study of 33 learners aged 10 to 17, those who had the AI write whole solutions scored highest on the writing tasks and lowest when they later had to modify code. Write your own first attempt, ask for explanations and hints, and test everything the assistant produces.

### Do I need to be good at math to learn programming?

Not to start. Google's Android Basics with Compose course asks for basic computer and math skills (and a computer that can run Android Studio), and most beginner programs need arithmetic and logic rather than advanced math. Fields such as graphics, machine learning and data science use more, and you can learn it when you get there.

### What should my first coding project be?

Something small you would use yourself and can finish: a personal web page, a tip calculator, a script that cleans a spreadsheet export, a flash-card quiz. Finishing a small project teaches more than starting a large one.

## Sources
1. [Stack Overflow Developer Survey 2025: Technology](https://survey.stackoverflow.co/2025/technology), Stack Overflow
2. [Octoverse: A new developer joins GitHub every second as AI leads TypeScript to #1](https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/), GitHub
3. [CS50's Introduction to Computer Science (CS50x 2026)](https://cs50.harvard.edu/x/), Harvard University
4. [Android's Kotlin-first approach](https://developer.android.com/kotlin/first), Android Developers
5. [Android Basics with Compose course](https://developer.android.com/courses/android-basics-compose/course), Android Developers
6. [Develop in Swift Tutorials](https://developer.apple.com/tutorials/develop-in-swift), Apple Developer
7. [Xcode](https://developer.apple.com/xcode/), Apple Developer
8. [Flutter](https://flutter.dev/), Google
9. [Flutter learning pathway](https://docs.flutter.dev/learn/pathway), Flutter documentation
10. [React Native](https://reactnative.dev/), Meta
11. [CS50's Introduction to Programming with Python (CS50P)](https://cs50.harvard.edu/python/), Harvard University
12. [The role of deliberate practice in the acquisition of expert performance](https://psycnet.apa.org/record/1993-40718-001), Psychological Review (APA)
13. [About The Odin Project](https://www.theodinproject.com/about), The Odin Project
14. [Errors and Exceptions (The Python Tutorial)](https://docs.python.org/3/tutorial/errors.html), Python Software Foundation
15. [Built-in Functions: input() and breakpoint()](https://docs.python.org/3/builtins/functions.html), Python Software Foundation
16. [Debug JavaScript](https://developer.chrome.com/docs/devtools/javascript), Chrome for Developers
17. [Stack Overflow Developer Survey 2025: Developers](https://survey.stackoverflow.co/2025/developers), Stack Overflow
18. [Pro Git, second edition](https://git-scm.com/book/en/v2), Git project
19. [git-restore: Restore working tree files](https://git-scm.com/docs/git-restore), Git project
20. [git-revert: Revert some existing commits](https://git-scm.com/docs/git-revert), Git project
21. [Ignoring files](https://docs.github.com/en/get-started/git-basics/ignoring-files), GitHub Docs
22. [Storing your secrets safely](https://docs.github.com/en/get-started/learning-to-code/storing-your-secrets-safely), GitHub Docs
23. [venv: Creation of virtual environments](https://docs.python.org/3/library/venv.html), Python Software Foundation
24. [Get Started](https://docs.pytest.org/en/stable/getting-started.html), pytest
25. [Test runner (node:test)](https://nodejs.org/api/test.html), Node.js
26. [How do I ask a good question?](https://stackoverflow.com/help/how-to-ask), Stack Overflow Help Center
27. [How to create a Minimal, Reproducible Example](https://stackoverflow.com/help/minimal-reproducible-example), Stack Overflow Help Center
28. [Google Engineering Practices: code review](https://google.github.io/eng-practices/review/), Google
29. [Small CLs](https://google.github.io/eng-practices/review/developer/small-cls.html), Google Engineering Practices
30. [About pull requests](https://docs.github.com/en/pull-requests/get-started/about-pull-requests), GitHub Docs
31. [Encouraging helpful contributions to your project with labels](https://docs.github.com/en/communities/setting-up-your-project-for-healthy-contributions/encouraging-helpful-contributions-to-your-project-with-labels), GitHub Docs
32. [Stack Overflow Developer Survey 2025: AI](https://survey.stackoverflow.co/2025/ai), Stack Overflow
33. [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://arxiv.org/abs/2302.07427), CHI 2023 (arXiv)
34. [How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment](https://arxiv.org/abs/2309.14049), Koli Calling 2023 (arXiv)
35. [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://arxiv.org/abs/2405.17739), ICER 2024 (arXiv)
36. [How AI assistance impacts the formation of coding skills](https://www.anthropic.com/research/AI-assistance-coding-skills), Anthropic
37. [How AI Impacts Skill Formation](https://arxiv.org/abs/2601.20245), arXiv preprint
38. [Setting up Copilot for learning to code](https://docs.github.com/en/get-started/learning-to-code/setting-up-copilot-for-learning-to-code), GitHub Docs
39. [We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs](https://arxiv.org/abs/2406.10279), USENIX Security 2025 (arXiv)
40. [CS50x Academic Honesty](https://cs50.harvard.edu/x/honesty/), Harvard University
41. [Learn web development](https://developer.mozilla.org/en-US/docs/Learn_web_development), MDN Web Docs
42. [freeCodeCamp: Learn to code for free](https://www.freecodecamp.org/), freeCodeCamp
43. [The Python Tutorial](https://docs.python.org/3/tutorial/index.html), Python Software Foundation