Learning to code in 2026 doesn’t look like it used to.
With the tech industry’s rapid adoption of generative AI coding tools like GitHub Copilot, Claude, and Cursor, the rules have changed for aspiring developers. It’s not just about what you need to learn, it’s also about how you approach building those crucial skills in a world where code is increasingly being written by machines.
And here’s what else most people won’t tell you: tech's increasing reliance on AI is exactly why mastering the fundamentals has never been more important.
AI can generate functional code, but it can’t tell you if the solution is robust, efficient, or aligned with sound design principles. Developers who only “prompt” without understanding are going to stall out quickly. But developers who understand the principles beneath the code, and know how to leverage cutting-edge AI tools to build effectively? Those are the ones landing at FAANG companies.
This guide will put you on that path.
8 steps for learning to code in 2026#
Let this resource serve as your comprehensive roadmap for learning to code in 2026. Here’s what we’ll cover:
Thinking like a developer: how to approach problems and build the right mindset
Core coding concepts: loops, functions, testing, and small building blocks are explained simply
Pick your first language: quick, interactive examples in Python, JavaScript, and SQL
Essential developer habits: debugging, version control, and documenting your work
Understanding Databases and SQL: this is where important information is stored
Working with AI the right way: how to use Copilot, Claude, and Cursor responsibly in your learning loop
Beginner project paths: two structured projects (Python desktop and JavaScript web)
Next steps toward a career: turning projects into portfolio proof and preparing for interviews
To get started, all you need is a keyboard and an open mind.
A little later in the guide, there are additional practice sections that will require you to download a free code editor like VS Code, and sign up for a free account on GitHub to save your work. However, these are totally optional and there is no need to worry about complex installations upfront; we’ll begin in a simple, browser-based coding environment included with this guide.
Now — let's begin!
1. Think like a developer#
The key to thinking like a developer is to work in small, repeatable loops: understand the problem, plan a tiny step, implement only that step, and then reflect on the result.
Before we even touch the terminal, it's important to start thinking like a developer.
The essential mindset shift is that seasoned developers do not try to build everything in one heroic sprint. Instead, they move in cycles. First, they ensure that they understand the problem in plain language. Then, they select the smallest possible step forward, which can be attempted in under 90 minutes. They write enough code to test that single step, run it, and observe what happens. If it fails, they adjust one thing and try again. If it works, they save their progress and move to the next small step.
This cycle of understanding, planning, implementing, and reflecting makes complex problems feel manageable. Beginners often get stuck because they try to do everything at once. By shrinking the scope of their work, the next move will almost always become clear.
From vague idea to shippable slice#
A vague goal, such as “make a notes app,” is too abstract to begin coding.
However, it becomes concrete if you restate the goal with specific details. For example: “I want to create a new note with a title and body text. When I submit the form, I should see that the new note appears at the top of my list with a unique ID and a timestamp.”
This clear statement is a specification, or “spec.” It is also a task small enough to be completed within a week.
An example of a notes app slice is given below:
Goal: Create a new note.
Inputs: Title, body text.
Outputs: Note ID, timestamp.
Successful result: After submitting, the new note appears at the top of the list.
This structure is a useful template. It forces you to define what the feature needs (inputs), what it produces (outputs), and what a successful result looks like. This is the foundation of thinking like a developer.
Debugging without panic#
When you write code, you will inevitably encounter mistakes and bugs. Do not panic; they are simply puzzles to be solved. Treat debugging as a methodical process.
Reproduce the bug consistently: A bug you can trigger every time is a bug you can solve.
Isolate the problem: Simplify or remove code piece by piece until the bug disappears. The last piece you removed is the likely source.
Inspect your program’s state: Use a print statement to observe the values of your variables at a specific point in the code.
Form a hypothesis: Based on your observation, make one small, targeted change to the code and check if the output changes as expected.
Write a test: Once the bug is fixed, write a small test that would have caught it. This prevents it from reappearing in the future.
This process may feel slow at first, but it will become second nature with practice.
Asking for help (humans and AIs)#
Even experienced developers need help. Knowing how to ask for it effectively is a crucial skill. A clear request for assistance works well for human mentors and AI tools. A strong request includes several components, as mentioned below.
Context: State what you are building and what this specific code should do.
The problem: Provide the minimal code required to reproduce the issue and the exact error message.
Your expectation: Explain what you expected the code to do and why.
Constraints: Specify limitations, such as avoiding new libraries or major rewrites.
This format is valuable because it forces you to clarify your thinking before asking for help. You will get better answers and learn more from the process.
Ethics and safety (keep it clean from day one)#
Good developers practice safe habits from the beginning. They do not paste sensitive information like passwords or API keys into their code or online tools. They use dummy data for examples, respect software licenses, and do not use code in a commercial project if the license forbids it. If your code handles sensitive information like payments, logins, or personal user data, getting a review from a more experienced developer is critical.
Developing these ethical habits from day one is essential for a long and successful career.
Two micro-skills that compound#
We have an entire section dedicated to habits later in the guide, but this is worth stating early on: there are two habits that, if adopted early, will significantly improve your work over time.
Write pseudocode first: Pseudocode is a plain-language outline of the steps your code will take. Sketching your logic this way helps you design better solutions and write code with fewer errors.
Name things well: Functions should be named like verbs that describe what they do, and variables should be named for the data they hold. Your future self will thank you.
Making these two skills a habit will provide immense value throughout your career.
Now then, time to dive into some building blocks of software development.
2. Core concepts of coding#
What core ideas do beginners need? To learn coding, beginners need to understand a few essential ideas. The most important is to think of code as a process that transforms inputs into outputs. This flow is then expressed using a small set of tools: variables, conditionals, loops, and functions. By writing a simple test for each piece you build, you can ensure your code works as expected.
If coding seems complex, simplify it to resemble this picture: data goes in, your code transforms it, and a result comes out. Once you can describe that flow in plain English, writing the code is merely a translation. This core toolkit of concepts can be applied to any programming language you choose to learn.
In this section, you’ll find interactive examples in two popular coding languages: Python and JavaScript. These two languages are among the most beginner-friendly and widely used, but they also highlight slightly different strengths:
Python is clean, readable, and great for learning the fundamentals of logic and problem-solving.
JavaScript powers most of the web and shows you how those same concepts apply in an interactive environment.
You don’t need to master both right now. Instead, pick one to follow closely and treat the other as a “translation”—a way to see how the same core idea looks in a different language. If you’re leaning toward data, follow the Python examples. If you’re more interested in the web, follow the JavaScript ones. This will help you understand that programming concepts stay the same, even when the syntax changes.
Inputs to outputs#
Before writing any code for a new feature, you should first state what information will come in (inputs) and what result should come out (outputs). For example, imagine you need to calculate the total price of an item, including sales tax. The flow is as follows: the price and tax rate are the inputs, and the final total is the output.
Python:
# Inputsprice = 100tax_rate = 0.08 # 8% sales tax# Transformationtax_amount = price * tax_ratetotal = price + tax_amount# Output is the value in the 'total' variable: 108.0
This demonstrates the entire process: the code takes a price and a tax_rate, calculates the tax_amount, and adds it to the original price to produce the total.
Variables#
A variable is like a labeled box where you can store a value to use it later. It is essential to give variables names that clearly describe their purpose. In the tax example, price, tax_rate, tax_amount, and total are all variables that hold distinct values.
In some languages like JavaScript, you often use a keyword such as let to declare a new variable. In Python, you can simply assign a value to a name.
// Inputslet price = 100;let taxRate = 0.08; // Use a decimal for 8%// Transformationlet taxAmount = price * taxRate;let total = price + taxAmount;// Output is the value in the 'total' variable: 108
As a simple exercise, try to extend this example. Introduce a new variable named discountRate and use it to compute a finalTotal. You will see how clear variable names guide the logic of your code.
Conditionals #
Code often needs to make decisions based on certain conditions. For example, a store might offer free shipping on orders of $50 or more. This rule can be written as a conditional statement using an if block. The code checks if a condition is true, and if it is, runs a specific block of code.
JavaScript
let orderTotal = 25;let shippingCost = 4.99;if (orderTotal >= 50) {shippingCost = 0;}// shippingCost remains 4.99 because the condition was false
Python
order_total = 25shipping_cost = 4.99if order_total >= 50:shipping_cost = 0# shipping_cost remains 4.99 because the condition was false
Even if rules grow more complex, like offering a special discount for members, the pattern essentially remains the same: state the rule in your code, and the program will follow the correct path.
Loops #
When you need to perform the same action on a collection of items, you use a loop. A loop repeats a block of code for each item in a list and can be used to accumulate a result. For example, to calculate the total price of items in a shopping cart, you can loop through the list of prices and add each one to a running total.
cart_prices = [19.99, 2.50, 4.00, 25.00, 30.75, 15.20]total_price = 0for price in cart_prices:total_price = total_price + priceprint("Cart prices:", cart_prices)print("Total:", round(total_price, 2))
This loop goes through the cart_prices list one item at a time. For each price in the list, it adds that price to the total_price variable.
Functions#
A function is a reusable block of code that performs a specific task. Instead of writing the same logic multiple times, you define it once and call it whenever you need it. Functions take inputs (called parameters), process them, and return an output. This makes your programs cleaner, easier to maintain, and less error-prone.
For example, rather than repeating the same tax calculation code in several places, you can wrap it in a function and reuse it with different inputs.
JavaScript
Lines 1–4 define the function calculateTotalWithTax, using the keyword function in JavaScript. The function uses two inputs, price and rate, and returns the calculated price with tax:
function calculateTotalWithTax(price, rate) {const tax = price * rate;return price + tax;}// Call the function with different valueslet laptopTotal = calculateTotalWithTax(1200, 0.08); // Result: 1296console.log(laptopTotal);
Python
Python uses the keyword def to define a function:
def calculate_total_with_tax(price, rate):tax = price * ratereturn price + tax# Call the function with different valueslaptop_total = calculate_total_with_tax(1200, 0.08) # Result: 1296.0print(laptop_total)
This allows you to perform the same logic multiple times with different inputs without rewriting the code.
One test#
Writing code without tests is like launching a product without quality assurance; you are only assuming it performs as intended.A simple test is a line of code that verifies your code behaves as you expect. You do not need complex tools to start. A single assertion checks if a condition is true. If it is, nothing happens. If it is false, the assertion will raise an error, immediately notifying you of a problem.
JavaScript:
function totalWithTax(price, rate) {const tax = price * rate;return Math.round((price + tax) * 100) / 100;}console.assert(totalWithTax(100, 0.1) === 110, "Should be 110");
If a future code change accidentally breaks this calculation, this test will fail and alert you instantly.
Python:
def total_with_tax(price, rate):tax = price * ratereturn round(price + tax, 2)assert total_with_tax(100, 0.1) == 110.0, "Should be 110.0"
Add one more check for a decimal price like 19.99. If a later change breaks the rounding, the test will catch it immediately.
Print to the console#
When your code’s behavior is unclear, you can print values to see what is happening inside the program at any given moment. Printing is like a window into your program’s logic, allowing you to see what’s happening behind-the-scenes as it runs. In JavaScript, this is done with console.log(), while in Python, you use the print() function.
JavaScript:
In JavaScript printing something on the screen is done by logging that on the console, while in Python, it’s simply calling the built-in print function:
let price = 100;let taxRate = 0.08;console.log("price:", price, ", taxRate:", taxRate);console.log("totalBeforeRound:", price + price * taxRate);const total = Math.round((price + price * taxRate) * 100) / 100;console.log("total: ", total);
Python:
price = 100tax_rate = 0.08print("price:", price, ", tax_rate:", tax_rate)print("total_before_round: ", price + price * tax_rate)total = round(price + price * tax_rate, 2)print("total: ", total)
A couple of gentle rules: Print just before the line you suspect, limit it to one or two key values, and remove the debug lines once you’ve figured out what’s going on.
Read docs without drowning#
A software developer’s most valuable resource is documentation, often called docs. When using code written by others, refer to its documentation to understand how it works. A good approach is as mentioned below.
Skim the examples first to get a general idea.
Confirm the parameters (inputs) the code expects and what it returns (outputs).
Copy a small example and run it yourself to see it in action.
Tweak one thing, like changing an argument, to understand its effect.
Error messages#
Every developer, regardless of experience, writes code that produces errors. When you encounter one, do not be discouraged. To solve it, take the steps mentioned below.
Read the first line of the error message, as it usually names the problem.
Look at the line number indicated in the error. The mistake is often on that line or the one directly before it.
Reduce the code. If you are unsure about what is wrong, remove the code line-by-line until the error disappears. The last line you removed is likely responsible for the error.
Now, let's get into the fun part — choosing your first coding language!
3. Pick your first language#
Which language should I start with? Choose by project: JavaScript for web, Python for automation/data, SQL for data storage/analysis, Java for solid OOP/app backends, and C++ for performance and systems.
Below are five beginner-friendly snapshots, each with a one-line history, a realistic use case, and an interactive-style snippet you can run today.
Python#
Learn Python
This course uses an active learning approach to teach Python programming to beginners. You’ll interact with the code from the start, using everyday logic and fun challenges to build confidence. You will learn essential programming concepts through interactive examples and mini projects like input/output, decision-making, error handling, and simple logic. Whether new to coding or just starting with Python, this course provides the perfect foundation to develop your problem-solving skills and easily write your programs. More than anything else, this course aims to make you a lifelong learner and serve as a strong starting point for a successful career in computing. You don’t need any programming experience to begin.
The story: Released in 1991 by Guido van Rossum, Python was designed to feel like writing English with fewer brackets and more breathing room.
Why learn it first: It’s the all-in-one toolkit for coding, versatile enough for scripting, automation, analytics, and connecting systems.
That’s why beginners, scientists, and AI researchers all rely on it.
Sample use case: Count email signups by domain (quick data wrangling).
# emails -> domain frequencyemails = ["amy@example.com", "bob@work.co", "cat@example.com","dan@news.io", "eve@work.co", "fox@example.com"]from collections import Counterdomains = [e.split("@")[1] for e in emails]for dom, n in Counter(domains).most_common():print(dom, n)
Like this approach? Try Learn Python to practice loops, functions, and small data tasks; then specialize via Become a Python Developer.
JavaScript#
Learn JavaScript
In this course, you’ll learn JavaScript from scratch by building things step by step. You’ll start with simple interactions like displaying messages and buttons that respond to clicks. Then, you’ll teach your code to think using logic, remember things with variables, and make decisions based on the user’s actions. You’ll explore variables, functions, objects, DOM manipulation, event handling, loops, and arrays to build simple yet interactive real-life projects. You’ll go from writing your first line of code to building mini apps like a quiz, a to-do list, and even your digital pet! Every lesson is project-based and beginner-friendly—designed to help you create, not just code. By the end, you’ll confidently understand how to control the page, respond to users, and build interactive web experiences.
The story: Born in 1995 to bring life to web pages, JavaScript is now widespread on the web. With Node.js, it also runs servers, command-line tools, and even desktop apps.
Why learn it first: If your dream is to build websites, web apps, or anything users click in a browser, JavaScript is unavoidable. It’s the only language that speaks directly to the web.
Sample use case: Live-filter a list as the user types (instant UX win).
Want more of this? Try Learn JavaScript to turn patterns into mini-apps, then specialize via Become a Web Developer.
SQL#
Learn SQL
Learn SQL basics with our beginner-friendly guide to understanding the language of data. You’ll start by learning to talk to a database: asking questions and getting meaningful answers using SQL. With AI-assisted guidance, you’ll explore the essentials—selecting columns, filtering rows, sorting results, and applying logic to uncover patterns in raw data. As your questions become more precise, you’ll explore grouping, aggregation, and subqueries to extract deeper insights. Then you’ll shift gears and learn how to create tables, insert and edit data, and design simple relational schemas. Finally, you’ll tackle real-world scenarios using joins, building multi-table queries, and designing interactive dashboards. You’ll complete hands-on challenges and projects like a student tracker and a game leaderboard. No prior experience with databases or programming is needed—this course is your friendly introduction to the language of data.
The story: First built at IBM in the 1970s as “SEQUEL,” and later standardized, SQL is the language for talking to databases.
Why learn it first: Unlike other languages, SQL doesn’t tell the computer how to do something, instead it lets you declare what result you want. The database figures out the “how.” That makes it perfect for pulling insights from data, running reports, or powering apps behind-the-scenes.
Sample use case: Find top-revenue products from a small orders table.
-- SetupCREATE TABLE orders (id INTEGER, product TEXT, qty INTEGER, price REAL);INSERT INTO orders VALUES(1,'Pen',3,1.50), (2,'Notebook',2,4.00), (3,'Pen',10,1.50),(4,'Mug',1,8.00), (5,'Notebook',5,4.00), (6,'Mug',3,8.00);-- Query: top 3 products by revenueSELECT product,SUM(qty * price) AS revenueFROM ordersGROUP BY productORDER BY revenue DESCLIMIT 3;
Ready to go deeper? Start with Learn SQL, then specialize via Become a Database Professional with SQL.
Java#
Learn Java
You’ll start Java with the basics, such as printing messages, doing math, and working with user input, before exploring loops, conditionals, and object-oriented programming. Along the way, you’ll build real console apps, like games and menu systems, while learning how to structure your code using classes, methods, and objects. This course emphasizes hands-on learning and real-world modeling, making Java feel less intimidating and more intuitive. Whether you’re aiming to become an Android developer or backend engineer, or just want a solid foundation in programming, this course will help you write clean, structured code and confidently take your first step into software development. You need to know absolutely nothing about programming before your first lesson.
The story: Released in 1995 by Sun Microsystems, Java introduced the JVM: “write once, run anywhere.” It powered a wave of enterprise software and continues to underpin large-scale backends and much of Android’s legacy infrastructure.
Why learn it first: Java shines when you want structure, stability, and scale. Banks, airlines, and big tech firms rely on it for systems that must run reliably for years. Its object-oriented style also teaches discipline in design.
Sample use case: Money math (precise totals with tax) using BigDecimal.
import java.math.BigDecimal;import java.math.RoundingMode;public class Order {static BigDecimal totalWithTax(BigDecimal subtotal, BigDecimal rate) {return subtotal.add(subtotal.multiply(rate)).setScale(2, RoundingMode.HALF_UP);}public static void main(String[] args) {System.out.println(totalWithTax(new BigDecimal("100"), new BigDecimal("0.08")));// Expected: 108.00}}
Like the structure? Try Learn Java for OOP foundations and tooling, then specialize in it via Become a Java Developer.
C++#
Learn C++
This beginner-friendly course teaches C++ through a practical, hands-on approach. You’ll begin by learning how to communicate with the machine through input/output, simple calculations, and variables. Then, you’ll build programs to make decisions and repeat actions using conditionals and loops. As you progress, you’ll learn how to organize your code using functions, store data with arrays and vectors, and apply your knowledge by building mini projects like a number guessing game and a contact book. These exercises are designed to help you build confidence and reinforce your understanding. In the final section, you’ll build a complete project that combines all your skills in a creative, interactive program. Whether starting a tech career or just curious about coding, this course will give you a solid foundation in procedural C++ programming.
The story: In the 1980s, Bjarne Stroustrup extended the C language with objects and templates, creating C++. It quickly became the driving force for systems where performance is everything.
Why learn it first: C++ gives you power close to the machine along with fine control over memory and speed, while still offering modern abstractions. It powers game engines, browsers, operating systems, and embedded devices.
Sample use case: Streaming moving average (constant-time window updates).
#include <iostream>#include <vector>#include <deque>#include <iomanip>using namespace std;int main() {vector<double> vals = {10, 20, 30, 40, 50};int k = 3;deque<double> win;double sum = 0.0;for (double v : vals) {win.push_back(v); sum += v;if (win.size() > (size_t)k) { sum -= win.front(); win.pop_front(); }cout << fixed << setprecision(2) << (sum / win.size()) << "\n";}}
Want to build up systems thinking? Try Learn C++ to deepen fundamentals, then specialize in it via Become a C++ Programmer.
All programming languages at a glance#
Language | Advantages (Why It’s Considered Beginner Friendly) | Trade-Offs (What to Look Out For) |
Python | Readable, fast to prototype, contains huge libraries for data/automation | Slower than compiled, packaging/environment setup can confuse beginners |
JavaScript | Runs in every browser, instant visual wins, huge ecosystem | Tooling sprawl, challenging async patterns and dynamic types cause issues |
SQL | Declarative and commonly used; great for real data questions | Not general-purpose, contains inconsistencies in dialect, performance needs indexes |
Java | Has strong typing, great tooling, and offers a stable path to back-end roles | Verbose, requires more setup, offers slower iteration than scripts |
C++ | Maximum control and speed, possesses systems-level understanding | Steep learning curve, contains memory pitfalls, build process can be complex |
Remember earlier when we mentioned that you might eventually want a local code editor? Now’s a good time to take that first step. You’ve just run your first snippets in the browser, but real-world developers use tools like VS Code to organize files, run programs, and connect with GitHub to save their work.
Optional: Your first local step#
As you move into the next section on starter habits, try copying one of the snippets from above into VS Code, running it locally, and adding a short README that explains what it does and how to run it. This is your bridge from browser-based play to the everyday workflow of professional developers.
Create a new folder (e.g.,
first-snippet/).Save the code as a single file (
app.py,index.html, ormain.sql).Run it locally and confirm the same output.
Add a minimal
README.md(“what / run / expect”).(Optional) Create a new GitHub repo and push the folder.
4. Essential developer habits#
What’s the minimum that you need to feel like a real coder? You do not need a full suite of professional tools. Instead, you need a few simple habits: keep your project tidy, document what it does, save working copies, and add a single test so you know when it breaks. These practices make your first wins repeatable, and help you build a solid foundation.
Save your work#
You should think of each project as a self-contained unit. Place all related files inside a single folder, for example, notes-app/./d
When you reach a point where your code is working correctly, do not rely on your memory. Instead, make a complete copy of the project folder. You can name these copies sequentially, such as notes-app-v1/ and notes-app-v2/. If a future change breaks your code, you can always return to a previous working version. While professional tools like Git and GitHub are the industry standard for this process (known as version control), making simple folder copies is a perfectly acceptable starting point.
README#
A README file serves as essential documentation for your project, both for yourself and for others. Create a file named README.md in your project’s main folder and write down three key pieces of information:
What: A tiny notes application that can add and list notes.Run: Open the index.html file in a browser (or run: python app.py).Expect: After adding a note, it appears at the top of the list.
That is all you need. The goal is to provide just enough information to understand and run the project.
One file#
At this stage, it is best to keep your project simple by keeping all your code in a single file. This makes it easy to see everything at a glance.
For a JavaScript project, use a single
index.htmlfile that includes a<script>tag.For a Python project, use a single
app.pyfile.For a SQL project, use a single
.sqlfile containing both your table setup and your queries.
You do not need to use any frameworks or external packages at this stage. Additional structure can be added later as your projects evolve.
Debug along the way#
Encountering bugs is a normal part of the development process. Here is a simple rhythm to follow for calm and effective debugging:
Reproduce the bug consistently with the same steps.
Inspect the value of a variable by using a print statement just before the line where you suspect the error occurs.
Isolate the problem by making a single, small change and running the code again. If it works, keep the change. If not, undo it and try a different small change.
Debugging is a detective-like process which is initially slow, but which accelerates significantly with experience.
One test#
Adding a test is like checking the blueprint before building; it ensures your code behaves as expected. You can add a single test directly at the bottom of your file to confirm its correctness.
JavaScript:
function add(a,b){ return a + b; }// test:console.assert(add(2,3) === 5, "add(2,3) should be 5");
Python:
def add(a, b):return a + b# test:assert add(2, 3) == 5, "add(2,3) should be 5"
For now, you can keep this test at the bottom of your code file. If a future change introduces a bug, the test will fail and you will know immediately.
Asking for help#
When asking for help, do not simply state that your code is broken. Instead, provide a clear and concise story using the template outlined below, which works well for both humans and AIs.
Context: “I am building a notes application, and this button should add a new note.”
What I tried: Paste the smallest possible code snippet that reproduces the problem.
What I expected: “After clicking the button, I expected to see the new note appear at the top of the list.”
What I got: Provide the exact error message or describe the incorrect behavior.
My question: “Can you please suggest a minimal fix and one test that I can add?”
Asking clear questions is the best way to receive clear answers.
5. Understanding Databases and SQL#
Why learn SQL now? It is important to learn SQL (Structured Query Language) because modern applications and data analysis rely on storing data permanently in databases. SQL is the standard language for interacting with these databases. It allows you to describe the data you want to retrieve, such as specific rows or summaries, and the database engine handles the complex work of how to find and return it.
Whatever language you pick, and whatever software discipline you land in — you're going to run into SQL eventually. That's why we are dedicating an entire section here.
You can think of SQL as the language for asking questions of your data. While buttons and web pages are the front door to an application, the database is where important information is stored. When you need to retrieve that information, to find a user, analyze sales by date, or answer, “Which product sold best?”, you will need to communicate with a database using SQL.
Zero-setup path#
For this guide, you do not need to install any software. Press the “Run” button in the example below to create a small, sample dataset that you can safely practice with.
The following code uses the CREATE TABLE command to define the structure for two tables, users and orders. Each column is given a name and a data type, such as INTEGER for whole numbers, TEXT for strings, and REAL for decimal numbers.
-- Setup: two tiny tablesCREATE TABLE users (id INTEGER PRIMARY KEY,name TEXT,is_member INTEGER -- 1=true, 0=false);CREATE TABLE orders (id INTEGER PRIMARY KEY,user_id INTEGER,product TEXT,qty INTEGER,price REAL, -- price per unitorder_date TEXT, -- YYYY-MM-DDFOREIGN KEY (user_id) REFERENCES users(id));INSERT INTO users VALUES(1,'Amy',1),(2,'Bo',0),(3,'Cat',1),(4,'Dan',0);INSERT INTO orders VALUES(1,1,'Pen',3,1.50,'2025-08-20'),(2,1,'Notebook',1,4.00,'2025-08-21'),(3,3,'Mug',2,8.00,'2025-08-22'),(4,3,'Pen',10,1.50,'2025-08-23');SELECT * FROM orders;
This setup gives you two connected tables, users and orders, which is enough to begin asking meaningful questions.
Pattern 1: Filter and sort#
The most basic SQL pattern is retrieving data that matches a specific rule. Suppose that you are asked to find the latest orders for a user named Amy, who has a user_id of 1. You can translate this request into SQL almost word-for-word.
SELECT id, product, qty, price, order_dateFROM ordersWHERE user_id = 1ORDER BY order_date DESC;
By changing one detail in the query, you can ask a completely new question. For example, try changing the WHERE clause to WHERE product = 'Pen'. The pattern is the same, but the question is different.
Pattern 2: Summaries#
Next, consider a common business question: “Which product generates the most revenue?” Answering this requires summarizing your data. This is done by grouping rows that have a common value (like the same product name), and then performing a calculation on each group using an aggregate function like SUM().
SELECT product,SUM(qty * price) AS revenueFROM ordersGROUP BY productORDER BY revenue DESC;
You can pivot your analysis by changing the aggregate function. Use COUNT(*) to find which product sells most often, or AVG(qty * price) to find the average order value.
One important rule to remember is that WHERE is used to filter rows before they are grouped, while HAVING is used to filter the groups after they have been created. For example, to see only the products that generated at least $10 in revenue, do the following:
SELECT product, SUM(qty*price) AS revenueFROM ordersGROUP BY productHAVING revenue >= 10;
Pattern 3: Combine tables#
Your analysis becomes more powerful when you connect, or join related data from different tables. Consider the question: “List each order with the buyer’s name.” The orders table does not contain names, but the users table does. To answer this, you must join the two tables using the user_id column that they have in common.
Note the use of aliases (o for orders, u for users) to make the query shorter and more readable.
SELECT o.id, u.name, o.product, o.qty, o.priceFROM orders AS oJOIN users AS u ON o.user_id = u.idORDER BY o.id;
If you want to create a customer list that includes total revenue, even for users who have not bought anything, you can switch to a LEFT JOIN. This ensures that every row from the “left” table (in this case, users) is included in the result. The COALESCE function is used here to replace any missing revenue values with 0:
SELECT u.name,COALESCE(SUM(o.qty*o.price), 0) AS revenueFROM users AS uLEFT JOIN orders AS o ON o.user_id = u.idGROUP BY u.nameORDER BY revenue DESC;
Read SQL like a story#
While you write a SQL query in a certain order (SELECT first), the database processes it in a different logical order. Understanding this helps you write better queries.
FROM / JOIN: Determines the source of the rows.WHERE: Filters individual rows based on a condition.GROUP BY: Buckets the filtered rows into groups.HAVING: Filters the groups based on a condition.SELECT: Specifies which columns or calculations to show.ORDER BY / LIMIT: Presents the final result in a specific order.
Practice now!#
Run the setup once. Ask three questions:
Find Amy’s latest orders by running the filter and sort query. Then, modify the
WHEREclause to ask a different question.Find the revenue by product by running the grouping query. Then, add the
HAVINGclause to filter the results.Find out which customers have earned zero revenue by running the
LEFT JOINquery.
Look it up: Start Learn SQL for guided practice with these patterns. Ready for schema design, views, procedures, triggers, and anomaly detection? Continue with Become a Database Professional with SQL.
6. Learning & building with AI#
What’s the best way for beginners to engage with AI responsibly and effectively?
The responsible way to use AI as a beginner is to let it assist you with small pieces of code while explaining concepts you do not know. However, you should only use code that you can personally run, test, and explain.
AI can seem impressive, but its value diminishes when it generates a large, unexplainable block of code. If you cannot modify or debug the code, you do not truly own it.
The goal is not to avoid AI, but to partner with it effectively. This allows you to move faster while still developing the skills that matter: writing clear specifications, creating simple designs, debugging, and verifying your work.
Think of AI as a GPS: it can suggest the fastest route, but you’re still the driver.
You decide the destination, check if the route makes sense, and handle roadblocks. To use AI effectively, you first need to know what’s available and what each tool is good at.
The Tools You’ll See Most Often#
Here are some of the most widely used AI tools for coding today:
GitHub Copilot: Built directly into VS Code; suggests code as you type. Great for autocomplete, boilerplate, and small functions.
Cursor: A code editor with AI deeply integrated; ideal for debugging, refactoring, and asking “why” about your code.
Claude (Anthropic): Strong at explaining code in plain English, summarizing large files, and generating structured outputs.
ChatGPT (OpenAI): Flexible assistant for brainstorming, pseudocode, documentation help, and step-by-step walkthroughs.
Phind: An AI search engine for developers; helpful when you want quick explanations or coding best practices.
These tools differ in interface, but they all work best when you ask them for something small and specific.
The pitfall of large-scale prompts#
Consider what happens when you ask an AI chatbot for a complex application with a prompt like: “Build a notes app with authentication and search.” The model will generate a large volume of code. You might paste it into your editor and find that it runs, which can feel like a quick success.
However, a week later, a small change can cause the entire application to break. You will not know which parts are safe to modify, and you will be unable to explain the design choices. This approach is not learning; it jeopardizes your understanding.
The fix: A five-step pairing loop#
The solution is to shrink the problem to a manageable size. Then, you can use an iterative five-step loop to work with AI effectively.
Specify: Write a clear specification with inputs, outputs, and a concrete example of what “done” means.
Prompt: Ask the AI for only that single, minimal piece of functionality. Avoid asking for frameworks or complex solutions.
Run: Always run the code yourself. Do not trust that it works just by looking at it.
Test: Add a single, simple test that proves that the code works and will fail if you break it.
Explain: If you cannot explain the code in plain English, the task was too complex. Simplify it and try again.
Using this method, AI acts as your assistant, but you remain the engineer in control of the project.
7. Beginner project paths#
Goal: Write shippingCost to return free shipping for orders $50 and up or for members; otherwise, charge $4.99.
Tiny spec:
Inputs:
total(number),isMember(boolean)Output: Number (shipping cost)
Done means:
shippingCost(50,false) = 0shippingCost(34,true) = 0shippingCost(20,false) = 4.99
Good prompt:
You are my pair programmer. Write a single function named shippingCost(total, isMember).Constraints:- The function should return 0 if total >= 50 OR if isMember is true. Otherwise, it should return 4.99.- The function must return a number.- Keep the code minimal and do not use any external frameworks.- Include three short tests I can run immediately.Language: Python
You can run the result in a single app.py file. The test should be kept in the same file so you can always run it.
Python
def shippingCost(total, is_member):return 0 if (total >= 50 or is_member) else 4.99# test (same file)assert shippingCost(50, False) == 0assert shippingCost(34, True) == 0assert shippingCost(20, False) == 4.99
Explanation: “The function returns 0 if either the order total is 50 or more, or if the user is a member; otherwise, it returns 4.99.” If this explanation is clear and obvious to you, the code is at the right level of simplicity.
Your task: Ask an AI to modify this function to add a member-only free-shipping threshold of $35. After getting the code, re-run the existing tests and add one new test that proves that the new $35 rule works correctly.
Prompts that keep you safe#
To get safe and useful responses from AI, always lead with a small specification and an example input/output pair. Add constraints like “use a single file and no frameworks.” Always ask for a few runnable tests and a brief explanation of the code. If the AI assistant asks a clarifying question, it is often a good sign that it is processing your constraints correctly.
Note: You should be especially cautious and avoid relying heavily on AI for the following areas: authentication, payment processing, personal user data, secret keys, and performance-critical code.
Additionally, avoid using large blocks of code that you do not understand. Instead, ask the AI for a smaller version or a step by step outline that you can implement yourself.
Verification checklist for your code#
Before finalizing any piece of code, ask yourself these questions:
Have I run the code myself?
Does my test correctly fail if I intentionally break the code?
Can I explain the code in plain English?
Have I considered edge cases, such as empty inputs or unexpected values?
If the code handles sensitive data, have I reviewed it twice?
Practice now!#
Write a one-paragraph spec for the next tiny slice of your project (“add a note”). Use the safe prompt pattern to generate a single function. Run it, add one test in the same file, and jot down a two-line explanation in your README.
Look it up: Continue learning programming and prompting with Learn to Code with AI.
7.1 Hands-on projects#
What’s the fastest way to get real, portfolio-ready proof? Pick one route, Desktop App (Python) or Web App (JavaScript). Ship a tiny starter in weeks 1–2, then a small growth upgrade in weeks 3–4. Keep it to one file, add a tiny test per function, and record a 60-second demo.
Think of this next section as two steady sprints.
You’ll work 5 short sessions a week (60–90 minutes each) and finish with a project you can actually show: a clean README (“what / run / expect”), one or two screenshots, small tests that pass, and a 60-second demo explaining what you built.
This is where all your habits pay off: tiny specs, tests, debugging loops, and safe AI prompts.
Pick your route
Route A is a desktop app (Python, Tkinter): Build “Quick Notes” with a “Reports” upgrade.
Route B is a web app (JavaScript, one HTML file): Build “Sticky Notes” with “Search and Stats.”
Choose one. You can always try the other one next month. Focus is what gets you finished.
7.1.1Route A: Desktop app#
Why this is beginner-friendly: The tkinter library ships with Python, so you click a file and a window appears, with no extra installations involved. You’ll practice graphical user interfaces (GUIs), events, small functions, and saving to a simple file (JSON). If you’re curious later, you can swap JSON for SQLite.
Starter: “Quick notes” GUI
Start with three actions: add a note (title + body), list notes (newest first), and delete a note. Keep everything in one file, let the app create notes.json automatically, and tuck a single assertion at the bottom so you know your add-note logic didn’t just silently break.
import json, os, tkinter as tkDATA = "notes.json"def load_notes():return json.load(open(DATA, "r", encoding="utf-8")) if os.path.exists(DATA) else []def save_notes(notes):json.dump(notes, open(DATA, "w", encoding="utf-8"), ensure_ascii=False, indent=2)def add_note(notes, title, body):if not title.strip(): return notesreturn [{"title": title.strip(), "body": body.strip()}] + notes# tiny test (logic only)assert add_note([], "Hello", "World")[0]["title"] == "Hello"notes = load_notes()root = tk.Tk(); root.title("Quick Notes")title = tk.Entry(root, width=40); title.grid(row=0, column=0, padx=8, pady=6)body = tk.Text(root, height=5, width=40); body.grid(row=1, column=0, padx=8)list_frame = tk.Frame(root); list_frame.grid(row=2, column=0, pady=8)def render():for w in list_frame.winfo_children(): w.destroy()for i, n in enumerate(notes):tk.Label(list_frame, text=n["title"], anchor="w").grid(row=i, column=0, sticky="w")tk.Button(list_frame, text="Delete", command=lambda i=i: on_delete(i)).grid(row=i, column=1)def on_add():global notesnotes = add_note(notes, title.get(), body.get("1.0", "end"))save_notes(notes); render(); title.delete(0,"end"); body.delete("1.0","end")def on_delete(i):global notesdel notes[i]; save_notes(notes); render()tk.Button(root, text="Add", command=on_add).grid(row=0, column=1, padx=6)render(); root.mainloop()
By the end of Week 2 you should have on your Git a short README (“what / run / expect”), one screenshot, that tiny test still passing, and a 60-second demo where you add and delete a note.
Run this AI prompt on the LLM of your choice to finish the project, and verify it on your local machine to see if everything works:
Growth: “Reports”
Now teach the app to answer simple questions, for example, how many notes exist, how many were added this week, and which words show up most (ignoring common stopwords). Keep it in one file if you can; the important part is refactoring logic into small, testable helpers.
STOP = {"the","a","an","and","or","to","of","in","on","for","with"}def top_words(notes, k=3):counts = {}for n in notes:for w in (n["title"] + " " + n["body"]).lower().split():if w.isalpha() and w not in STOP:counts[w] = counts.get(w, 0) + 1return sorted(counts.items(), key=lambda kv: kv[1], reverse=True)[:k]# tiny testassert isinstance(top_words([{"title":"Hello World","body":"Hello again"}], 2), list)
By the end of Week 4, you’ll have a second screenshot, a short demo showing the report output (a pop-up or console print is fine), and two passing tests. Curious about databases? Swap JSON for sqlite3 and re-ask these questions with SELECT, GROUP BY, and a tiny JOIN, a perfect excuse to start Learn SQL.
7.1.2 Route B: Web app #
Why this is beginner-friendly: Everything lives in one HTML file. You open it in a browser and see results instantly. Persistence comes from localStorage, so you don’t need a server.
Starter: “Sticky notes”
Start with add, list (newest first), and delete. Keep a single index.html. A tiny console.assert right below your logic proves it does what you think:
By the end of Week 2 you’ll have the README, a screenshot, the console.assert still green, and a 60-second demo. If delete doesn’t work, confirm the button has data-i and you’re checking e.target.tagName === "BUTTON".
Growth: “Search and Stats”
We should add a search box that filters as you type and a tiny stats panel that counts notes, shows notes from this week, and lists the top three words:
By the end of Week 4, your search should feel instant, and the stats should update as you type; you’ll have a second screenshot and demo, and two tiny tests passing, all still in one file.
One month learn to code roadmap#
Week 1: Get the starter running with one small test and a screenshot.
Week 2: Smooth the rough edges and record your first 60-second demo and a short “What I learned” note.
Week 3: Deliver the growth feature (Reports or Search and Stats) and add one more tiny test.
Week 4: Focus on polish and presentation; share a second demo, refine the
README, and gather feedback from one friend for clarity.
Success metrics#
One-file app runs on your machine.
Two tiny tests pass (one per function you added).
READMEsays what / run / expect and includes screenshots.Demo shows the app and what you learned.
You can explain each function in two sentences.
Supplementary courses: For Python, use Learn Python (loops/functions), later Learn SQL if you upgrade to SQLite. For JS route, use Learn JavaScript (DOM/events), and later Learn SQL to analyze exported notes. When you’re ready for version control, take the click-first path with Learn Git.
8. Next steps toward a career: Time to apply!#
You’ve spent a month coding—how do you turn that into interviews? You can transition from a learner to a job applicant by making your proof of work easy to review. This includes two small projects, a clear README file, a short demonstration video, and an honest list of your skills. Finally, you should practice explaining your work out loud.
You do not need to solve a thousand advanced coding problems to get noticed. Instead, you need to provide evidence that you can think clearly and complete a real project. Recruiters and hiring managers are busy. If they can understand your value in under a minute, you have a much better chance of moving forward.
Prepare a good portfolio#
Your portfolio should serve as a central hub for your work. A single GitHub README file or a simple personal webpage is sufficient. For each of your two main projects, provide a fast, human-friendly introduction that includes the following:
A brief, one-sentence description of what the project does.
Clear instructions on how to run the project.
A description of the expected outcome when it runs successfully.
A screenshot of the application in action.
A link to a 60-second video demonstration.
Within the project’s code repository, ensure that you have a few simple tests that pass successfully. Add brief comments to your main functions explaining what each one does and the reasoning behind your implementation. If a person unfamiliar with your work can understand the project’s purpose and value in under a minute, you have succeeded.
Perfect your résumé#
Your résumé does not need to be flashy. It needs to be honest and aligned with the roles you are applying for.
Skills block: Keep the list concise and relevant to your chosen path. For example,Python, JavaScript, SQL, Testing, Git.
Projects section: For each project, provide a title, a one-line description of its outcome, and a link to the code repository or a live demo. It is helpful to use keywords from the job descriptions you are targeting, such as “web,” “full-stack,” or “data/SQL.” However, do not include any terms or technologies that you cannot confidently explain.
Rehearse like you ship#
Polishing your code is important, but it is equally important to practice explaining it. Prepare for interviews with these two exercises mentioned below.
Debugging mini-mock (10 minutes): Intentionally break your code in a minor way. Then, practice fixing it while narrating your thought process out loud. Explain what you observe, what you hypothesize the problem is, and why you are trying each step.
Project walkthrough (10 minutes): Prepare a short presentation of your project. State the problem it solves, show the first small piece you built, mention one trade-off you made in your design, and point to the test that proves a key feature works.
If you can explain your project clearly in simple terms, you are well-prepared for a technical interview.
Choose your next step#
Your next move depends on the lane you’re building momentum in.
Web momentum: For deepening web events, and then adding a tiny backend → Learn JavaScript.
Python momentum: With Python under your belt, you can:
Data momentum: For practicing joins/CTEs, and answering business questions → Learn SQL.
Hygiene (when you're ready): For saving history the easy way → Learn Git.
You’ve got this. Keep the loop small, understand → plan → implement → reflect. Use AI as a power tool, and not a crutch. This month gave you proof; the next month can turn it into momentum. Use Educative’s resources to learn and earn your certificates.
Onward and upward#
Learning to code in 2026 is less about memorizing syntax and more about building a toolkit: the ability to break problems down, write small, testable pieces of code, and know when to lean on AI responsibly. Along the way, you’ve seen how Python, JavaScript, and SQL bring these concepts to life, and how simple projects can quickly turn into portfolio pieces that prove your skills.
Your next step is simple: pick one project path, commit to working on it consistently, and use the habits from this guide — debugging, version control, testing, and clear explanations — as your foundation. The job market will keep changing, and the tools will keep evolving, but those fundamentals will always hold their value. Pair them with the right AI tools, and you’ll not only keep up, you’ll move faster.
Learn to Code with AI
This course teaches you to become a productive and professional software developer by leveraging AI Coding Assistant for hands-on experience in industry-relevant workflows. You will start by generating code for basic tasks like writing functions, then advance to creating entire classes, and finally select the appropriate data structures and algorithms to solve problems. As you progress, you’ll tackle real-world scenarios, including using AI for code generation through writing prompts for AI code generation, debugging, testing, and explaining code. The course also covers both procedural programming and object-oriented programming principles. Ultimately, you’ll learn to utilize Python libraries AI assistant for continuous growth and improvement, preparing you for a future-proof career in software development with AI as your collaborative coding partner.
Coding is a long game, but you’ve already started building the mindset and structure you’ll need. Keep shipping small wins, stay curious, and let this first month of learning be the launchpad for everything that comes after.
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