How to prepare for computer science, software development, and IT at university while you are still in high school

Of all the university disciplines a high school student can prepare for in advance, computer science is the one where early preparation pays off most visibly and most immediately. The reason is straightforward: programming is a skill, and like all skills it is built through accumulated practice over time. A student who begins university already fluent in at least one programming language and familiar with the core concepts of computer science is not simply ahead in the first week, they can focus on new ideas rather than on the mechanics of expressing them in code, and that difference compounds across every subsequent semester.

The gap between students who arrive with prior experience and those who arrive without it is one of the most consistently observed disparities in first-year computer science. It is also one of the most preventable. Unlike the mathematical depth required for engineering or the clinical exposure required for medicine, the core skills of programming are genuinely accessible to any motivated high school student with a laptop and a reliable internet connection. The preparation is available. Most students simply do not know where to start.

1. Learn to code properly before you arrive

The single most impactful thing a prospective computer science or software development student can do in high school is learn to program, not at a surface level, not by following along with tutorials passively, but to the point where they can sit down with a blank file and write a working solution to a problem they have not seen before. Any preparation short of that benchmark is useful, but incomplete.

Python is the right starting language for almost every student in this position. It is used in introductory courses at most Australian universities, its syntax is clean enough to learn without fighting the language itself, and it is the dominant language in data science, machine learning, and scripting, fields adjacent to computer science that students will encounter throughout their degree. Once Python is genuinely comfortable, the concepts transfer readily to Java, C, and C++, which appear heavily in second and third year.

CS50x, Harvard's Introduction to Computer Science, free on edX, is the best single starting resource available. It is rigorous, well-paced, genuinely challenging, and has produced more confident first-year students than any other introductory resource we are aware of. For students who want a Python-specific complement, Automate the Boring Stuff with Python by Al Sweigart is freely available online and takes a practical, project-based approach that builds real competence through things that feel immediately useful. The two together cover more ground than most first-semester university courses.

YouTube is a genuinely valuable learning tool for programming when used deliberately rather than passively. Channels such as Corey Schafer for Python fundamentals, Traversy Media for web development, and freeCodeCamp for structured full-length courses provide instruction that rivals anything in a paid course. The distinction worth making is between watching and doing: a video watched without the code editor open alongside it produces a fraction of the learning that comes from pausing, attempting the implementation independently, and only then watching how it is done.

2. Use LeetCode to build problem-solving fluency

LeetCode is a platform containing thousands of programming problems organised by difficulty and topic, arrays, strings, linked lists, trees, dynamic programming, and more. It is used extensively by software engineers preparing for technical interviews at technology companies, and it is among the most effective tools available for developing the kind of algorithmic thinking that computer science degrees are built around.

For a high school student preparing for university, LeetCode serves a purpose that goes beyond interview preparation. Working through its problems systematically builds two things that classroom programming rarely develops: comfort with fundamental data structures, and the ability to reason about the efficiency of a solution, not just whether it works, but how well it scales as the input grows. These ideas appear in the first year of every serious computer science degree, and arriving already familiar with them is a substantial advantage.

Start with Easy problems and work through them by topic rather than at random, all array problems, then all string problems, then linked lists. The goal at this stage is not speed but pattern recognition: understanding that certain problem types yield to certain approaches. When a problem resists you for more than twenty minutes, look at the solution, but do not simply read it. Close it, wait ten minutes, and write the solution from memory. The ability to reconstruct an approach from understanding, rather than recall it verbatim, is the difference between having seen a solution and having learned from it.

NeetCode, a YouTube channel and associated website, provides structured roadmaps through LeetCode's problem set, with video explanations that focus on the underlying pattern rather than just the answer. For a student beginning with LeetCode, NeetCode's roadmap is the most efficient path through the material. It organises problems into the topic groups that matter most and explains them at a level that assumes no prior computer science education.

3. Understand the fundamentals, not just the syntax

A student can learn to write Python in a few weeks. Understanding why certain approaches to a problem are faster than others, how memory is managed when a program runs, what a data structure actually is and why the choice between them matters, these take longer and require deliberate attention. They are also what computer science degrees are fundamentally about, and students who arrive with some conceptual grounding in them engage with the early material at a qualitatively different level from those encountering it for the first time.

The core concepts worth developing before university are: variables, control flow, and functions (the mechanics of any language); arrays and lists (how collections of data are stored and accessed); recursion (a pattern of thinking that appears throughout computer science and takes time to become intuitive); and time complexity, the idea that two programs can produce identical output but differ dramatically in how efficiently they do so. None of these require a university course to understand at a useful level. CS50x covers all of them, as do introductory textbooks and dozens of well-regarded YouTube channels.

For computer science concepts: CS Dojo and William Fiset cover data structures and algorithms clearly at an introductory level. For Python specifically: Corey Schafer's tutorial series is among the most thorough and well-explained available anywhere. For system design and how large-scale software works: ByteByteGo explains software architecture in a way that is accessible without being superficial. For LeetCode preparation: NeetCode is the standard reference. Watching any of these passively is of limited value, the learning happens when the video pauses and the code editor opens.

4. Build something real

There is a ceiling to what tutorial-based learning produces. At some point, ideally before university begins, a student needs to close the tutorials and build something of their own design: a project with a purpose they chose, a problem they found interesting, a tool they actually want to use. This is where programming competence becomes programming fluency, because it forces decisions that tutorials make on the student's behalf, how to structure the code, which libraries to use, how to handle inputs the program was not designed for.

The project does not need to be impressive. A simple web scraper, a command-line game, a personal finance tracker, a script that automates something tedious, any of these, built independently from conception to working product, demonstrates capability that tutorial completion cannot. It also produces something concrete: a GitHub repository that, by the time a student applies for internships in second or third year, shows a history of genuine engagement with the craft rather than a list of courses completed.

5. Develop comfort with mathematics and logical reasoning

Computer science is a mathematical discipline, and students who underestimate this find themselves in difficulty from the second year onward. Discrete mathematics, the study of logic, sets, graphs, and combinatorics, is the mathematical foundation of algorithms and data structures, and it appears in some form in every serious computer science curriculum. Probability and statistics underlie machine learning and data science. Linear algebra is the language of graphics, computer vision, and neural networks.

For a student still in high school, the most useful preparation is not attempting to pre-study these subjects in full, but ensuring the mathematical habits required to engage with them are already in place: comfort with formal logical reasoning, the ability to read and construct a basic proof, and familiarity with the kind of abstract thinking that distinguishes mathematical argument from arithmetic. HSC Mathematics Extension 1 or Extension 2, taken seriously rather than treated as a hurdle, develops all of these, which is one of the strongest arguments for choosing the most demanding mathematics available in Year 11 and 12.

6. Start thinking like a software engineer

The difference between a student who can write code and one who thinks like a software engineer is not technical, it is dispositional. Software engineers approach problems by breaking them into smaller problems, identifying the cases their solution needs to handle, thinking about what happens when something goes wrong, and asking whether there is a simpler way to achieve the same outcome. These habits are not taught in a single course. They develop through sustained practice with problems that resist easy solutions.

Reading about how real software systems are designed, how companies like Google, Atlassian, or Canva architect the products that millions of people use, builds this orientation in a way that practice problems alone do not. The ByteByteGo YouTube channel and its associated book System Design Interview are among the best resources for developing this perspective accessibly. Understanding that the code written in first year is the foundation of systems that eventually need to serve thousands of concurrent users changes how seriously a student engages with the fundamentals from the outset, and that seriousness, established early, is what separates the graduates who are ready for industry from those who are not.

At Shoreline, computer science students are often surprised by how much ground can be covered before university begins, and how directly that preparation translates into first-year performance. The students who arrive having worked through CS50x, built a personal project, and spent consistent time on LeetCode are not just ahead in the first semester; they spend their entire degree building on a foundation their peers are still constructing underneath them. The resources exist. What most students need is help identifying the right ones, building the study habits that make self-directed technical learning sustainable, and understanding early enough that the time between Year 11 and the first university lecture is not a gap to wait out, it is the best preparation window they will have.