Essential Computer Science Terms Every New Programmer Should Know
As an experienced full-stack software engineer, I‘ve worked with numerous programming languages, frameworks, and tools across my career. I‘m often asked for advice from aspiring developers on what they should learn to succeed in this field.
While the specific tech stack you focus on will depend on your interests and career path, there are some fundamental computer science concepts that every software developer should have a solid grasp on. In this in-depth guide, I‘ll share my perspective on the most essential terms and why they matter, informed by real-world experience and industry data.
Programming Languages
Perhaps the most important decision you‘ll make early on is which programming language(s) to learn. Different languages are suited for different domains and offer tradeoffs in terms of performance, development speed, and learning curve. Here‘s an overview of some of the most in-demand languages in 2022:
| Language | Popularity Rank | Average Salary | Common Uses |
|---|---|---|---|
| Python | 1 | $120,000 | Web dev, data science, scripting, automation |
| Java | 2 | $104,000 | Enterprise apps, Android mobile, backend |
| JavaScript | 3 | $112,000 | Frontend web, backend (Node.js), cross-platform |
| C# | 4 | $97,000 | Windows desktop, game dev, VR/AR |
| C++ | 5 | $101,000 | Game engines, operating systems, embedded |
Source: Stack Overflow Developer Survey 2022, Glassdoor
As you can see, these versatile languages each offer lucrative opportunities across different domains. My advice would be to start by learning the language that aligns best with the type of development you‘re interested in (e.g. Python for data science and backend, JavaScript for frontend web).
Personally, I started my career focused on backend development with Java. Its verbosity and rigid object-oriented architecture provided a helpful foundation. Over time, I added Python for data analysis and scripting, JavaScript for frontend web apps and Node.js backend, and dabbled in others like Ruby and Go.
In my experience, once you grasp the core concepts of programming in one language, you can apply them to learn others more easily. Focus on mastering one language to start, then branch out to add complementary skills.
Data Structures & Algorithms
Another cornerstone of computer science is data structures and algorithms. These concepts lay the groundwork for writing efficient, optimized code that can scale.
Some key data structures every developer should be familiar with include:
- Arrays & Linked Lists
- Stacks & Queues
- Trees & Graphs
- Hash Tables
- Heaps
Choosing the right data structure has a substantial impact on the performance and memory usage of your programs. It‘s also crucial to understand the tradeoffs of each. For example, arrays offer fast random access and cache-friendly contiguous memory, while linked lists are better for frequent insertions/deletions in the middle of a sequence.
Alongside data structures, you‘ll need a toolkit of algorithms to operate on them efficiently. Some must-know algorithms include:
- Search: linear search, binary search, depth/breadth-first search on graphs
- Sort: bubble sort, insertion sort, quicksort, merge sort
- Hashing: hash functions, collision resolution techniques
- Dynamic programming: memoization, tabulation
- Greedy algorithms
I frequently use Python‘s built-in list (dynamic array), dict (hash table), and set data structures in my day-to-day work. For processing large datasets, I‘ll turn to libraries like NumPy for optimized matrix operations or Pandas for tabular analysis.
When it comes to algorithms, you can‘t go wrong investing time in mastering a few versatile workhorses like binary search and quicksort. I‘d recommend practicing implementing these manually in your language of choice. LeetCode and HackerRank are great resources to hone your algo skills.
Object-Oriented Programming
Object-oriented programming (OOP) is a paradigm based on encapsulating data and behavior into "objects" that interact with each other. It‘s a powerful approach for modeling real-world entities and relationships in code.
The four pillars of OOP are:
- Encapsulation – Binding data and methods together in a class, hiding internal state
- Abstraction – Exposing only essential features of an object, hiding complexity
- Inheritance – Deriving new classes from existing ones, enabling code reuse
- Polymorphism – Objects can take multiple forms, enabling flexible, decoupled design
OOP is supported by most modern programming languages, though some (like Java) enforce it more strictly than others (like JavaScript). Some common OOP design patterns I‘ve used extensively include:
- Singleton – Ensuring only one instance of a class is created
- Factory – Creating objects without specifying the exact class
- Observer – Notifying multiple objects of state changes
- Decorator – Dynamically extending an object‘s functionality
- Model-View-Controller (MVC) – Architectural pattern for user interfaces
When used judiciously, OOP allows you to write modular, maintainable, and scalable code. It‘s especially valuable in large, complex applications developed by teams of engineers (like most enterprise software).
However, OOP is not a silver bullet and overuse of deep inheritance hierarchies and excessive abstraction can lead to bloated, over-engineered code. In my work, I try to balance OOP principles with pragmatic design and judicious use of functional programming techniques.
Databases & Data Modeling
Most applications require a persistent way to store and retrieve data. Relational databases like MySQL, PostgreSQL, and SQL Server have long been the default choice, using SQL (Structured Query Language) to define and manipulate schemas.
In recent years, NoSQL ("not only SQL") databases have surged in popularity, particularly for web applications with unstructured or rapidly changing data. Popular NoSQL databases include:
- MongoDB – Document-based, stores semi-structured data in JSON-like format
- Cassandra – Wide column store, excels at fast writes and horizontal scalability
- Redis – In-memory key-value store, often used for caching and real-time analytics
- Neo4j – Graph database, models data as nodes and relationships
Choosing the right database depends on your application‘s read/write patterns, scalability needs, and data model. SQL databases enforce a rigid schema and ACID transactions, while NoSQL offers more flexibility and scalability at the expense of strong consistency.
Whichever database you use, data modeling – structuring your data effectively – is a key skill. Normalization, denormalization, indexing, and sharding are all techniques to optimize read/write performance for your application‘s access patterns.
I‘ve used MongoDB heavily for quickly prototyping web apps with JavaScript, enjoying its flexibility and natural fit with JSON data. As apps scale up, I often add a Redis cache in front for blazing-fast reads of frequently-accessed data. With relational data, I‘ll turn to battle-tested PostgreSQL and write raw SQL for maximum control.
Version Control
Version control is an indispensable tool for any software developer. It allows you to track changes to your codebase over time, collaborate with other developers, and roll back to previous versions if needed.
Git has emerged as the de facto standard for version control. Its distributed model means every developer has a full copy of the repository locally, enabling offline work and flexible workflows. Key Git concepts to understand include:
- Repositories – The complete history of your project files
- Branches – Isolated lines of development, e.g. for working on features or fixes
- Commits – Snapshots of changes to files at a point in time
- Push/Pull – Syncing changes between local and remote repositories
- Merge – Combining branches back together, resolving conflicts
Collaborating via Git typically involves branching off main to work on features/fixes independently, then opening a pull request (PR) to merge the branch back to main after review by team members.
Platforms like GitHub, GitLab, and Bitbucket have become hubs for the open source community and many businesses to share, discover, and contribute to projects. I make a point to contribute to open source when I can, as it‘s a fantastic way to build real-world experience, learn best practices, and demonstrate skills to potential employers.
If you‘re new to Git, I‘d suggest getting comfortable with the basic command line operations before moving on to a GUI tool like GitHub Desktop or GitKraken. Mastering Git is a core skill for collaborating on modern software teams.
Testing & Debugging
Testing and debugging are integral to the software development process. Writing comprehensive test suites helps catch bugs early, ensure correctness as your codebase evolves, and functions as living documentation of expected behavior.
Some common types of testing include:
- Unit testing – Verifying individual functions in isolation
- Integration testing – Checking that several units work together
- End-to-end (E2E) testing – Validating behavior from the user‘s perspective
- Performance testing – Measuring responsiveness and resource usage under load
- Acceptance testing – Confirming that features meet specified requirements
Automated testing has become a standard industry practice, with tools like JUnit, pytest, Jest, and Cypress enabling developers to efficiently run large suites with each change. Many teams practice test-driven development (TDD), writing the tests before the actual implementation code.
When tests fail or users file bug reports, you‘ll need to put on your detective hat and debug. Some techniques I turn to include:
- Print debugging – Liberally logging state to the console to trace issues
- Debugger – Using breakpoints to pause execution and inspect variables
- Rubber duck debugging – Methodically explaining the code line-by-line to identify flaws
- Binary search – Commenting out chunks to narrow down the offending region
Investing in your debugging skills will pay dividends in reclaimed time and fewer frustrated nights banging your head on the keyboard.
DevOps & Deployment
As applications grow more complex and scale to larger user bases, streamlined development and deployment processes become vital.
DevOps (Development + Operations) is the practice of automating the integration, testing, and deployment of code changes. The goal is to rapidly, reliably, and repeatedly move code from development to production with minimal manual intervention.
Some key DevOps concepts include:
- CI/CD – Continuous Integration/Continuous Deployment, automatically testing and deploying every commit that passes
- Infrastructure-as-Code (IaC) – Defining infrastructure like servers and databases as versionable code
- Containerization – Packaging an application and its dependencies into a standardized unit for consistency across environments
- Orchestration – Automating deployment, scaling, and management of containerized apps
- Monitoring – Visibility into an application‘s performance and health, proactively identifying issues
Tools of the trade include Docker for containerization, Kubernetes for orchestration, Terraform for IaC, Jenkins or CircleCI for CI/CD pipelines, Prometheus + Grafana for monitoring, and Splunk or ELK stack for log aggregation.
The DevOps landscape is rapidly evolving and can be intimidating for new developers. My advice would be to start with the fundamentals – learn how to package your app as a Docker container, deploy it to a platform like Heroku or AWS Elastic Beanstalk, and set up a basic CI pipeline to run your tests.
As you progress in your career, you‘ll likely work closely with dedicated DevOps engineers who can help implement more sophisticated infra. Having a baseline understanding will help you collaborate more effectively and appreciate the challenges they face.
Parting Thoughts
This whirlwind tour covers some of the most important computer science fundamentals I think every developer should be conversant in, based on my experience as a full-stack engineer and conversations with many colleagues.
Of course, this only scratches the surface and there‘s much more to learn in areas like computer architecture, networking, security, and specialized domains. You could spend years diving deep on any one of these topics – and many people do!
The good news is that you don‘t have to master everything at once. Start with one area, go deep enough to build something real, then expand your knowledge as the need arises. Pursue what genuinely interests you and don‘t be afraid to step outside your comfort zone.
Some of the best engineers I know are endlessly curious and always leveling up their skills. They read voraciously (both code and prose), take on challenging projects, contribute to the community, and learn from their failures. You can do the same – it just takes dedication and grit.
Remember – a career in software is a marathon, not a sprint. Focus on consistently growing and honing your craft, and you‘ll go far. I‘m rooting for you!
Some resources I recommend for going deeper:
- Teach Yourself Computer Science – https://teachyourselfcs.com/
- Open Source Society University – https://github.com/ossu/computer-science
- Four Semesters of Computer Science in 5 Hours – https://frontendmasters.com/courses/computer-science/
- Base CS Podcast – https://www.codenewbie.org/basecs