Regardless of how you found yourself in the tech space, there’s ofcourse many different ways to contribute to software development and yet having a clear reason why you have chosen to learn to code will be very important in how your entire journey pretty much plays out.

I started working with HTML/CSS like a lot of developers and two things I quickly observed about the tech space is one - Software is everywhere, making software development one of the most bankable skills; and secondly, I noticed all the secondary skills I was developing as a result of starting to code. These are skills that directly and indirectly compliment problem solving which I was able to apply and bring my ideas to life. On the flip side, one of the challenges I had to contend with was the occasional feelings of inadequacy as a result of the steep learning curve.

If you have read through different developer stories, you will not be surprised to learn that programmers, even those we admire, also faced these challenges admitting to making a lot of mistakes and getting lost countless times when learning a new language or working with multiple frameworks, libraries and tools.

Amazingly this is one of the really good things about experimenting with code. Its that, if anyone applies patience, persistence and consistency in their learning regiment they’ll most likely get to the hard part of learning to code which for me was learning to apply the principles or components of computational thinking in the way that I write and test code and the way I look at efficient software development.

Computational Thinking for Developers

Computational thinking is a way of solving problems where a solution (of which there may be many), can be executed by a computer. For developers, it is the thought process that you run through even before you start writing code. At a high-level each of its principles will guide your software design and development from phase-to-phase until you've created a viable, real-world solution to a problem.

Whether you’re building a web-based solution, an iOS application, deploying containers or even managing libraries, it’s generally recommended that you apply these principles because combined they will enable you to determine how good you are getting at writing and testing code.

Decomposition

To illustrate how each principle is applicable, note that developing and delivering software products is a challenge that requires an understanding of the variation of project sizes and complexities. With decomposition, as a programmer you’ll be able to break down these complexities into smaller, more manageable problems using exercises like user-story decomposition, code decomposition and product release decomposition.

By breaking down each project variation feature into simpler unit tasks, you’ll:

• Be able to make more regular progress towards your product release goals
• Get additional people involved with accomplishing the larger goal when work is broken into isolated items done in parallel.
• Avold analysis paralysis where without the constraints of smaller units of tasks, complexity of discovering, understanding, validating and implementing larger tasks will lead to overthinking, overdesigning and accumulating work we may never need.

This process is a continual exercise from early feature ideas, to low-level code changes and product-releases lead-times, decomposition makes this work more manageable. The next time you review your use story, you’re in your codebase or creating your release timeline, look out for the types of decomposition you could use.

Pattern Recognition

This is the mapping of similarities and differences among decomposed problems. It helps with finding commonalities of repetition which is beneficial for making predictions, working efficiently and building a strong foundation for designing algorithms. As a developer you’ll apply pattern recognition extensively to make connections between similar problems in large amounts of data in an efficient way.

One way this can be reflected in your code is the use of a Nested For Loop to allow rapid data sorting or insertion. Its logical structure provides a simple way to cause sequential events to build upon each other and although its processes vary between programming languages (Python, Java, JS), find examples available that you can use to practise writing these scripts.

There’s also multiple pattern recognition tools that are used for computation of dissimilarities, feature extraction, cluster analysis and more. The most common use cases of pattern recognition are in the design and development of data analysis solutions, Natural Language Processing, document clarification and facial recognition.

Abstraction

Here it is recommended that every time you start writing code, you should identify the complexity in your solution that users do not need to interact with and “hiding” it within your code functions, interfaces, objects and implementations in layers. This process is called abstraction and it essentially splits one function into multiple interconnected functions.

Applying abstraction skills is vital for developers working in collaborative long term projects to ensure continuous expressiveness of the code you’re writing. This is helpful when designing code, to determine which processes happen when or where, or to identify the processes that depend on each other. The identification of important information will enable you to leave out a lot of unnecessary code, you’ll be able to write documentation and leave code comments.

All these different tasks will require you to be able to identify important information to a user at any given time. It will also enable you to assist the users get familiar with the product in the shortest time possible. As a developer I have gone to certain documentation and the ones I found easiest to use are from software and products that I’m still using to date which is to say good abstraction goes a long way.

Algorithmic Thinking

This is a combination of all the three elements we’ve covered so far. It involves designing algorithms that can determine the appropriate steps to take and organizing them into a series of instructions for solving a problem correctly. Each algorithm typically has a starting point, a finishing point and a set of well-defined instructions in between.

A practical use case of algorithm design in code writing is with the development of solutions that are increasingly used to automate software development processes for efficient creation and deployment of the product. For example with GitHub Actions, which is a product of algorithm design, users can build simple workflows for code reviews, branch management and issue tracking which will in turn level up the CI/CD pipeline.

Being able to apply all these solutions within your code development and developer journey will enable you to share your solutions with users quicker, you’ll improve the quality of the softwares we bring to market.

The Place of Community

Community is one of the better outcomes of learning to code, because they are groups of like minded individuals who come together to solve problems and offer different perspectives on particular technologies, in the right community you’ll:

• Get access to up-to-date information
• Receive insightful answers
• Get tips & tricks for all-level programmers
• Get links to resources, talks and research papers
• Meet new friends, coding partners, mentors and/or business partners

When you join a community encourage yourself to participate by focusing on the specific challenges you are trying to solve. Create opportunities for great interactions, to learn and to grow.