A Guide to Mastering Computer Science

A Guide to Mastering Computer Science

Mastering computer science in 2022 – how hard can it be? Perhaps you’re looking to equip yourself with the necessary tools and know-how to establish your own tech startup. Maybe you want to stand at the frontline of defense against cybercrime. You might be interested in constructing your own programs or algorithms. It could be that you have an affinity for data mining and analysis. 

In any case, computer science is the way forward. 

If you want to learn the subject, you probably have a few questions. 

  • What programming languages should I be learning? 
  • Are there any other skills that I need to know? 
  • Where can I learn all of this? 

There’s a decent amount of information online, but it can be difficult to ferret out the facts from the fluff. Let’s make things easier by mapping the path to mastering computer science and understanding a little more about what you can expect working in the industry. 

The ICT Sector at a Glance

According to the Government of Canada’s 2018 ICT Sector Snapshot, the industry in question earned $193 billion in revenue in 2018 and experienced a 5.9% growth in size and 4.6% growth in employment. The latter growth rate has consistently outpaced the overall national economy since 2012. 

Average earnings for employees in computer science were over $77,800 per year and have undoubtedly risen since then. The median annual salary is almost 50% higher than the economy-wide average. More specialized roles earn far more. 

To sum it up, you can expect a great salary, high job security, and plenty of advancement opportunities working in this sector. But you need to know your stuff. Moreover, you need proof. Here’s how you can achieve that. 

Phases of Computer Science

We can separate the learning process of computer science into three consecutive phases, namely: 

  • Coding
  • Programming
  • Computer Science

This distinction is not necessarily a universal way forward. See it instead as a guideline that you can follow to get a better idea of what steps to take and when. 


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For most software engineers, it was coding that kick-started their career. Learning to understand and speak the language of machines is foundational knowledge. It’s also the easiest phase, so if you struggle or find it uninteresting, you may want to consider shifting your career path to something more suitable. 

If you choose to stay in this stage (many software engineers do), you can only really expect an entry-level position. Regardless, it’s paramount that you gain a strong grasp of what is taught here. 

Your first step will be to choose a programming language. 

The specific language you opt for is not as important as the principle of learning it, which is to understand the concepts that hold true across the board. Over time, your proficiency will increase to a point where you can learn new languages within a few weeks or less, so there’s no need to fret over finding the “perfect language.” 

With that said, here are the two most widely recommended options: 


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One of the biggest reasons for Python being a top choice among aspiring programmers is how easy it is to learn. It’s also a high-level language that allows you to create functioning applications with a small amount of code. This means that you’ll quickly reach the point where you’re developing large projects. 

There are several ways to learn Python. You can take the traditional route and read a few books while experimenting on your desktop. A more hands-on (and arguably more enjoyable and effective) approach is to learn through online courses. Codecademy, Coursera, Udemy and Khan Academy are popular sites. 


Aside from being a valuable addition to your resume, Java can teach you concepts that Python doesn’t. For instance, the former is statically-typed, while Python is dynamically-typed. Both languages are in high demand, so it’s good to know them. Once you do, it will be time to move on to the next phase. 


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The main focus here is being able to write code that’s efficient and error-free. Here are five skills that you should learn during the programming phase: 

  1. Knowing how any code can be made into something that is legible and executable. 
  2. Awareness of a system’s limitations and building software accordingly.
  3. Understanding data structures and algorithms.
  4. Identifying inefficient code. 
  5. Proficiency with quality and testing procedures.

At this phase, you can expect to land a job at one of Silicon Valley’s residents. The likes of Google, Facebook, and Uber are more interested in your abilities as a programmer, not a coder. 

It’s often suggested to begin your reading with a book called The Elements of Computing Systems. This will provide essential information, specifically regarding the software stack. 

Introduction to Algorithms is another worthwhile read. The material will give you a better understanding of algorithms and data structures. This is key to gaining awareness of system limitations and writing code that is fast and light on resources. At this point, it’s worth noting that your programs will seldom run on a single computer. 

That’s why it’s important to learn about networks, as our increasingly connected world requires code that can speak to other machines. A well-written source of knowledge here is Computer Networking: A Top-Down Approach. 

Your final step before moving on to the third phase will be to gain a deep understanding of operating systems. It’s a dense topic, so try to focus on the fundamentals and avoid getting lost in the details.

Computer Science

Now that you’ve mastered programming, you can start becoming an architect who considers projects in their entirety as opposed to looking at minute details. Much of this phase involves using your knowledge to build scalable, error-tolerant systems capable of handling large loads. 

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Here, you’re always learning and staying on top of new trends. That’s part of what makes computer science exciting. Most professionals at this phase are in charge of entire projects, managing other programmers, and overseeing their work. Having strong social and leadership skills will be to your advantage here. 

We can break up your learning into two distinct subjects:

Distributed Systems

This refers to building scalable and tolerant software, which requires being able to foresee the potential problems and challenges the program will face and coding for them accordingly. A search engine is a simple example. You need to ensure that it can handle high numbers of requests, as well as what it should do if the host fails. 

Mobile computing is a major trend right now, but it’s not without its challenges, as explained in this post at Wilfrid Laurier Online. This is one area that highlights the importance of understanding distributed systems. The university also offers a Master’s in Computer Science, which is naturally a must-have at this phase of your career. 

Machine Learning

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If you’ve spent any time online in the past few years, you’ve probably heard about machine learning. This field can be classified as interdisciplinary, as it involves not only computer science but also statistics and mathematics. Having a strong understanding of the latter two subjects is paramount to your progress. 

Aside from obtaining a degree, you can speed up your learning through online courses during your spare time. But having accredited and recognized certification of your competence is necessary for landing any jobs, as recruiters are unlikely to just take your word for it. 

With some prior knowledge of mathematics and probability, you can take on a book such as An Introduction to Statistical Learning to strengthen your foundation. 


Mastering computer science isn’t easy, but it’s doable. The above-listed points can be dissolved into nine key subjects. The most common recommendation is to spend between 100 and 200 hours on each, revisiting your favorites as you gain a better understanding of what you enjoy most. Here are said subjects:

  1. Programming
  2. Computer Architecture
  3. Data Structures and Algorithms
  4. Mathematics
  5. Operating Systems
  6. Networking
  7. Distributed Systems
  8. Languages and Compilers
  9. Databases

This may seem like a ton of work, but it will be split up throughout your career. As you move up the ranks, you can dedicate some spare time to learning new subjects before obtaining recognition of your knowledge and taking the next step forward. 

Why Master Computer Science?

So, why go this far? Well, let’s say that there are two types of software engineers:

  • Those who get by with their understanding of high-level concepts. 
  • Those who know computer science well enough to enjoy challenging and innovative projects. 
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Both are rightfully software engineers and earn roughly at the same paycheck, at least towards the beginning of their careers. However, the latter enjoy more fulfilling and higher-paying work, driving breakthroughs, and taking the field to new heights. Doing that requires a dedication to lifelong learning. It takes discipline, but it’s far more rewarding.

If you’re still closer to the first phase of computer science mastery, it’s safe to say that you still have a long way to go. But don’t let this discourage you. Reaching the top takes time, effort, and dedication for a reason. Once you’re there, you’ll only gain more opportunities and your career will be more prosperous than you could ever imagine. So, get to work. 

How can I fully master computer science?

Your true question is how you can consume all the candy in a candy store, and the answer is that you can’t, at least not all at once. A career is a sequence of occupations that you do throughout the course of your life. Your first goal is to execute the work, and your second goal is to have the abilities necessary to capitalize on chances when they emerge. Your inquiry is about abilities, not ambitions.
Normally, years of experience and study are required to master a specialism. The typical academic path is an MSCS for the professional and a Ph.D. for the researcher. You accomplish this by focusing on one specialism at a time.
If you want particular help, add your aims when you ask a broad question like “how can I totally master?” Based on your experience, I would recommend MOOCs for certification/specialization/nano degree when you have narrowed your focus to a single specialization. I also recommend Georgia Tech’s OMSCS program.
if you want to keep things technical.
Bottom line: You’re in the middle of the lake, attempting to figure out how to get to dry land without knowing which paddle to use.

What makes a Master’s in Computer Science (MS CS) degree worth it and why?

In general, a Master’s degree in computer science will not compensate for lost years in a high-growth job position. If you have a choice between acquiring a Master’s degree and working for a high-growth engineering-oriented technology company, you should normally choose the latter, especially if you are not self-sufficiently rich.
However, there are specific situations in which you SHOULD obtain a master’s degree.
If you want to immigrate to the United States, a master’s degree is a must. It is quite difficult to move to the United States without first attending school here. Obtaining a STEM degree from a university will allow you to work in the United States and put you on the path to permanent residency (if you subsequently get a work visa).
If you attended a lower-ranking institution and have the opportunity to earn a master’s degree from one of the top schools, it may benefit your brand. Of course, if you can do this, you should be able to secure a job with a fast-growing engineering firm, and the latter is likely to be a better bet overall.
Going to school can be a lot of fun if you are independently rich. Living on the Stanford or MIT campuses can be exciting and will introduce you to new people. Of course, if you are independently affluent, you most likely already have a strong network and a plethora of exciting prospects.
Overall, very engaging, growth-oriented work is likely to outweigh a graduate degree.

Is a career in computer science worth it?

Saying you’re a software engineer is like saying you’re a writer. One may be JK Rowling or the author of a blog that no one reads. They are both technically ‘writers,’ but they are worlds apart.
So, certainly, we have an army of software engineers working in low-skilled jobs at IT firms, surviving on the wage disparities between India and the United States. But, if that were all there was to software engineering, why would Google and Facebook hire swarms of high-paying software engineers in California?
Although India has millions of software engineers, where does all of the innovation come from? Has an Indian created a major software language such as Python, Ruby, Perl, or C? How about web frameworks such as Rails or Django? What about Numpy and Pandas libraries? I could go on forever.
Even US organizations that outsource work too our IT firms frequently have crucial work completed locally by highly skilled programmers in the US. A large portion of what is outsourced is low-skilled grunt jobs. I’m clearly generalizing, but this is generally true.
Unfortunately, the story of software engineering in India is similar to that of most other disciplines, whether sports, technology, or business. We have an army of folks with low and mid-level skill sets. However, we are lacking at the high end of the skill set. Our greatest engineers are being poached by the world’s leading engineering institutions and firms.
Back in India, many of our new graduates starting out as software engineers are solely concerned with frequently transferring professions in order to achieve that ’30 percent wage boost,’ rather than becoming exceptionally skilled at what they do. People from other countries have made fun of the low quality of code generated by Indian engineers, which should make us all very upset.
It’s not that Indians aren’t capable. We share the same genetic code as everyone else. My guess is that Indians make up roughly 20% of the MS/Ph.D. the student population at many top US engineering schools. Back home, though, perfection is not a priority; instead, we settle for a low degree of equilibrium.
It may appear harsh. However, avoiding reality will not make it go away. It’s painful, but we have to experience it and do something about it.
What does the future of computer science hold?
Let us turn to the past to predict the future. What is the greatest significant change in our life during the last 100 or 500 years? We can still paint, draw, and cook just as well as we used to. One may argue, for example, that the nawabs of Awadh ate finer food than we do today.
Technology is what has changed. Everything we do is powered by technology, from how we communicate with others, how we travel, how we consume entertainment, to how we receive medical treatment from our doctors. And, of all fields of technology, software reigns supreme.
Software now manages our lives 24 hours a day, seven days a week, and will continue to do so in the future. NASA and ISRO rockets are powered by a massive amount of software. Our planes are advanced flying computers. When you become ill, your blood is tested by some fancy technology, which is nothing more than a computer at its core. Without software, automobiles cannot be made, hospitals cannot function, and even your next-door restaurant cannot bill you for meals.
The world’s largest corporations are de facto software companies: Apple, Facebook, Amazon, Google, and so on. The industrial behemoths of yesteryear are no longer in the same category.
The next 100 years will be dominated by software. The software will govern everything, from data science to machine learning, medicine to biotechnology. And in order to win, you must be on the cutting edge.
The true game does not take place at the low end. It was never the case. The world does not treat foot troops well. Unfortunately, we live in a world where the winner takes all.
You will be the envy of the world if you are at the cutting edge of computer science. But if we keep moving jobs for that 30% raise, we’ll be wiped out sooner or later by someone with a stronger thirst for knowledge and a drive for greatness. Make no doubt about it.

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