Adopt an Attitude of Always Learning 

Data science is a constantly evolving discipline. New frameworks, packages, research, tools and datasets are being released. Thanks to the industry’s vibrant ecosystem there’s never a shortage of the new and cutting edge to learn. Stay hungry to learn these skills and new technologies. Companies are constantly looking for innovative new tools to cut costs, increase efficiency and improve the quality of output. Proving you’re the type of person that likes to keep up to date with these kinds of things is a fantastic signal to the company that you may be able to potentially improve the way things are done in the future. Furthermore, it presents you as being deeply submerged in the space. 

Managers want you to be able to teach them something new as well as vice versa. Injecting new ideas into the team increases the team’s cognitive diversity and makes the team more likely to find the best methods to solve problems going forward. 

Whilst keeping up to date with the most recent developments in Data science, be it in machine vision, natural language understanding, reinforcement learning or any other field is important, so too is showing interest in ideas outside of the world of data science. Smart people have the common characteristic of being curious and wanting to know how and why things work in my experience. For example they become enveloped in some obscure historical period in time or become obsessed with wanting to know how the human body works, random knowledge like this is often found in data scientists. If that sounds like you then that’s great. You’re curious and enjoy learning things.


Get Good First

All too often people get inpatient and want to begin earning professionally full time. They may have completed an online course, completed a couple of projects, learned the fundamentals of programming, then decided to apply for a data scientist role a few weeks or months after.

My advice is avoid doing this for two main reasons: 

Firstly, if you receive an interview it will become apparent that you have a superficial understanding of the concepts at hand, you may know all the buzzwords, but not know how to clearly articulate what the concepts actually mean and how they interact. You could be setting yourself up for disappointment, as valuable as the interview experience may be. 

Secondly, if you do receive a job you may end up convincing yourself that you really do know a lot, and in my experience this attitude is dangerous. Especially when you forgo the opportunities to learn as a result. 

Having a strong, well crafted understanding of the fundamentals will mean you’ll enjoy the work more, you’ll have greater earning potential and give you a base for a long and meaningful career. Lest not forget, it’s a competitive market and the likelihood is there’s going to be stronger candidates out there somewhere, give yourself a fighting chance by learning the basics well.


Reward Yourself For Solving Problems

The life of a data scientist can be filled with questions and very few solutions.That’s why every time you solve a hard problem, celebrate. Celebrate like you’ve just unearthed a gleaming nugget of gold in the rubble.  How can you celebrate? Make a cup of tea, go treat yourself to something nice to eat, go for a walk, whatever works for you. By celebrating, appreciating and respecting the problem at hand, it’s also a good sign that you’re not setting super high expectations of yourself. 

Social rewards are also useful, if you can find someone who is interested, show them what you achieved. Much of the value of a university environment is the people and academic social culture that you’ll find yourself surrounded in. The inherent want to prove your capabilities to peers is a powerful motivational force, find a way to simulate it and once the social reward mechanism kicks in you’ll find yourself pushing harder to learn. Read on to find out how best to do this.


The Internet Will Know

I recall having a conversation with a colleague that went along the lines of,  “How did anyone ever get anything done before Google?”. The truth is I doubt that I would be a data scientist if it wasn’t for Google’s magic hand. Whilst there’s not much more to say here besides, “Google it”, there are some tips and tricks that may be of use. 

The first is, copy and paste the errors returned into Google. Remove the parts that are specific to your problem like ID’s or column names, but keep the rest. At the start of your career you’ll find no shortage of explanations and solutions. 

Right click, then use “open in new tab” on the search results. Open 3-6 tabs, this will help you quickly cross reference answers without having to keep clicking back to the search results. 

Close non-relevant unnecessary tabs before you make a search, these tabs just create more cognitive load that you don’t need. At the start I used to think that I’ll come back to this page so i’ll keep it open, 90% of the time I never did. If it’s a really important page then bookmark it. Worst case you can always pull up the page from your history.

Split screens work, always have an internet window accessible with Google opened to quickly search stuff whilst you’re working on a problem. If you don’t have multiple screens; most modern operating systems let you split your single screen. 

Try not to overly rely on Google too much. If you find that you are constantly just copying and pasting code without understanding what’s going on then that’s a bad sign. Try and read multiple solutions, read information about the code as well as the code itself, once you are happy & have a rough idea of what’s going on – write your own code using the answers you found as an outline. You may think you are saving time by copying and pasting an answer from or Github, however you might be wasting time in the long run if you can’t come up with it yourself in the future.


Develop a Problem Solving Protocol

A highly tuned, slick, problem solving protocol will become your most valuable asset and mechanism. You’ll depend on this skill to get you a job and employers will test it. A protocol to solve problems can also prevent you from becoming overwhelmed or lost. There is not one single protocol for all problems, however having a loose idea of steps you need to take in-order to solve a given problem is vital.

Here are some questions you may try to mentally answer as part of a problem solving protocol:

  1. “Do I know what i’m trying to solve? 
  2. “What does ‘solved’ even look like?”
  3. “Can I test my solution/s?”
  4. “Is this really a problem? Or can I reframe the task such that this problem doesn’t exist”
  5. “How likely is it that the solution is going to take a long time to figure out?”
  6. “Is this problem even worth solving?”
  7. “What’s the probability that someone has already solved this?”
  8. “What’s the google query most likely to provide me with a solution?”
  9. “Who can I easily ask, that is most likely to know the optimal solution”
  10. “How big is the possible solution space?”
  11.  “What’s the simplest possible solution?”
  12. “How can I rank possible solutions by which is optimal”
  13. “Which is most likely to be the optimal solution”
  14. “Is this one problem or can I split it up into lots of small solvable problems?”

Practical tip – write down your own current problem solving protocol as a list of questions. Then have a go at using that protocol to solve a problem you currently have. As you develop your problem solving attitude and protocol further, you’ll discover you’re capable of much more than you thought.


Develop a Problem Solving Attitude

There’s no doubt that there will be what appear like roadblocks. At the start, most likely you’ll find that your code won’t run, you’ll get an error that makes no sense. Your personal troubleshooting system may not be formalised yet and you end up giving up. You’ll tell yourself something along the lines of, ‘this is too hard for me’. The truth is it’s not, rather it’s a matter of mental resource allocation. More specifically about how much you’re willing to dedicate to the problem. As a data scientist you learn to dedicate a much higher percent of brain power to your work than you would for trivial tasks. For example, thinking about the quickest journey home may be 10% brain power and take you 1 minute. In the beginning for me, I found that I would need hours of intense concentration trying to fix a single error in my code. After much training and repetition you begin to develop an instinct for fixing bugs in your code, you’ll subconsciously assess the probability of a set of possible mistakes and reasons for the error. Then begin digging into the most likely, which is usually some sort of syntax error or data type error when you begin. 

The key message is that problem solving takes nothing but time and brain cycles, and you need to consecutively keep solving problems until you begin to develop an instinct. Which means you can’t avoid putting in the work. 

Given you need to dedicate this high level of mental resource to the problem you can’t waste brain power beating yourself up about how you can’t get it to work, this is usually a big distraction and could lead to your downfall (of your data science plans at least). That said it’s natural to do so and everyone does it to some degree.

Try to think of a problem as not a roadblock, but rather a problem pending solved status, that way you know it’s just a matter of time and brain power till you figure it out. 

Your perspective on a given problem can be somewhere on either two ends of a spectrum, the first – a problem that causes you pain and self doubt, a dead end of sorts. The other end, a fantastic opportunity for pleasure, the greater the amount of work you dedicate the more pleasure you’ll gain once it’s solved, like an existing holiday in the not too distant future. Rewiring the way you think like this takes time and can be difficult for some, so start with easy problems and don’t bite off more than you can chew. As the level of difficulty of the problems increases each time one is solved, you’ll gradually gain momentum for solving much harder problems. It’s very hard to get this momentum when you start straight off the mark with hard problems. 

A useful problem solving mental crutch is to psychologically unbound the amount of time and brain power you might dedicate to the problem, tell yourself something like,”I’ll work on this small problem for a year if I have to”. There’s something about complete acceptance that the problem will be solved that will help your brain find a solution faster. Or at least that’s been the case in my experience.


Understand Why You Want a Career in Data Science

I’m a strong believer in conviction over intelligence, I also believe conviction breads intelligence. How do you get convicted? This is where a little soul searching comes in, think about the context and your life holistically. There’s a reason why you’ve chosen to read this after all. What’s the why? Are you just interested in what it takes to be a data scientist? Are you here because someone told you it’s the sexiest job of the 21st century? Because you want to earn the big bucks? For the job title’s prestige? Maybe you’re bored of a job and want a challenge? It’s probably a mixture of things, I know it was for me. As long as that reason isn’t too concentrated around what I call, ‘non-intrinsic’ motives then you’re in good standing. Non- intrinsic motives are any external reasons outside of the satisfaction of actually doing the work. What’s the cutoff for the mix of intrinsic vs non-intrinsic motives? I don’t know but if it’s 50% money, 50% prestige then I don’t think it’s going to work… There needs to be some level of job satisfaction, some level of enjoyment, that’s what’s going to be the fuel for progress, the propeller for going on to implementing the practical tips on this site.

That said, I believe everyone has some capacity to get satisfaction from doing the type of work a data scientist does. However not everyone starts on an even playing field, it’s important to be pragmatic about that. Some people have a much bigger mountain to climb. 

The types of people I see going into this field generally have some element of mathematical experience, however, not all. Social scientists, economists, doctors, biologists, chemists, physicists, software engineers are common. However, I’ve seen great data scientists from a range of disciplines including journalists, historians and philosophers. Each with their unique edge they can inject into their data science skill set. Philosophers for example, tend to have the characteristic of inquisition, which becomes mighty useful when it comes to data expiration or just simply not assuming that the data received will be correct.

So what’s the practical tip here? Write down 3 reasons why you want to become a data scientist on some post-it notes, be honest with yourself, if one of them is related to money, that’s good. For the right type of person becoming a data scientist is a fantastic way to pull yourself out of poverty and debt. Remember a mixture of non-intrinsic and intrinsic motives is normal. Once you have your reasons, stick the post-it notes somewhere you’ll see them everyday. Maybe on your wardrobe or bathroom mirror. That way you’ll remind yourself each day why you want to become a data scientist. 


Are You Ready For a Career In Data Science?

I have great memories of being young and obsessed with building model cars. These miniature models of classic cars were built from plastic kits, going from picking the plastic components out individually, then following the visual instructions one by one until the car was fully assembled with glue. I would then paint some flames along the sides, soon after running to my family to show off what I created. Later, moving on to building with Meccano, assembling all manner of contraptions. I attempted to build a marble gun with Meccano pieces and a hand held fan. Didn’t turn out to be as powerful as I hoped. 

When building these childhood relics, I remember time melting away as if it didn’t exist, I was fully immersed, hunched over my desk under a lamp, it felt good. I guess you could call it flow. A few years after I realised I could reach this state with problem solving, puzzles, games and math – if there was a solution to be discovered, I wanted to prove to myself that I could figure it out, and when I did it felt good. Little did I know that it would be the inherent motivation behind my day job today.

For those of you who are like me, who find peace or pleasure in problem solving and creating- then this blog may be for you. If you hope to find a path in life that enables you to problem solve and create each day – then this blog is definitely for you.

This blog is my personal collection of nuggets of wisdom for becoming a data scientist. Almost all of these practical tips helped me become a data scientist. For the tips I did not follow, I retrospectively wish I had. It’s my belief that if you follow them, your chances of becoming a data scientist can increase by an order of magnitude. That doesn’t not mean to say it’s a complete checklist, nor will every tip be relevant for you. But it’s a collection substantial enough, that if followed, will send you on your way. I have purposely attempted to avoid vagueness and ambiguity where possible, the best advice is that of which can be easily interpreted and implemented.