Digitally Immerse Yourself 

As part of taking your career in data science seriously, you should try and surround yourself with as many different information and learning inputs. Try and build this into your lifestyle as best you can. This will most certainly involve spending your free time ingesting data science related content. As you slowly chip away at content you’ll start making connections between concepts without even realising, it may even happen on a subconscious level. 

The longer you immerse yourself, the more you’ll learn and the more likely you’ll stick at it for even longer… constantly being surrounded by data science related content will also act as daily reminders to keep learning and help prevent you from getting distracted and moving on to some other shiny project. 

A useful tactic here is to leverage the existing habits your brain has already set with social media. For example, the involuntary twitches making you open up Facebook when procrastinating, and replacing the not so useful content you were ingesting with data science related content. Liking, following and engaging DS groups and pages will fill your newsfeed with interesting data science related content.

Here are some ways you can immerse your digital life with data science information online. 

  • Podcasts and ebooks
  • Email newsletters 
  • Subscribe to subreddits, Facebook groups and forums
  • Follow data scientists / data science related accounts on Twitter and blogs
  • Watch films related to AI & data science 
  • Download apps that send you content recommendation notifications
  • Subscribe to YouTube channels

Accept You Won’t Understand Everything

This piece of advice is short but sweet. Don’t expect to understand everything. Don’t beat yourself up for it, it’s normal. Some things are too complex and simply unnecessary to know. For example, machine code – what’s the use of knowing how to write hexadecimal instructions or binary if you don’t need to? Same goes for some mathematical proofs, understanding the idea is usually enough, be pragmatic about what you need to know and avoid following rabbit holes into pools of unnecessary knowledge. This can be difficult to begin with as you’re less experienced at classifying what’s useful and what is not. A general rule of thumb is that if there isn’t a huge amount of information available online, it’s usually not worth knowing to begin with. 

As your data science career develops you will begin to specialise and acquire more of this rare knowledge where necessary. Don’t try and learn it all at the start. Sounds simple, but you might come across people who like to know things others don’t, leading them to almost brag about something niche and impractical for the sake of sounding intelligent. Avoid this trap at all costs, people will listen if you have something that can actually add value to them. 


Don’t Fear The Mathematics

Reading any scientific research paper can often be intimidating when you see large sections of mathematical notation – to the untrained eye at least, it can look like some foreign language that only intelligent people can understand. You’ll realise soon enough that the fundamental elements of these equations, broken down, are much simpler concepts than many of the complex ideas portrayed in the English language. Breaking the equations down and googling each element will let you build a picture of what that equation means. Soon, you’ll begin decoding and understanding what the equations means from understanding what each element means. 

A good data scientist can take an equation, understand the variables involved, understand the operators, such as ‘sum over’ or ‘series product’, then turn that single or set of equations into pseudocode. The pseudocode forms the outline for an algorithmic implementation of the methodology described in the paper. In reality, the majority of the important research that’s implemented regularly, such as many of the machine learning algorithms have already been codified and incorporated into some package for you. However being able to understand the mathematics of these implementations is a crucial part of the job of a data scientist. I would suggest having a mathematical notation ‘cheat sheet’ to hand, which you can either write yourself or find online. Use it like you would use a dictionary, translate the mathematical notation into concepts that you understand in your head.


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.


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.


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.