Category: resources

A memory that has always stuck with me during my career-to-date is when my data science hiring manager drew a quadrant on the white board during one of my hiring interviews. The quadrant represents the spectrum skills you need to become a data scientist.  Every data scientist sits somewhere on this landscape of skills. I was asked to draw a dot on where I thought I sat on a spectrum of four primary DS skills. The skills were: Technical/Engineering, Theoretical/Mathematical, Commercial Aptitude and Data Storytelling.

At the time, I feel I greatly overestimated my technical ability – knowing what I know now, I would have rated myself lower on this scale. Now, however, I would say I’d be offset towards technical/engineering. 

I believe this type of honest self analysis is useful to any data scientist at any stage of their career, however even more so at the early stages. This kind of self awareness of your skills set can act as a compass to direct you into what areas you need to improve on and guide towards doing work that you are most likely to succeed at. Have a go at plotting yourself on the quadrant now, and then where you would like to be in, say, 5 years from now.

The truth is employers will most likely want you to sit somewhere across multiple skills groups. What use is complex statistical analysis if you can’t extract and articulate the valuable knowledge to others in the company, be it verbally or visually?

Same goes for brilliant technical data scientists who create amazing pieces of technology that hold no commercial value? Or vice versa, a data scientist has a brilliant grasp on the intricate commercial value of a project, yet does not know where to start on delivering the project. The first years of your career will enable you to feel out what types of tasks you have the greatest aptitude for, however, some of you will already know this. And for the very rare exceptional cases, you have a high aptitude for all 4 scales of the quadrant. 

The DS skills quadrant:

One of the modern dilemmas of aspiring data scientists today is that the job description of a data scientist is ever expanding, the possible skills that could fall under the umbrella of “Data Scientist” is extremely large and growing by the day. Thus, it’s important to rank these skills by some criteria. However you choose to rank these skills, ensure that you don’t become overwhelmed by the endless number of possible tools and skills you think you require. That said there are some basic skills that are fundamental to the job.

Some concepts in data science need to be explained to you in a clear and human way. Aside from having a tutor, mentor or teacher then the next best thing can be to watch a quality video explaining concepts to you. Luckily there are thousands of videos with varying degrees of quality explaining most of the important fundamentals. For example, you could type “How decision trees work” into YouTube, start at the top, if one video doesn’t make sense to you, you can simply try another video until you find someone who explains the concept in a way that you can understand. You get the added benefit of being able to pause a video and look up as you go along. I have found this a highly effective way to learn. Furthermore you can rewind sections of the video over and over again, going over anything that you may have missed. 

When I was learning, I created a table with important fundamental concepts and links to videos I found helpful explaining them.

Many paid online course platforms such as Udemy and Coursera, deliver lessons through video however much of the content is already available for free through YouTube and other platforms. Do some research and find a channel and platform that works for you.

As with any technical industry buzzwords, abbreviations and obscure words synonymous with less obscure words are common. They are just part of the game and learning what all these words mean can seem daunting, in a way they act as a barrier to entry for people when they really shouldn’t be. 

Rather than write an exhaustive list which would take far too long, here are a few important examples and their definitions:

• ETL – Extract Transform Load
• Heuristic(s) – Finding an approximate solution when classical methods don’t work. 
• Structured / Unstructured Data – Data that can easily be put into a table or database vs other data such as PDFs or MP3 files.
• Supervised / Unsupervised Algorithms – Machine learning algorithms that learn with labelled training data vs those without 
• Greedy Algorithms – An algorithm that iteratively finds the localised best option in order to get to the global optimal option.
• Normalization –  Adjusting values measured on different scales to a common scale.
• Residual (Error) – Deviation of the observed value from some derived value.
• Feature engineering –  Process of transforming and creating training data for a machine learning algorithm.
• Application containerization – The process of creating standard unit of software that packages up code and dependencies so the application runs between computing environments.

There are many more of course and terminology is ever changing. 

Just don’t take how many words you know the meaning of as an approximation for your capabilities as a data scientist. You’ll learn what they mean in time.