Today I had a catch up with some friends to celebrate our 20-year high school reunion As an extension of this celebration and to lift peoples spirits in a somewhat gloomy period, I wanted to share one of my favourite stories to tell, which doubles as one of my most embarrassing / funniest High school moments.

Anyway, it happened in English, in around year 10 or 11. We were reading a play and we would take turns alternating the roles. Now I want to emphasise that we were reading, and not acting the parts. We would literally sit in our…

Recently I spent some of my spare time developing a Covid-19 dashboard for my home state, NSW, in Australia. The primary motivation for doing so was because certain data that I was interested in was either not available, not available at daily granularity, or was otherwise not being presented in a way that I found informative. The dashboard can be found here:

Designing a dashboard is as much of a design decision as a data project. Decisions that need to be made include: which metrics are required, how will end-users be using the tool, how to add context appropriate to…

Black boxes aren’t the only reason that data science can be perceived as magic by people from outside the field. A lack of understanding of the data science process and what constitutes data science can add to the mystique (or mirage) from an outsiders perspective. As much as retaining the allure of data science can be ego-enhancing, it should be something that you ultimately want to address and de-mystify. Here are 5 important things that you should educate your non-data science peers on to make your life easier.

Data science is iterative

In data science, you plan to follow a process, but you outsource…

Photo by Zichuan Han from Pexels

There is breaking into data science, and then there is securing a dream data science role. At some point, everyone should look at pursuing what they are passionate about & gain experience in that field. Roles that tackle big problems, or give you experience that you would otherwise not be able to get, or are in a field that you are particularly interested in. The more popular a field, the more competitive it will be. So how do you stand out in a competitive field?

In general, you will need to exhibit these traits:

  • Passion (you need to love what…

You may have heard, data has become a thing. The space has quickly grown, and its popularity, and interest has never been bigger.

However as in demand as data may be, you may be surprised how much excitement you will need to build for your Data and Analytics (DNA) practice, in order to effectively communicate its outcomes. Effectively evangelising your teams mission and outputs should be a key focus area, in order to give your work the exposure it needs to scale. …

How you don’t have to be a data scientist to acquire useful data science skills

Reading many though pieces in the data science community, one would believe that there is a clear delineation of what a data scientist is and what they do. Being a data scientist is regarded as a boolean consideration. You either are a data scientist or you are not.

However, in reality even if you secure the title ‘data scientist’ you probably will not exclusively be doing data science (if, sometimes at all). …

7 things to consider when looking for your next data science opportunity

There is a great demand for data scientists presenting market dynamics that are favourable for the community. More so than your peers in other professions, you will be able to evaluate a company for what it is able to offer you, rather than solely being the one that is being evaluated. So what should you look for when comparing and evaluating data science roles? Here is a list of some commonly known factors plus some less discussed ones that will help you in your evaluation.

The Data

Makes sense right? This is the thing that you will be working with.

This is…

What are the hard to acquire data science skills and why you should look to acquire them

There is both a shortage of data scientists and a subsequent demand to train a growing number of them. Increasingly this is happening in classrooms, but a high proportion of practicing data scientists are still self taught (more than 1 in 4 in recent Kaggle research). Lifelong learning is also important, and whether you are a formally trained data scientist or self-taught, you will continue learning and will rely on a combination of MOOC’s, textbooks and other self teaching to do so.

So with all this learning happening, for the benefit of both future and present data scientists, what are…

With the rise of data science there has been the rise of data science managers. So what do you need to keep in mind if you wish to join these data translators that are acting as a conduit between the business and technical data teams? Going from a practitioner to a manager — your job now is to make sure that data resources are being used optimally so how do you go about doing this effectively?

Technical skill does not ensure management competency

Get your head around the fact that these are two completely different skill-sets. The skills required to manage a team of people performing a…

What is the first thing that you do when you start a new data science or analytics role? You know, besides installing all the necessary tools and packages that you will need, and getting access to data? If you haven’t already it is wise to do a data questions audit, to identify all the areas in your organisation where data can make a difference.

A data question audit is simply running a series of interviews to surface the questions that exist within your organisation, that can be answered with data. Surfacing all these questions is valuable whether you are an…

Damjan Vlastelica

Data Artist and Influencer. Looking to make the world an incrementally better place through the use of actionable, ethical and insightful data insights

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