Finding an intensified mission as companies try to forecast beyond the global Covid-19 health pandemic. Here are three of the top skills that I believe will be key for data scientists, now and in the near future.
There’s no question that data science skills were an integral part of our world of work pre-pandemic. Big data has been on the rise as advancements in tech continue to disrupt industries and ways of working. But among the myriad changes, the coronavirus pandemic has caused, reliance on data is one of the most significant. The work of data scientists is very much in the spotlight and the value of their work is being felt perhaps more strongly than ever.
Having said this, just like so many other professions, data scientists need to adapt their skillsets to stay relevant in our changing world of work. Here are three of the top skills that I believe will be key for data scientists, now and in the near future.
There is a huge demand currently for data-visualization skills. It won’t come as a surprise that we need people to crunch the numbers — that will continue to be crucial. But what we’re seeing more of is the need to present this data in a way that people understand, which is a different skill set entirely.
Having dealt with the immediate impacts of the pandemic, lots of organizations are looking ahead and asking: what is our future business strategy going to be? And how do we use data to inform this strategy? This is particularly acute in organizations with a customer-facing function, whether that’s towards businesses or individuals. For a lot of them, their audiences have changed overnight now that much of the world is working remotely, so they now need to pivot by making quick decisions and re-strategizing.
This is where data visualization comes in. It’s the ability to take the numbers, draw out trends, opportunities, and (most importantly) risks, and then present this in an easily digestible way to decision-makers who don’t have a background in this area or work with data day in, day out.
One of the challenges to note with data visualization is handling multiple data sources. Organizations will sometimes just use the data that they collect themselves from their customers and clients but, increasingly, they buy in additional data. The sticking point is disseminating and drawing meaning from different datasets and different sources — which is an ability a skilled data visualizer will possess.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
I’ll be outlining a full curriculum for learning data science from scratch in the following series of blogs. These are all skills that I have been studying for the past few years on my own personal journey.
Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.
I’m a data science self-learner. I’ve been challenging myself with the data world for a few months now and decided to share how/what I’m doing and a few tips that are helping me in this journey so far.