In this post I want to talk about all of us finding our place and building our careers in data. And when I say “data”, I mean analytics, data science, business intelligence, and so on. In the previous post, I talked about meeting new people and starting a personal website – go back to that one, if you haven’t read it yet. Today, you are going to hear about a recurring theme, which is: getting started now. This means, getting the skills, joining the community, finding likeminded people… and seeing where it all takes you.
In a while, I’m going to write about how you can showcase your skills and use data to actually make a difference. But before that, we need to talk about sharpening up your skills – and this means starting with whatever you have, and sitting yourself down and thinking about the skills you lack, but could acquire quickly. Those skills don’t have to be all about algorithms and programming languages. They can be anything. Why should you learn new skills? Because in data, things change faster than in many other industries, and you always have to stay on top of things.
Here are some of the core skills to consider:
- Programming language with statistical packages like Python or R
- Database querying (SQL)
- Business statistics
- Machine learning
- Business mathematics
- Data mining
- Business intelligence (data blending and ETL)
- Data visualization and storytelling
- Big data tools (open source and commercial)
If you are a beginner, and think you may lack some core skills, check out this article Data scientist – core skills.
Another important thing that so many data people lack know-how about, is the human side of data science. Communication and presentation skills are so important. Gone are the days when data people were not expected to be able to do presentations to clients, or to get up on stage and present to a large crowd, or to explain what they are doing to a general, non-technical audience. Get tips on how to make engaging technical presentations, and develop effective writing and public speaking skills.
And if you are not very technical, if you don’t know how to code or do any kind of data work, don’t fall into the trap of wanting to go back to school for this, or signing up for expensive training sessions. It’s fun in that “signing up” feels like you are doing something proactive. But you are not. Going back to school to learn analytics doesn’t make you a data scientist. Analyzing data, however, does. Learn by doing, and getting advice from practitioners. And that’s why it’s so important to join the community of other data people – to be able to reach out and have your questions answered.
So, improve your resume by learning new stuff, at no cost, and without attending university. Join the community of other data people. Start small, start now.
