If you are reading this there is a good chance that you are thinking about moving to analytics. So what do you need to know?  Analytics covers a broad range of activities so you should be a little bit more specific. (It’s a bit like asking about a job in IT. Are you talking about front-end development? Or back-end? Or network administration? Get the drift…?)

Here’s the most used job titles and their meanings to help guide you (and no – it’s not going to be a long list of skills):

• Data Analyst: In this role you’ll need to collect, process, and perform data analysis. What technologies should you know? Well, being familiar with one of the most commonly using database platforms – R, Python or SQL is a plus – NoSQL is the icing on the cake.  Oh, and of course, the good old reliable Excel (yes, spreadsheets are still very much alive and well).

• Data Engineer: You are an all-rounder. You love to develop, test, and maintain data architectures.  Basically you have renamed all your relatives after your skill sets, so now you have your Hadoop wife, your R sister and her husband Python, your grandpa Perl (or his name was C?). Oh, and you love hanging out with your Qlikview & Tableau twins.

• Data Scientist: Yes, this is the job title. Even though recruiters usually refer to it as Unicorn, Rockstar, or Ninja. Getting serious though – this job is the most sought after, because you need to add value to product analytics, data engineering, A/B testing, and data modelling. You also need to be naturally curious and ask yourself questions like “what can I do with all this data” or “what opportunities lie hidden within this data?”

• Analytics Manager: Besides being delighted with your bank statement on pay day, in this role you’ll be required to manage a team of analysts and scientists. Very often this is a hands-on role so you need to master SQL, R, Python, MatLAB, etc. Sounds like a Data Scientist skill set, right? Well, sort of, but on top of that you need to have leadership and project management skills, be a good interpersonal communicator, and be able to build solid relationships with internal (and external) stakeholders.

Now that you have a basic idea about what working in analytics involves, what should you do now? Well, start upskilling. NOW.  Already upskilled? Well done you, give me a call and let’s have a chat or email me.

PS: allow me to add some boring keywords (that by the way are the skills that you should aim to possess) so that this article will sparkle and shine in the web: Apache, Hadoop, Spark, NoSQL, Machine Learning, Data Mining, Statistical and Quantitative Analysis (R, SAS, MatLAB, SPSS, Stata), SQL, Data Visualization (Tableau, Qlikview), Programming Languages (Java, C, Python, Scala), Creativity and Problem Solving, Big Data.

Good luck and I look forward to hearing from you.