Our very own Pope (Sam Pope) extols the virtues in developing your knowledge in three key analytical tools.
No matter where you're looking to take your career in analytics, having a firm foundation in the basic software packages for data manipulation will allow you more choice when considering what career options are available to you.
It may sound obvious, but having a basic knowledge in of some of the most common tools on the market will stand you in good stead no matter what path you take. Whether you're just starting out in analytics or have been commercially analysing data, I’ve tried to outline below the three key tools that you can develop in your own time without breaking the bank. There are other tools available that will help you focus in one direction or another, but these three will open the wider range of options to you.
Yes, it’s been around for years, and the number of variants now available equally reflects its longevity along with its ongoing versatility.
Don’t be lulled into thinking that its basic nature isn’t going to benefit you (especially for a graduate) and that being proficient with a higher level statistics package only, will be of more benefit. If you can’t walk you'll find running a damn sight harder. Invest time developing your ability to fully utilise its functionality, and SQL will prove itself as a great career asset.
From extraction and cleansing data sets, right through to using SQL to perform calculations for higher end modeling, its versatility and ability to integrate with other technologies from Oracle to SAS, means being able to fall back on a strong SQL knowledge base is still a sought after skill in today’s marketplace.
Wait a minute, SAS licences are expensive, right?! Yes, but thanks to the increasing number and uptake in open source packages, SAS has responded by launching the 'University Edition'. Free to Download (click on the image below), and as I found out first-hand, with a great support base to draw upon, there's now an option to build your skills and familiarity with one of the most sought commercial skillsets, without the need for prior exposure, commercially or in academia.
As one of the most widely used providers of analytical software from Visual Studio to Advanced Analytics, gaining experience with SAS's programming language 'Base SAS' will make you more commercially attractive, and able to better tailor modeling techniques to client briefs than by using the less adaptable guide versions of the software. Especially if you’re interested in customer behaviour or risk modeling, investing time in learning SAS will more than pay off when searching for that next commercial career opportunity.
No, I'm not encouraging you to take up a pirate persona; although a sense of humour’s always a great way to build rapport, unfortunately poor hygiene and carrying around a cutlass tend to put most employers off.
R is rapidly becoming one of the most popular open source statistical packages being adopted by companies from start up's to multinationals. If you take a quick look on LinkedIn, Reddit or YouTube you'll find an active community dedicated to refining and improving its capabilities as well as offering free tutorials and advice on becoming unstuck when you’re using it to tackle complex projects. Whereas with SAS, its commercial support structure and capabilities are among some of its chief selling points, R which is the next evolution of S+, through its dedicated community is rapidly matching these capabilities and levels of available support materials, all at a fraction of the cost... well technically it’s free to download!
R is rapidly being recognised as one of the top packages for programming stats based models, and even if the company you're looking to join are not using it, having a familiarity with its functionality will hold you in a higher regard with any up to date hiring manager.
So, there it is, a low down of what I'd consider, as an industry insider, the best free languages to have in your toolbox. Whilst I appreciate there are plenty of others options out there, from Python to SPSS depending on your specialism, which others may argue for. But these three from my experience of speaking with analysts, managers, consultants and directors are those that will best enhance you, as an analyst, in this fascinating industry we call analytics.
If you'd like to get in touch, have any questions, or would like to share your opinions, I'd love to hear from you!
You can email me at firstname.lastname@example.org
, tweet me @XcedePope
or leave me a comment below. Also, be sure to check out #XcedeData
where the data team and I are keeping twitter up to date with some of our latest roles and interesting articles.