Data Analysis

First things first, 10 points to anyone who understands what the title is referring to (that's either Charles Atlas' workout philosophy, or one of core tenants of Bokononism in Kurt Vonnegut's standout Cat's Cradle).

Now, with my obscure reference quota filled for the day, let's assume that you've been working as an analyst for some time now. As stated in a previous post, generally you're likely to get promoted into a management position, where you no longer need your formidable skills of analysis, and they too, like all good things, will atrophy to nothingness. However, for those of you who

If you've been following along, we discussed in the last several posts of this series how, if you're not working in a very "tech forward" organization (like my two compatriots on his site), but you have the same title, you're probably obtaining your data from another department (or it might be a sentient sponge, or a gang of squirrels with dreams of world domination, you'll actually have no idea) who you will have no contact with. As a side effect of this...rather strange situation (ya know, like being a child and being abandoned briefly by your mother in a

In my last post, we explored the organizational structure of many large companies and how this pertains to one's duties as a fledgling data analyst. I highly recommend you go back and read the first post on "dirty" data, but just in case you're one of those rebels who thinks that they're too cool to read part 1, here's a quick refresher to put you back in the analytical mindset (which is the perfect combination tactical laziness, ADHD, and a complete disregard for downside risk).

Essentially, the reason why most data analysts exist within a company (remember, NOT a tech

In my last post, we discussed what separates a true analyst (read: technical) from a project manager wearing the mask of an analyst like some Scott Snyder era Joker (I figure that there's a solid overlap between fans of comic books and fans of the real world application of data. Note that this is a study with an N = 1 so it bares no statistical significance, but I have a funny feeling...call it spidey sense).

Full disclosure, this post comes mostly out of my inability to sleep in my hotel room in Chicago following a grueling day of doing

If you've been reading along, over the last several posts you've learned the two major skills that any self-respecting Excel jockey counts as their go-tos: the ability to lookup (remember, I'm partial to index-match, but if you learned VH lookup, ride that until you crash your system) and the ability to pivot.

Now here's something really interesting: until we pierce the veil on doing some VB macros, by truly mastering just those two skills, you can handle basically any job that Excel can actually handle, anything more advanced is just a matter of doing it faster (and faster, and faster)

During the last discussion, you've (hopefully) learned how to generate a pivot table, and learned about the four "buckets" that can house your columns:

• Filter
• Row
• Column
• Value

I'm also going to make the wild assumption that you've played around with your newly birthed pivot table, taking your column headings from your "raw" data (in the lingua franca of the numerically inclined, this means the data just as you've received it, before you add formulae or engage in any other manipulation.) Hopefully this gave you a feel of how a pivot table works, and if your synapses are wired in

In my last entry, we discussed how to write a formula, and you've been armed with what each piece of the formula represents (the command, the variables, and the definition of an array). With this knowledge, you've actually been armed with the keys to the kingdom, and you're finally going to learn how to do something fancy.

There comes a time in every person's life where just by learning one piece of information, suddenly everything else you've learned clicks into place. For those who utilize Excel, learning this formula WILL BE THAT FOR YOU. In our last entry, I stated