Data Analysis

Draw meaningful conclusions from data. Includes a broad spectrum from interpretation, dashboard creation, and data manipulation.

Geocoding Raw Datasets for Mapbox

Make sense of unstructured data with enough precision to put it on a map.

This wouldn't be a proper data blog unless we spend a vast majority of our time talking about cleaning data. Chances are if you're pursuing analysis that's groundbreaking (or worthwhile), we're probably starting with some ugly, untapped information. It turns out Mapbox has an API specifically for this purpose: the Mapbox Geocoding API.

Geocoding is a blanket term for turning

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toddJan 15
Dec 18
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Dynamic Tension! Creating and Using Dynamic Named Ranges

Auto refresh data in pivot tables

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

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snacksJan 15
Aug 31
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Getting Iffy With it

Adventures in Excel

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

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snacksJan 15
Jun 10
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Taking Out the Trash

Dealing With Dirty Data Pt 2

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

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snacksJan 15
Jun 05
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Dealing with Dirty Data

Adventures in Excel

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

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snacksJan 15
May 31
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Doing the Data Dance

Adventures in Excel

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

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snacksJan 15
May 29
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Power to the Pivot

Adventures in Excel

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

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snacksJan 15
May 24
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The One Formula to Rule Them All

Adventures in Excel

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

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snacksJan 15
May 18
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Dropping Rows of Data Using Pandas

Square one of cleaning your Pandas Dataframes: dropping empty or problematic data.

You've heard the cliché before: it is often cited that roughly %80~ of a data scientist's role is dedicated to cleaning data sets. I Personally haven't looked in to the papers or clinical trials which prove this number (that was a joke), but the idea holds true: in the data profession, we find ourselves doing away with blatantly corrupt or

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toddJan 15
Apr 18
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Merge Sets of Data in Python Using Pandas

Perform SQL-like merges of data using Python's Pandas

Let's say you have two obscenely large sets of data.

These sets of data contain information on a similar topic, such as customers. Dataset #1 might contain a high-level view of all customers of a business, while Datatset #2 contains a lifetime history of orders for a company. Unsurprisingly, the customers in Dataset #1 appear in

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toddJan 15
Nov 17
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