Getting started with a database, and beginning to learn SQL can be difficult. We definitely experienced this challenge in our analytic education journeys, and have witnessed it first-hand teaching analytic classes. So, we pulled together a brief video walking through the context of a database + a tutorial on getting a local database setup. Let us know what you think!
We built four (4) different datasets to encompass a motivating hypothetical that's based of personal experience. This is an abridged version of a practical dataset both Serge and I have had to interact with at work:
You're a Business Analyst for a business-to-consumer (B2C) software company that launched at the beginning of 2018. The software is available via the public web, and allows anyone to sign up for a Free account and/or pay for a Premium account. The software is currently available in the United States and Canada. Examples of a business like this: Spotify, Wix, Amazon Prime, Dropbox, Evernote, etc.
Here's a three (3) step formula for getting the most out of presenting a spreadsheet to others:
1. Describe each field, and walk through a single row of data
All too often, presenters of a spreadsheet jump-in to "the weeds" – details that are important and make sense to them, but typically not important and confusing to others. Take the time to explain what each field/column represents:
Imagine you have $1,000,000 in an investment account (one day, right). Over the course of two years you generate the following returns:
Start of Year 1 ➡︎ End of Year 1: +100%
Start of Year 2 ➡︎ End of Year 2: -50%
What is your average annual return?
There’s a lot of ways to articulate the amount of change between two or more numbers. Very often an explanation can be interpreted in more than one way, and/or hide important elements of truly how much something has changed. Unfortunately there is no one right answer, but we do think there is an answer - and it’s to use all metrics of change, all of the time. In doubt, better to give too much information than less.
We put our first product in motion today by launching on Udemy, and we couldn't be more excited! The course is titled: A Beginner's Guide to Data, and it's the analytics context we wish we had, when we first started. Use the link provided to visit the official Udemy page and receive a 50% discount (automatically applied).
Here at The San Francisco Data School we find the term Big Data to be ambiguous, and evolutionary. Both adjectives are being used optimistically. As in, we think there should be more than one interpretation (ambiguous), and that those interpretations should be refined over time (evolutionary).