Data Analytics Simplified
Imagine that you are the host of “Help! I Wrecked My House!” But instead of navigating through the debris of a DIY home renovation gone awry, you’re diving headfirst into a chaotic world of spreadsheets, rogue data streams, and a jumble of mismatched tools. The mission? To declutter, organize, and automate workflows, laying down the solid groundwork necessary for efficient reporting and data science. This is the life of a data analytics engineer.
Here on this blog, I’ll share insights, tips, tricks, and a robust framework designed to tackle the ever-evolving challenges of data engineering and analytics. Given that every company and initiative comes with its unique set of requirements, and considering the dynamic nature of data, you won’t find any one-size-fits-all guides here. Instead, I aim to share my thought process and problem-solving strategies to help you identify the most effective processes and tools for your projects.
You might find yourself here because you:
No matter your situation, I’m here to equip you with the essential tools for your data analtyics toolkit, tailored specifically for the lean tech startup environment. Welcome!
Here’s the query you can run in SQLite to return all the columns from a specified table in SQLite.
Plotting data that is organized into pivot table has a slightly different syntax than plotting a columns in a dataframe.
The Federal Bank of St. Louis (FRED) has one of the largest free databases of economic data. It’s an excellent and trusted data source to use for financial analysis. The Pandas Datareader package makes it so easy to start analyzing the data with a few lines of code in Python.
A weighted average calculation takes into account some numbers should influence the average more than others. I typically find myself needing this calculation when working with data that is already summarized in a table.
Save yourself time by using this script to automatically pull data from a CSV file you receive as a Gmail attachment into a Google Sheet. I find this script especially useful when working with CSV files I receive on a recurring basis.
Easily combine multiple CSV files with the Terminal on Mac.
Make the Google Sheets QUERY function even more flexible when you add variable parameters to your query.
By default, the FILTER function in Google Sheets is set up to take multiple criteria using AND logic. With a slight modification, you can make it use OR logic.
Let’s say you have a column with someone’s full name stored in the format of “LastName, FirstName” and you would rather have two columns – one with the first name and one with the last name. The below code to split out the names in your results window.