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!
In this post, I’ll show you how to create an environment variable so that you don’t have to write your passwords directly into your code. This will keep your passwords safe and prevent you from accidentally sharing your credentials with others.
The matplotlib.ticker library provides an easy way to convert the y-axis number formatting in Matplotlib. Here’s how to turn a decimal y-axis and display them as a percentage.
The SQLite VACUUM command is essential to run after modifying your SQLite database, especially after dropping data.
Leveraging the GROUP_CONCAT function in MySQL allows you to concatenate query results into one row which you can then pass through as a user-defined variable in MySQL.
If you have leading 0’s in your cell, Excel and Google Sheets will remove them. However, those may be important. Here is how to keep them.
INDEX and MATCH function are close cousins to the VLOOKUP function and their combination creates a more flexible and better lookup function.
Cells showing up with a bunch of #’s is typically the result of cell content that is too large to display. Let’s check out a few solutions to fix this.
Using the SUBSTRING_INDEX function in MySQL makes it really easy to split your string data based on a specified delimiter. This comes in handy when your string data is not uniform.
Data stored as strings can be problematic if you are exporting raw data from MySQL and using Excel or another application to analyze the data.