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!
Leverage Google Maps API or Nominatim in Python to return complete address information that you can use for geo charts.
In this post, I’ll show you how to extract every number from a string in Python using regular expressions.
The default print view for a Pandas DataFrame can be limiting for larger datasets and can get in the way of a thorough review of the data.
In this post, I’ll show you how to extract emojis from a string in Python, count the frequency, then plot them using Plotly.
In this post, I’ll show you how to add a timezone to a naive Datetime object.
Python Pandas allows for a lot of flexibility when naming your which can cause trouble if you are trying to import the data into a structured database like SQL or BigQuery. In this post, I’ll show you how to format your Pandas columns to make them compliant with structured databases.
Pandas has a function pd.read_html() to get an HTML table from a website in one line of code.
Google Colab is Google’s version of a Jupyter Notebook and takes advantage of the same features you find in other Google Apps to make Python coding easy.
In this post, I’ll walk you through exporting tickets, users, and organizations from Zendesk using the API and Python for data analysis.