Data Analytics Simplified
Welcome to Data Analytics Simplified, a blog dedicated to helping you streamline data workflows, automate processes, and scale your infrastructure—without the headaches. Whether you’re battling messy spreadsheets, inefficient pipelines, or trying to get the most out of your data analytics investments, you’re in the right place.
I’ll share proven strategies, tips, and frameworks from my experience in data engineering and analytics, focusing on:
Data doesn’t have to be overwhelming. With the right approach, you can declutter, optimize, and build a solid foundation for data science and analytics.
Let’s get to work.
In this post, I’ll be looking at a few indicators economic to measure the economic recovery from COVID-19. This was inspired by the Daily Podcast on November 19th, 2020 – The Pandemic Economy in 7 Numbers. I’ll be using data from FRED for this project and analyzing the data in a Jupyter Notebook.
In this post, I’ll analyze my Google Calendar events to see exactly how much time I spend in meetings each week.
I’ll be analyzing COVID-19 data in a Jupyter Notebook for this post.
The Salesforce Campaign object is a great way to bucket contacts for mass outreach. However, there are limited ways to actually getting contacts assigned to a campaign. In this project, I’ll discuss the Salesforce limitations and how I can get around them using the Salesforce API and Python.
Excel has a limit of 1,048,576 rows and 16,384 columns. Here are a few options to explore if your data exceeds those limitations.
This dashboard is using data from the COVID Tracking Project. The dashboard is built using Python and Flask. The visualizations are made using Plotly and the Dash framework. The app is hosted on AWS Beanstalk.
Nan values in a Pandas DataFrame can be tricky to work with. In this post, I’ll show you how to correctly account for them when writing a function with conditionals.
Getting month-end, or any other interval can be cumbersome. In this post, I’ll show you how to generate a list of dates quickly in Python.
Using the built-in Pandas function to export a DataFrame to an HTML table is quick way to export your DataFrame but it does has some limitations. Using a for loop to create your HTML table allows you to add any custom styling or CSS classes for enhanced formatting. In this post, I’ll walk you through…