Stann-Omar Jones

Let’s be real—data visualization can be frustrating, even for power analysts. I often get so buried in the numbers that stepping back to cOmMuNiCaTe insights can feel like a chore. It’s like making a delicious meal but skipping the plating; no matter how good it tastes, it won’t impress if it looks like mush. But visuals matter. A lot. The good news? You don’t have to sacrifice hours to create dashboards that are sharp, clear, and beautiful. Here are five go-to tips to level up your visualizations—without burning out. The tips are tailored to Tableau users but can be repurposed for your preferred data visualization tool. ...

I took Andy Kriebel’s Makeover Monday challenge a step further by creating a quick tutorial on how to present a dashboard. Surprisingly, I couldn’t find a comprehensive resource on this topic online, so I’m hoping other netizens will find this helpful. Presenting a dashboard, especially when it’s compact like a one-page overview, requires a structured approach to convey insights effectively and keep the audience engaged. A typical presentation time for this kind of dashboard is around 15-20 minutes, as this provides enough time to cover the dashboard’s key elements without overwhelming your audience. ...

Why Learn OOP? Lately, I’ve been revisiting some fundamentals to reinforce Python’s role as a powerful tool for object-oriented programming (OOP). My mentor encouraged me to use Python like a developer—not just as an analyst—and I completely agree. Understanding data structures is essential, but incorporating OOP principles makes you a much stronger problem solver. Surprisingly, many analysts either never learn OOP theory or only encounter it after they’ve already become proficient in Python. ...

I’ll be skipping past the standard SQL techniques, assuming we’re all familiar with the basics because in today’s data-driven world, mastering advanced SQL isn’t just useful for day-to-day analysis, it’s crucial for standing out in live-coding interviews, where you’re expected to navigate and manipulate complex datasets quickly and confidently. 1️⃣ GROUP BY vs. DISTINCT vs. Window Functions Feature GROUP BY DISTINCT Window Functions Use Case Aggregates Removes duplicates Ranking, cumulative sums Performance Medium Fast Can be slow with large data Example: Count unique customers per region. ...

As part of an analytics project for a client, I used R and various libraries to import, clean, and analyze datasets related to labor market statistics in British Columbia. This project involves reading data from CSV files, performing data cleansing operations, merging datasets, and generating visualizations such as box plots and a bar chart showing the monthly change in employment across Canadian provinces. The analysis focuses on specific sectors, including accommodation and food services, wholesale and retail trade, and other services, providing valuable insights into the labor market dynamics. In this post, we’ll walk through the steps to analyze employment data across various sectors and provinces in Canada using R. ...

In this project, I generated insights specifically tailored for a web team within a prominent political advocacy organization. The queries cover various aspects such as user engagement, revenue analysis, and page views over time. Additionally, I designed visualizations in Tableau and provided recommendations based on my findings. Exploratory Data Analysis (EDA) Summary This section outlines the steps taken to explore and analyze web analytics data using SQL queries. The analysis provides insights into user engagement, popular pages, device segmentation, and referral sources. ...

Context From 2020 to 2021, I volunteered at the COVID Tracking Project, where our efforts significantly enhanced the accuracy of data collection in the USA. By auditing, compiling, and analyzing sub-national, daily data sources, we delivered accurate, real-time insights to government officials. Our team built over 50 collaborative datasets and corrected inconsistencies in more than 40,000 data points, informing life-saving national testing and immunization strategies to combat COVID-19. Dashboard This dashboard, inspired by Alex the Analyst, exemplifies the impact of our work in providing reliable data that informed public health decisions worldwide, playing a crucial role in creating transparent and globally accessible COVID-19 statistics. ...

Introduction As a Python developer, managing dependencies and ensuring a clean development environment has always been a top priority for me. Virtual environments have become my go-to tool for handling project-specific dependencies, avoiding conflicts, and maintaining a tidy global Python environment. In this blog post, I’ll share the benefits I’ve experienced using virtual environments and walk you through a quick tutorial on using virtualenvwrapper, an extension that has significantly enhanced my workflow. ...

Cohort tables offer valuable insights into customer behavior by grouping them based on shared attributes, in this case, acquisition date or date of first purchase. They are fantastic for identifying whether you have a retention problem or a user acquisition problem and can allow marketers to understand user behavior through experiments. Learning to read these tables can be tricky at first but effectively empowers you to analyze customer engagement, retention, and acquisition trends. ...

King County, WA Housing Insights Line Chart: The line chart displays daily prices from May 1, 2014, to May 1, 2015, illustrating a general upward trend over the year with some fluctuations. Housing prices rise from an average of $0.5M to $1M by November 2014, peaking at $1.3M in May 2015. The zip codes with the highest average price, ranging from $0.9M to $1M, include 98109, 98105, 98004, and 98040. These zip codes, situated close to Lake Union, the only water body within city limits, are still connected to the mainland, possibly making them desirable locations for homes. This observation is supported by the fact that Vashon Island, despite being surrounded by water, is one of the cheapest places to own a home, likely due to its isolation from the mainland and possibly poorer infrastructure. ...