You’re a statistician on a budget. How can you create great data visualizations?

Assuming you have access to a computer where you can install open-source software tools such as R, Python, ggplot2, seaborn, etc., your imagination is the only limiting factor in creating great data visualizations.

Being a data whiz on a budget doesn't have to hold you back from creating impactful visualizations! These are some strategies to keep your wallet happy while crafting stellar data stories: Embrace free and open-source tools: Visualization gems: Dive into options like Google Data Studio, Tableau Public, Microsoft Power BI Desktop, and R/Python with libraries like ggplot2, matplotlib, and seaborn. These offer powerful features without hefty price tags.

Visualization is the first step in understanding data so that you can make a choice of the right statistical tests. Among the simple steps you can do in R Software are: 1. Investigate the data types by using str function - this will give you more insights of the variables (the type of data each variable contains). You will have a hint of what the predictors and responses are in the dataset. 2. Normality can be tested using Shapiro Wilk or Anderson tests for the response variables. 3. For variables with numeric data, the plot function gives a scatter diagram; this can be the beginning of understanding the trends for the relationships. 4. The ggplot function can be used to plot more informative graphs (histogram, bar graphs, boxplots, etc).

Create impactful data visualizations on a budget by leveraging free or open source tools. R, a statistical computing language, enables the creation of stunning interactive plots. Python, with libraries like Matplotlib, Seaborn, or Plotly, offers powerful and versatile visualization capabilities. Opt for graphical user interfaces with tools like Tableau Public, Google Data Studio, or Power BI for drag-and-drop dashboard creation and sharing. By embracing these cost-effective options, statisticians can effectively communicate insights without the need for expensive software or subscriptions. Python is my favorite!

There are so many ways to learn from the best without spending a dime. The ones I found more effective in recent years are: Blogs and articles: Follow prominent data visualization personalities like Edward Tufte, Nathan Yau (flowingdata.com), etc. Online communities: data visualization groups on platforms like Reddit. YouTube channels: Many data visualization experts have active YouTube channels where they share free tutorials.

Experimenting and iterating are the steps to getting so good at data visualization. When doing that is also important to consider the following: What message are you trying to convey with your visualization and who is the audience? Don't get stuck in the same charts! Try out various chart types. Use color intentionally to highlight patterns, trends, and relationships. Share your drafts with colleagues, friends, or online communities for constructive criticism. Don't just present data; tell a story with it.

Simplifying and focusing is indeed one of the most crucial aspects of creating effective data visualizations, especially when on a budget: Here is why is so important: Clarity and understanding: Complex visualizations can overwhelm viewers and obscure the key message. Impactful storytelling: Focusing on a single, clear message helps you craft a compelling narrative with your data.

As a statistician within a budget-constrained organization, it's crucial to discuss the benefits of low-cost or no-cost analytics tools with stakeholders. By highlighting the value these tools offer compared to paid options, you can help them make informed decisions about future budget allocations. It's vital to not only fulfill your role as a statistician but also advocate for tools that align with both your and the organization's needs, based on your understanding of their capabilities. This ensures that investments are made wisely and effectively.