How can you compare and contrast data sets using data visualization?

The purpose of Data Visualization is to have a holistic and comprehensible view of the data. Hence whenever there is a need to compare data sets, visualizations are the perfect denomination. Apart from the Chart type there are many other factors as well which should be considered: 1. Metadata Type 2. Data Range 3. Data Fields Type 4. Data Patterns 5. Data Modal 6. Data Group Finally, its utmost important to understand the type of data being considered and hence charting accordingly.

Effective data visualization in science requires strategic thinking. Choose suitable chart types (bar for numbers, pie for shares, scatter for relationships), but understanding the data and crafting a clear narrative is key. Knowing what to include and exclude is crucial for a streamlined visual that highlights pertinent data. The goal is precision and impact, resonating with the audience. By combining data insight, narrative clarity, and careful curation, selecting the right visualization becomes an intuitive decision for the savvy data scientist.

One approach I find helpful in comparing and contrasting data sets through data visualization is highlighting patterns and outliers. Disagreeing with traditional methods, visual representations offer a more intuitive understanding. For instance, utilizing scatter plots to showcase relationships can reveal insights that may be overlooked in raw data. An example I've seen is visualizing sales data through line graphs, effectively emphasizing trends and aiding in decision-making processes.

*First identify the type of data you are dealing with - such as whether it is Categorical data, Discrete data or Continuous data * Then figure out how many data sets are being considered for comparison. * Also identify if any negative values need to be represented. Once you have considered the above, pick the chart type for comparison. For instance, for both categorical and discrete data, you could use Bar charts or Column charts or Pie charts. But for choosing bar chart or column chart, there are a few considerations. * If you have less than 10-12 categories for comparison use Column chart; else use Bar chart. * If you have both +ve and -ve values to deal with, use Column Chart. For continuous data, use line graphs to observe trends.

Bar/Column Charts: Compare values across categories (e.g., sales by product categories). Line Charts: Illustrate trends over time or continuous variables (ex. stock prices). Scatter Plots: Examine relationships between two variables (ex. advertising spend vs. sales). Pie Charts: Display proportions within a whole (ex., market share distribution). Box Plots: Compare distribution characteristics (ex., exam scores across subjects). Histograms: Depict the distribution of a single variable (age distribution in a population). Area Charts: Show cumulative contribution over time (revenue growth over months) Bubble Charts: Visualize three dimensions (population density and GDP). Heatmaps: Represent relationships in a matrix (correlation matrices).

In my experience, we should consider 3-point with Scales and colors. 1.) Same Size Rules: - Use the same rulers for similar things. Imagine if one chart says '10 dollars' means this tall, and another chart says the same '10 dollars' means much taller. That's confusing! 2.) Colors Stick Together: - Imagine your favorite color is blue. If, in one chart, blue means happy, but in another chart, it means sad, that's not good. Keep colors loyal to their meanings. 3.) No Confusing Impressions: - You don't want people to think things are similar when they're not, right? Using different rulers or colors can trick them. Be fair and use the same rules everywhere.

This is one of my pet peeves. I wish I had a dollar for every bar chart I've seen that used five different colors just because they look nice! Colors should be used for a specific purpose. The bars in a chart should be the same color. Whatever color you choose should be consistent throughout a presentation. The same goes for scales of bar or line charts. I've seen presentations with dramatic shifts in line charts that in actuality show a very small change. The drama comes from using a very small range in the scale that exaggerates the shift. The other mistake I've seen is inconsistent labels. Don't combine millions and thousands. For example, if sales of "Product A" are $2.5M, don't show "Product B" sales as $500K. It should be $0.5M.

A consistent scale and colour allows each stakeholder to make accurate comparisons, and understand trends if present. This ensures that stakeholders have a firm understanding of the insights presented to them.

One thing I've found helpful is to use labels to shout out the important stuff, guide people through your charts, keep it short and sweet, and don't overload it with words. Tell your story clearly! 1.) Point Out the Important Stuff: - Show what's important! Use labels or notes to say, 'Hey, this thing here is a big deal!' 2.) Guide Your Audience: - People might not get your charts on their own. Help them! Say, 'Look here, this line going up means good news!' 3.) Use Short and Sweet Labels: - Labels are like tiny stories. Keep them short and sweet. 'High sales here' is better than a long tale. 4.) Don't Overload with Words: Too many words can make things messy. Keep it simple. Just enough words to tell the story. Less is more!

It is important to highlight insights in your presentation or dashboard over all forms of data. This ensures that the stakeholders can focus on the relevant portion of your dashboard or chart. Which eliminates data noise and equips them to make decisions in a timely manner.

I recently worked on a project and I wanted to validate the data before I went way too far with the project. I validated my data from Looker to GCP data, only to find few discrepancies. Finally, after drilling down into the data I found that the definition used in one area of business was different from the next and therefore it would exclude few values from the table. Though, I could go all out and build the project quickly, I spent a lot of time looking into the data and validating whether the data is matching or not. Thus saving the company getting wrong data and unnecessary costs in querying large queries. It is also a good practice to follow some data rules so that the results are biased. eg. comparing roundness of watermelons to apple

Rigorous validation is a religious ceremony when analyzing data in my opinion. Unseen tides can take you by surprise if oversight occurs in cleaning and validation. Before unleashing your visualizations into the world, ensure your data is the superhero, not the villain – clean, consistent, and holding ethical integrity. Think of it as a net practice routine. Validate your data's prowess – is it robust, reliable, and ready for the visualization test? Test your visualizations' resilience with scenarios and diverse audiences to uncover hidden nuances. Ethical principles aren't just guidelines; they ensure your visualizations fly high without distortion. #DataVisualization

Collaboration is key. I can honestly say that presentations can always be improved. If you have the time, It's helpful to have a couple of colleagues review a deck to look for typos, suggest imrovements, or just take a step back and see if it has a logical flow. Something that seems clear to you might be confusing to somebody who looks at the presentation with a fresh perspective.

As you collaborate with diverse stakeholders and senior managers, allow room for feedback on your dashboard or chart. A beautiful dashboard is not more important than a fictional dashboard. Asking stakeholders for their preferences and implementing them will help you, stakeholders and the organisation at large grow. This is because by utilising the stakeholders colour, scale and chart preferences in your dashboards, you equip them to make informed adjustments to the business, which allows the business to manoeuvre the complex economic landscape.

I often use visual tools like bar charts, line graphs, or scatter plots for data display. These help me quickly spot patterns, trends, and outliers. For example, when comparing annual sales data of two products, a line graph clearly illustrates which product performed better each month. This method provides a fast, intuitive way to analyze differences and similarities between datasets.

1.) Know Your Crowd: - Think about who's looking at this. What will make sense to them? 2.) Keep It Clear and Clean: - No mess! Use white space; keep it simple. Less is more. 3.) Tell a Story: - Imagine you're telling a friend about your data. Make it a story, not just numbers. 4.) Check Your Tech: - Make sure it works on phones and computers. Everyone should get it, no matter what they're using. 5.) Share and Learn: - Show it to others. Listen to what they say. Make it better next time. 6.) See What Others Do: - Check out cool examples. Learn from them. There's always something new to pick up.

There’s a lot of great quality, data visualization and the list keeps getting longer and more impressive. The data analyst must resist the urge to display the extent of his or her knowledge of all that is available. Sometimes less is more and focusing on the audience and the purpose of the engagement, you are more likely to deliver relevant and useful results