Author: karen

https://www.caschooldashboard.org/#/Details/43696664337200/1/EquityReport

The California Department of Education publishes performance numbers on schools statewide including suspensions, test scores, and graduation rate. This data is publicly available at this link: https://www.caschooldashboard.org/#/Home . According to the small print on the main page the data is maintained every semester, however making the visualizations and cadence of data more of a scorecard instead of a dashboard.

Some of the topics do appear to require some knowledge of the education system or at least prior knowledge or research. There do seem to be some assumptions that the audience is familiar with the education system either as an educator, someone working in the school system, or a parent.

What I liked

There wasn’t a whole lot to like here from a visual and usability stand point, however it did help to have words – “low”, “very low”, etc to indicate how something ranked relative to what the expectation was (especially when an average user might not be able to recognize the performance level if they are just looking at a number in a chart). There are also multiple tabs to separate metric and score card groups so they are not all grouped together as different users may be interested in different sets of metrics at different times.

What I didn’t like

This does serve the function of a true score card. It shows performance at individual schools for a given time period. It even will say what performance is like for a certain attribute, measuring it against some sort of benchmark (although what that value is or why it was chosen is not [[disclosed—it may be in small print that is hard to find).

Several metrics are displayed as icons which represent a pre defined scale (small pie chart icons). This seems misleading because it is hard to tell if you are supposed to try to read the icon for a value or if they are just supposed to be an indicator icon.

As I experimented more and more with the tabs I realized that there was an extra layer of options in the detailed reports that was hard to see until further investigated.

How I would improve it

If I were to change this I would do the following:

 

• Highlight what the high level goal is of the school should be, what they should want to achieve for each tab so the user has a starting point for viewing the data. There is the assumption that the user knows something about the world that the data is from, but the author can frame where the data is coming from to help put users from various ranges of knowledge on the same page before jumping into analysis.
• Call out and communicate to the user what the benchmarks are and how they were calculated (can be in a separate chart)
• Simplify the icons used as indicators – the current use of color is good, but the icons cannot themselves be a chart type unless they are meant to be a chart, it can cause confusion. Use an icon type that is solely an icon.

The dashboard is in link form: https://www.caschooldashboard.org/#/Details/43696664337200/1/EquityReport

As images were unable to be posted in this bloghttps://www.caschooldashboard.org/#/Details/43696664337200/1/EquityReport

A Special Focus on Science, Technology, Engineering and Math

https://www.census.gov/dataviz/visualizations/stem/stem-html/

The average college degree takes four years to complete. By that time the job market or the student’s career choice could have changed providing a mismatch between the intended field of study and jobs available or targeted job.

Goal/Purpose/Question

The question that the visualization is asking that after a college student graduates, what field do they work in?

How far away/related is it from the field they studied? What is the volume of students that followed those paths?

This data is for 2014, but it can be implied that it also serves the purpose of displaying the shift in market labor demand from when the student entered school to when they entered the job market.

One could also potentially assume that it shows the state of where the job market is going– (if you make the assumption that those who selected majors going into college picked the correct majors at the time). However more investigation would be required to understand exactly why this would be the case.

What I liked about it

• Graphic is clean and simple. User can clearly tell quickly where the disconnects are either starting from occupations or from topics of study
• The data is cut in many different ways, making it easy to see how different types of groups differ
• The type of visualization used is good to show path, differentiation, and flow while also isolating STEM careers

What I didn’t like about it

• While this visualization choice is good for overall flow, it is very challenging to get an idea for actual percentages.
• Filters send you to additional pages instead of filtering current view
• It doesn’t allow you to filter on values other than STEM (although included in the viz). This makes it hard to read the overall view
• A bar char or additional colors may be more appropriate to distinguish unused space from actual values here
• I would also have the question of if the creator of the viz accounted for any of the college grads that either went back into school
• The visualization is not time relative to individual situation – IE to dig further into why there could be any potential gaps between major topic and ending career.

Conclusion

There are no specific goals of this viz aside from exploration at where STEM students end up in their careers however there are some ways that it could be cleaned up to better define and make it customized for the user and not break up the user experience.

Redesign/ additions

To redesign this I would use the additional tabs or page breaks for any alternate views IE if there were alternate visualizations however for filters only I would keep those in the same page to keep the user from having to switch pages.

I would also add another color to differentiate non stem majors/workers flow a bit more. They have been included, so making them easier for the user to distinguish and understand is important.

I would add a view as a bar chart or table with actual numbers and percentages so the user could understand the actual numbers that are associated with these transitions. It’s hard exactly to grasp if one wished to go deeper just looking at the flow.

References

https://www.census.gov/dataviz/visualizations/stem/stem-html/

 

In sports a team’s goal is to be successful. As with many sports in soccer success is winning as many games as possible to make it to post season and eventually win the championship.

How does an organization build a championship winning team? There are a lot of factors that can make an impact and data can help influence recruiting decisions.

Putting ourselves in the shoes of a recruiter, we’re looking to put together a new star team in the EPL(English Premier League). One of the best ways to learn is from looking at history. We are lucky enough to have data on the players currently in the league.

The objective is to find what would make an ideal recruit for our team (data only).  We want to find the optimal player profile that will help us have a successful season.

Location is important and this dashboard can tell us a lot about where to look for players:

https://www.tableau.com/solutions/workbook/create-optimal-game-strategies-based-past-results

according to the dashboard:

• Most players have spent time in other countries, but most have spent >50k days playing in Europe, conclusion is that there is experience in European leagues or the EPL
• EPL recruitment is concentrated in EU, but also pulls from other countries WW
• When looking at club breakdown, most patterns look similar with a cluster in EU and variance in the outliers (players from the US, Spain, South America). It is hard to correlate to current standings.
• Birth country shows that the EU isn’t the only location dominating the top – players are born in Senegal, Brazil, Argentina, and Nigeria (even though no players were directly recruited from Africa)
• The author digs further into Africa, revealing that a significant number of players born in Africa play in the EPL, regardless of their recruiting or development country, highlighting that people from Africa often come to Europe from France are developed and are recruited from Europe

Based on common trends it is pretty conclusive that focus would be on recruiting players that have done development work EU, but with South American, African, or European backgrounds.

This dashboard covers location, but it is not enough to tell us what makes the perfect player.

It does not include individual player performance or what combination of these two builds success (if someone figured that out, recruiters wouldn’t be needed).  The NCAA makes some recommendations on what body fat and other characteristics a higher level soccer player should have (among other sports):

http://www.ncaa.org/health-and-safety/sport-science-institute/body-composition-what-are-athletes-made

Pros

• Many different types of visuals are used
• The “story” aspect of Tableau is used to direct attention to the point the author is trying to make
• A lot of good data on what teams are already doing
• Compares different recruiting patterns, but doesn’t show how that impacts
• The visuals are all straightforward. It is not confusing to understand any page of the dashboard.

Cons

• Doesn’t provide the ability to necessarily explore options outside of what people are already doing, an observation of what current teams are already doing (IE what happens if I expand to recruiting in Antarctica)
• Can’t focus in on a specific team, don’t have enough info to use the data to see how expanding to new markets for recruiting has impacted performance (IE Everton recruiting in South America vs AFC Bournemouth staying closer to EU)
• Don’t put rankings, team performance

How would I change it?

• Presuming the goal is to provide insight that one can take action on, more data points need to be added in addition to location. Location is only an observation, adding player characteristics, team characteristics (resources), and performance can add more background and context to explain patterns and correlation about the players/teams and performance over time that could be made into recommendations.

How else could I use this data?

• If I am looking to someday become a player in the EPL, based on geography I could make strategic decisions on where to play (or to do development) in order to increase my chances on getting into the league (purely based on geographic indicators from this data)
• If significant correlations are seen between players from (born) in certain countries and performance in the EPL, development investment could be made in those countries (IE if players born in Africa are high performing, how do we optimize)

 

https://salankia.shinyapps.io/satRday2016DataViz/

This Dashboard was a part of a visualization challenge called SatRdays, a part of a regional Europoean Conference (http://satrdays.org/).

As an introvert, there may be several factors that influence decisions you make when you travel.

This dashboard has a couple of different visualizations to help the traveling introvert guarantee that he or she will have the least amount of crowd interaction. The designer has highlighted 3 main areas where one may encounter crowds when traveling through airports (direct flights from Belgium):

• traffic to connecting airports
• utilization of planes
• seasonal impact to travel

This data could also could be useful to anyone interested in optimizing travel time.

———————–

(entire viz cannot be seen in screenshot, visit: https://salankia.shinyapps.io/satRday2016DataViz/ for the complete view)

Images didn’t insert into the blog, use link below to view:

https://salankia.shinyapps.io/satRday2016DataViz/

———————–

This dashboard presents a lot of data. As a user, I can pick an airport that is less crowded, cross check my selection with plane utilization, and then see how  seasonality would further impact my trip. The data is separated on different tabs and filters can be made independently which allows for independent analysis. The user is presented a lot of good information that could help influence decisions, such as average number of people on planes leaving Belgium as well as trends to destination cities from Belgium.

——————————

It is up to the user to keep track of all selections. A way to improve this would be if filter values (or user choices) were global—if one selection is made, it changes everywhere.

For example if I made a selection on the connecting airport tab then it should show me corresponding utilization.

The data could be shown all on one tab, displaying all data at once providing the user with all the information at once in one place. The user wouldn’t have to keep going between tabs.

The designer also could also be more consistent with color, it is hard to tell just by looking – it looks like it is based on value, but assigning color to specific countries might make values easier to spot even if the layout of the data is different.

—————

Taking this a step further looking at traffic at origin airport (Belgium) , terminals, airports and additional factors could be examined for crowd factors.