Month: March 2017

Github has recently release a graph showing the changes in popularity of different programming languages on its platform from 2008-2015. Github is an online repository offering revision control and source code management on the web for its users. Most of the budding developers have been using Github over the years to host their project source codes. Looking at Github’s popularity, various coding boot-camps and academies are now using this graph to decide on their course curriculum.

The graph helps us to understand the various languages which have been on Github since 2008 and their changing trends. We can see that, Ruby and JavaScript have been amongst the top since 2008, with JavaScript still ruling the charts. Also, we can see that the languages like Perl and Objective-C have become extinct on this platform over the last few years, while CSS and C# have gained their place on the chart. Java has also gained a lot of popularity of Github, starting at 7^Th position in 2008 to becoming the second most popular language by 2015.

The graph, in my opinion, is very informative and can be a helpful resource for its audience. It is simple, easy to understand and very clear in its claim. Students who want to learn a programming language with the end goal of securing a good job can use this graph to choose amongst the top few languages.

Reference: https://mybroadband.co.za/news/software/136148-most-popular-programming-languages-on-github-2008-to-2015.html

https://www.fullstackacademy.com/blog/is-the-programming-language-taught-at-a-coding-bootcamp-important

Human life span is considered as an average of 79-80 years, so what is the right age to begin a new venture to achieve success. When I was looking for ages at which entrepreneurs started their companies and made it big I found this very interesting visualization.

From the biographies of top 100 founders on the Forbes List they have found that 35 is the most common age to start one of the top companies in the world. The result is a bell curve, just like in school most people get grades somewhere in the middle, in life most people succeed mid-life, that is about 35, for the current generation.

 

 

The above visualization is interactive and puts together all the right things that we need in an interactive viz. The circles highlighted on click shows us the age, name and the company started by the entrepreneur. The most impressive thing conveyed from the viz is the claim that it poses. It is the right way to target your audience and deliver your message, and it does everything right to the dot.

 

This was just an interesting find that I wanted to share with the class. With the quarter almost coming to an end, we have almost figured out the dos and don’ts for visualizations. This is one such viz that made me think of how far I have got from where we started.

 

http://fundersandfounders.com/too-late-to-start-life-crisis/

As data science students, all of us have heard the term predictive analysis which is basically forecasting or predetermining data for the future based on the trends and patterns of the past few years.As decision makers, stakeholders want to know what next lies in store for them in terms of the company’s future. Data visualizations on company’s performance, market value, stock prices are all indicators of what could happen next.

But there are two other dimensions to the analysis of any data or visualizations. Both are seldom heard and mentioned in market, and yet happen to have an immutable importance in the field of data analytics. These are descriptive and prescriptive analytics.

Descriptive analytics uses data aggregation and data mining to dig into the past data and understand “what exactly happened”. Prescriptive analytics on the other hand, use simulation to find alternatives and possible outcomes and answer “what can we do”.

Most companies conduct descriptive or predictive analytics on their data, mostly because they are trying to figure out what went wrong and what will be the future effects of it. They also hire professional experts to do the job of suggesting different recovery strategies, recommendations, and providing them with the best advice. However, the field of prescriptive analytics is relatively new and slowly getting its long-due attention. We rely on professionals and years of their knowledge and experience to prescribe what’s the best move for our companies. Predictive models, computational modeling and algorithms however are getting their long-due share of recognition as a more reliable and congenial way of approaching a business problem solution. They say to err is human and it has been proven right time to time! With all the amazing progress in data analytics, it is time now to move over human expertise and use prescriptive analysis.

References:https://channels.theinnovationenterprise.com/articles/data-analytics-top-trends-in-2017

This is the visualization based on the survey conducted by the government about American Time Use. It shows how people spend their days means exactly at what time they work.

I found this interesting mainly because of the use of interactivity. Also, the distribution of work schedule is rightly displayed by histogram. Using two filters user can analyze how much is the difference in work schedules for different occupations.It seems overcrowded at first sight but use of highlighters and shading have made it easy to perceive.

We can see most of these occupations fall under conventional work shift 9 a.m.- 5 p.m. But emergency services (police officers, fire fighters) have higher share of work till midnight.

Another interesting thing is we can see who takes lunch break most seriously and who are workaholic. And obviously, this is peak time for chefs and food services.

I think this graph can be made more appealing if it shows comparison between countries as well. That will be interesting to know cultural differences in work time. Another limitation of this data is for white collar work, the line between life and work can be blurred. For example, lunch or dinner with client can be considered as part of work. This throw a wrench into how work hours are measured overall.

Source: http://www.npr.org/sections/money/2014/08/27/343415569/whos-in-the-office-the-american-workday-in-one-graph?/templates/story/story_php=

For today’s blog, I have picked up an Info-graphic by TIME which was published close to Women’s Day in 2015. The graph shows how women are represented in politics after 95 years of getting the right to vote.

To some, this visualization might be very engaging but I see many pitfalls in this graph.
Firstly, I feel is the designer targeting the right audience? Is a reader who reads this article just to enjoy pictures with little concern for content and information the appropriate audience for this visualization?
Secondly, the info-graphic shows eight different measures of women’s participation in government and each of the measure is expressed as percentage of female v/s male participation. If they are all same, I do not understand the need to plot these differently.
Thirdly, in the process of making the chart engaging, the designer has exceeded the boundaries of single screen. Information is more powerful when seen together at the same time; this not only saves viewer’s valuable time but also paints complete picture and important connections that may not be visible otherwise.
Fourthly, there is inappropriate choice of media, just to create a variety designer has added pie charts which are a bad choice as already discussed in class.
Lastly, the color choice is misleading. At first glance it makes you think it has something to do with Democrats versus Republicans, while the graph has nothing related to it.

In the end, I feel a simple bar chart with all eight measures would have been an excellent visualization choice. Also, sorting data in order would actually make visualization more meaningful, as the viewer can then judge areas where women representation is best or worst.

References:
http://time.com/4010645/womens-equality-day/
http://www.datarevelations.com/tag/stephen-few
https://www.perceptualedge.com/articles/Whitepapers/Common_Pitfalls.pdf

Michael Anderson is a web designer and this is his resume. While a resume like this will definitely catch the recruiter’s attention, is it serving its purpose?

Firstly, by looking at the resume, we can see that this guy has definitely used an innovative approach to portray his designing skills. He explains what he did from ’95 to ’98 in terms of employment and academia, his primary skill sets and his behavioral skills using different types of idioms ; but is it helping the audience to derive the required conclusions?

As professor mentioned and as we all can see, the 3-D charts used here are quite confusing. Both the daily intake & output and primary skill sets charts are difficult to understand and analyze.

I do not understand how the different fields in the daily intake and output are related w.r.t. each other. The scale and the values are not clear. Does having less coffee increase his productivity, humor, communication etc. or they are individual graphs mapped across time independently without any relation.

Similarly,  for primary skill sets, the idiom used is a donut and its 3-D – it breaks two main rules of visualization. The comparison that he is trying to show is not clear. It is mapped according to his % personal time invested in these skills. But we do not have a scale or any way to actually get the number of percentage.

For the area chart, I do not understand the usage of color. What does the different shades of a color signify? Do the overlap of area charts mean that he had multiple responsibility during that time?

Also, on the first glance, the usage of color across the resume confused me as well. I was trying to identify if there was any connection between similar colors in different charts.  Apparently, there isn’t.

This resume is definitely interesting and different. I feel its attractive yet meaningless. What are your thoughts?

Michael Anderson’s Website – http://theportfolio.ofmichaelanderson.com/

This article analyzes how data visualization charts are used in United States Congress. It gives good, bad, and ugly examples to give the idea that not all congressmen are good at data visualizations, and sometimes, they fail to convey their ideas to their audiences.

Audience: Congress, CSPAN audience, public who concern about this topic.

Purpose: Using data visualization to show how important their proposals are, and influence them voting for them, not voting against them.

Advantage: A simple and powerful bar chart will represent the data clearly and convey a simple idea to the audience. For example, the first bart chart in “The Good” part created by Senator Dianne Feinstein send a clearly message: “Two-Thirds of Gun Owners want to renew the assault weapons ban.”

Disadvantage: Since the article is written in 2014, probably most of them are lack of data visualization knowledge. I see two bar chart and two hamburgers in this blog post. From this class, there should never be bar charts in the congress. They should consider  using tableau instead of drawing cars like the last visualization. Since the data visualization technology is developing fast nowadays, i believe it is better for congressmen to take one or to data visualization class.

Reference:

http://www.scribblelive.com/blog/2014/05/12/data-visualization-charts-form-the-u-s-congress-floor-the-good-the-bad-and-the-ugly/

Periscopic, a Portland, Oregon-based data visualizing firm designed an eye-catching a dynamic visualization to depict the remaining years for each person might have lived if their lives hadn’t been cut short by a bullet. They used FBI gun murders data in 2010 and 2013 and U.S. mortality data from the World Health Organization for the visualization. Golden or red arcs across a black screen and fades to gray, it showed ages of victims died when the arcs turn to grey and showed the ages of these victims might have lived when the arcs touch the horizontal line. Also, you can see the count of stolen years for these victims at the right corner.

When exploring the source code generating the graph, you will find javascript codes were used to create this amazing visualization. If you are interested in the graph, you can visit  http://guns.periscopic.com/?year=2013

Here’s the link for other amazing work the firm did. http://www.periscopic.com/our-work

Reference: http://www.periscopic.com/our-work/more-than-400000-stolen-years-an-examination-of-u-s-gun-murders-in-2010

 

The metrics chosen for a dashboard are metrics that an influential person thought were interesting. This is how a data puke gets created.

When choosing the core metrics to include on the dashboard, it is important to consider the dashboard’s audience and objective.

Once you have selected the core metrics, you have to create a hierarchy for the information. This can be done with the following practices:

Sizing widgets/sections accordingly – The point of a dashboard is to share complex company information in a way that’s easy to understand. Start by putting the most important information in the largest section and making the other sections smaller accordingly. You should use at most three relative sizes of widgets to make sure the dashboard isn’t overwhelming.

Group data logically – Grouping like data together will allow users to navigate through the information easily, especially when multiple users from different levels or departments are looking at the same dashboard.

Dashboards are great data management tools. However, it takes a more than putting a bunch of data points in one spot. You have to present the data effectively to make a dashboard a useful one.

https://www.betterbuys.com/bi/dashboard-best-practices/

 

What can you do to get more actionable insights from your data?

Analyze and interpret data:  Learn something from the picture you created. You could ask yourself: What can I see in this image? Is it what I expected? Are there any interesting patterns? What does this mean in the context of the data?Sometimes you might end up with visualization that, in spite of its beauty, might seem to tell you nothing of interest about your data. But there is almost always something that you can learn from any visualization.

Document your insight and steps: I really think that the documentation is the most important step of the process; and it is also the one we’re most likely to tend to skip. It’s a good idea to start the documentation by writing down these initial thoughts. This helps us to identify our bias and reduces the risk of mis-interpretation of the data by just finding what we originally wanted to find.

Transform Data: Aesthetics are important when it comes to data visualization, but this doesn’t mean that the graphs and charts need to have a ton of colors and effects. Here, we can subscribe to the old adage that “less is more.” Less may be more, but that doesn’t mean you should completely forgo any effects. Play around with one or two effects to see what best represents your data or most helps the viewer understand the data.

Have Someone Else Take a Look: Even if you’re pretty clear on what you’re seeing, get another set of eyes to take a look at your charts and graphs; one person can’t always see everything. You’ll get clean and clear insights as to what your data is saying.

Double-Check Your Data: Be skeptical with your data. Question what you’re seeing and look at it in as many different ways as possible to make sure you are understanding it correctly and interpreting it how someone else might see it. You don’t want to unintentionally mislead anyone, and you certainly don’t want to intentionally deceive.

There’s a lot you can do with visualizing data, but the real artistry comes in displaying it in such a way that brilliant, actionable insights emerge where they weren’t previously visible.

Sources: https://datahero.com/blog/2015/02/26/art-visualizing-data-find-actionable-insights/

http://datajournalismhandbook.org/1.0/en/understanding_data_7.html

 http://www.scribblelive.com/blog/2014/07/18/self-verifiable-visualizations/