What does the developer community has to say to the World !
By: Annish Prashanth
Tags: Data Visualization, Data Scientist, Python, Data Science, Insights

2. Most popular languages for developer community in the last 5 years

In this analysis, we try to derive both the language popular options currently worked and also desired to work in future among the developer community for the last 5 years. Let us consider for our scope, the top 5 prominent programming languages for deriving the insights.

There has been a clear down trend for Java and C# over the last 5 years, while Python has gained the popularity and emerged as the #3rd most popular language among all.

JavaScript and SQL maintain a steady pace throughout the span of 5 years and continue to hold their #1 and #2 positions respectively. The general sentiment is that JavaScript and SQL are the most prominent languages in past 5 years.

Figure 2.a: Top 5 languages currently popular among developers.

This analysis is an extension of the previous step, where the most desired languages among developers for the future trends can be visualized as shown in the below plot.

The general sentiment is that Python will replace all the other contingents in the top 5 the most prominent languages desired to work with in the coming years. But here is the most important finding,

There is a clear down trend for Java and C# in the future.

SQL and JavaScript have just started to slightly lose momentum among the developers in the desired set of languages.

Figure 2.b: Top 5 languages desired to work with in future.

3. Multivariate plot for understanding the relationship between age and compensation of the developer across the genders.

The longer you spend in the technology industry, the more you can expect to earn. According to multiple surveys, tech pros with significant levels of experience under their proverbial belt should see heady increases in annual salary.

The most obvious way to plot lots of variables is to augment the visualizations is to make use of more visual variables and plot a multi variate visualization. Good” multivariate data displays are the ones that make efficient, easily-interpretative use of these parameters.We’ll choose the scatter plot.

This scatter plot uses three visual variables. The horizontal position (x-value) tracks the Age of the developer. The vertical position (y-value) tracks the Compensation salary of the developer converted in standard currency. . And the Gender(the hue parameter) tracks the gender spread for this representation.

Figure 3.a: Multi-variate plot — complex

Figure 3.b: Multi-variate plot — simplified

In this scatter chart , I was interested in salary spread across age and gender. The axis represents a comparison of age to the average salary compensation , while the third variate is the gender which is representing by various markers.

The multiple categories in gender column can be grouped and simplified for better visual representation. Hence this scatter plot simplifies the representation of age VS salary relationship across only 3 genders.

The above scatter plots are multi-variate visualization of Age VS Compensation across different genders. The plot did look little clumsy and difficult to read due to the several categories of gender present in this survey data. In the second step, this plot was simplified by effective grouping of the genders (Data Transformation).

4. Data exploration of some of the common categorical responses of the survey