Latin American Labor Market Analysis

Web Scraping Workana

Similar to the first labor market analyis, the first part of this analysis was to scrape the website ‘www.workana.org’ and to gather data into a .csv file. Certain job sites are harder to scrape data from so Workana was chosen because not only was it was easier to scrape the necessary data but also because it is a job site primarily for Latin American countries, which is the target of this data science pilot.

Filtering Dataframes

Once the website was scraped and I had a .csv file, I used Jupyter and python to filter the dataframes and I found that many countries had no recorded data or very little recorded data. I decided to filter for only fourteen countries with the most amounts of recorded data and used this information for the next part of the analysis. Below are different types graphs that look at various ways of analyzing the fourteen countries.

These countries were: Chile, Bolivia, Nicaragua, Colombia, Brazil, Mexico, Argentina, Peru, Venezuela, Jamaica, Uruguay, Honduras, Panama and Guatemala.

In this analysis, my first task was to look at general trends between these fourteen countries. The four graphs below show: the count with outliers, the count without outliers and the distribution of freelancers by the country and the hourly rate.

The next part was to look at the different categories and create graphs to inform my team about various skills within the Latin American economy.

Each of the hundreds of languages from the .csv file were organized and placed within one of the ten categories below. The organization of some of the categories are seen in the image below. The categories were:

1. Websites, IT & Software - WIS

2. Mobile Phones & Computing - MPC

3. Writing and Content - WC

4. Design Media, and Architecture - DMA

5. Data Entry and Admin - DEA

6. Sales and Marketing - SM

7. Business, Accounting, Human Resources, and Legal - BAHRL

8. Translation and Languages - TL

9. Local Jobs & Services - LJS

10. Other - OTHER