Junior - Projeto II

[linharesjunior]

https://github.com/linharesjunior/Project-Data-Pipeline.git

[sh-ih]

You did great on this project!

• Repository You have an organized repository with files named clearly. I would only suggest you to create a notebooks folder for all your Jupyter Notebooks and leave the src only for .py files that contain the functions you create for modularization/encapsulation.

• Acquisition and enrichment of database Your decision to use wage data to analyze alongside the rent data gives a very nice insight on the situation from not only the real estate market but the actual economic impact of rising rents and stagnant wages.

But the source of the data you use as a data analyst is very important. In this case, you scrapped the countryeconomy.com website, which doesn’t specify the source of their data. Is it official? Self-calculated? We don’t know. It is always a good idea to consult official sources, in this case the INE’s Encuesta Anual de Estructura Salarial (https://www.ine.es/jaxiT3/Datos.htm?t=28187) could be a good first step.

And, since you are working with local data, it could be even better to search for wage data specific to Barcelona, because the wage trends could be different from the national trends. Here is data from 2021 (https://ajuntament.barcelona.cat/barcelonaeconomia/sites/default/files/Salaris_2021.pdf) that says that the gross annual salary of Barcelona’s residents was 32,324 euros in 2021, above both the INE’s data (25,896 euros) and countryeconomy.com’s data (27,570 euros).

• Reporting You have your analysis and conclusions mainly on your README, but it is important that you include that too in the notebook that will work as your final report, in this case it could be the Visualization.ipynb or you could even create a notebook just for the final report, where you can develop your storytelling. You have the opportunity to write more in depth analysis and conclusions there and just keep the most attractive points in the README.

On your Canva slides, you use a line graph to show correlation between average salary over the years and rent increase, but a better choice of graph to show the relationship between two variables is a scatterplot.

• README Your README is organized and informative. On the Hypothesis section, I would consolidate the numbered list and the explanations on the bulleted list on just one list.

And a way to make your README more attractive is to include your visualizations right along with your conclusions, that way the reader can see both at the same time. Here is a tutorial to do that: https://www.digitalocean.com/community/tutorials/markdown-markdown-images

I really liked how you write your conclusions. The text is clear and has a good mix of text and data to justify what you are saying. I would only try to separate your sentences so you have more than one paragraph. Big blocks of continuous text are hard to read and can discourage some people.

• Bonus: modularization/encapsulation I see you did create functions to do the extraction of wage data and merging it to rent data, as well as to create visualizations. Now you would only need create the .py files with those functions and import them to your notebooks. Then you can modify your notebooks to call those modules to use the functions.