Just finished the guided project “Visualizing The Gender Gap In College Degrees”. It was probably the most pampered guided project in DQ so far. Almost everything was given already!
It took me a while to figure out how to access each subplot and then it was easy afterwards.
Also the explanation from @Sahil related to removing the spine dictionary helped on the way. I personally believe it should be updated along with the guided project or with the missions somewhere.
Anyway, if you guys have some time to spare, please go through my latest guided project and let me know how I can improve it.
Hello @jithins123, Congratulations on finishing this project, a lot of students have found this to be the most difficult/challenging project, by adding comments to it makes it easier to understand. The overall project style is pretty.
Hi Victor, Thanks a lot for your kind words. This could be a very challenging project. But initial code was already provided by DQ terminal which is not the usual case in other guided projects. Maybe that is why it felt easier. Anyway thank you for going through my project.
I looked through your project, everything is perfect, as always. Well, here I agree with you that it was not the most challenging DQ project so far, and a big part of the code from the previous missions was already pre-written.
Anyway, I liked the way how you organized this project, and the idea to gather all the codes for visualization in the same code cell (I had a different approach, and now I am envious about yours ). That giant code cell was very well-commented, great idea to combine lateral comments for explanations and central ones for a kind of code sub-sections. Also it was good to introduce x and y variables, it made the whole code more readable.
Your conclusions look curious to me, because they are completely different from my own ones. From my point, of view it was mostly about the fact that historically, usually women don’t care about some majors, while men don’t care about the others. Let’s say, this gender gap, in given case, is not an unfair issue to be resolved, it’s just a natural choice. I really cannot imagine that, for example, a lot of men suddenly become interested in Education category, or a lot of women - in Engineering. Even though, judging by these plots, sometimes it really happens (Communications and Journalism, Psychology). I would say, in very few cases this gap was really unfair and prejudiced towards women, like in Business or Politics categories: in those spheres women were just mostly ignored and not accepted. But fortunately, life changes, global preferences and stereotypes also, and so do some of these trends.
Well, this time my comment was mostly philosophical Happy coding!
Thank you for your time to go through my project once again. I’ve checked your project just now and I now know why you are envious
I am glad that you liked my approach in those code cells.
I agree that my conclusion might have been not strictly sticking to the data. But I feel to get the story behind these changes, we might need some more datasets in other areas of life and check against these plots to see what would have happened in those years that made things change. Was it that they didn’t care about it, was it their personal choices, was it because of a new law that came into effect, was it the easy access of technology or was it like a universal uprising or something like that. Maybe I should have stuck to the data and conclude with the mere observations.
Anyway thanks a lot for your time to go through my project.
Well, it sounds very reasonable. Probably for a more objective picture we need other data, clarifying what exactly happened to stimulate this or that change. And also, probably, some more interesting majors, those more influencing and potential (politics, goverment, nuclear energy, etc.), where a gap can really be prejudiced instead of just being a matter of choice. Because in given dataset we have mostly “peaceful” majors. Indeed, who on earth cares about Biology, Psychology and so on? Then, at least from my standpoint, there are professions that are absolutely not female in whatever case (military industry, for instance), as well as some others are absolutely not male. Then such gaps depend also on cultural context, differ from country to country and definitely are not uniform around the world. It seems that there are still a lot of missing pieces of puzzle to understand the whole picture.
Well the interesting thing to note is that this is a data from US education department which means this is the gender gap represents the one in US only. We can’t really generalise it to other parts of the world.
). That giant code cell was very well-commented, great idea to combine lateral comments for explanations and central ones for a kind of code sub-sections. Also it was good to introduce x and y variables, it made the whole code more readable.
Happy coding!
Then, at least from my standpoint, there are professions that are absolutely not female in whatever case (military industry, for instance), as well as some others are absolutely not male. Then such gaps depend also on cultural context, differ from country to country and definitely are not uniform around the world. It seems that there are still a lot of missing pieces of puzzle to understand the whole picture.