Data-Science-Industry

I divided my analysis into 2 parts, in part 1 I modeled the salaries within the data science industry, in part 2 I did some EDA within the industry

Part 1:

I used the contienents2.csv data set and the ds_salaries.csv data set to create a model prediciting the salaries with the data industry with linear regression and ridge regression. I set my predictor to salaries_in_usd in the ds_salaries data set and the rest of the features were qualititative information about their careers. The contienents2 data set allowed me to allocate regions to each observation in the salaries dataset. I also compared the bias and variance of these respective models. To prep my data for these regressions I did some data cleaning and feature engineering.

Part 2:

I did some exploratory data analysis on programming languages used in the data industry using the kaggle_survey_2022_responses.csv data set. This data set was a survery where some questions you could have multiple answers. This was filled out by over 20000 people who worked in the data industry. I had to do some data cleaning, plotting and transformations in order to get insights.