Exploring Salary Data Trends in Excel and Python - How to clean and analyze anonymized, self-reported salary data from Google Sheets into Python/Jupyter Notebook.
How to clean and analyze anonymized, self-reported salary data from Google Sheets into Python/Jupyter Notebook.
I recently was told about self-reported, anonymous salary information and so was curious to analyze these data and see what patterns emerge.
The increased transparency promoted by the sharing of this information publicly is beneficial for understanding and avoiding pay inequities experienced by women and people of color. These data can be looked through to find one’s age group, level of experience, and similar roles, to see what level of pay they can expect. One can use this information to determine if they are being underpaid, or to choose what career path might be most worth getting in to.
In this article, I’ll work through the first step of the data science pipeline, data cleaning, and end with a couple of visualizations to understand these data better. I’ll end with some future next steps for these data.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
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This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python.
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