In 2020, I had the amazing experience of working for DigiDems, an organization that embeds technical staff on competitive Democratic campaigns across the United States. The first thing that I worked on was a digital and shareable constituent map for community organizers. I primarily used QGIS and Google products to make maps because they are free and easy to use. I am self-taught and the method I came up with is the result of reading a lot of the documentation. Hopefully, this blog post will help you get started if you are interested in doing something similar.
This was a question that I found myself asking recently and in an attempt to fully understand the answer, I am going to try to articulate it below.
Regression is a statistical measurement that attempts to determine the strength of the relationship between a dependent variable and a series of independent variables.
Linear regression always uses a linear equation, Y = a +bx, where x is the explanatory variable and Y is the dependent variable.
In multiple linear regression, multiple equations are added together but the parameters are still linear.
Simple graphs are essential visual tools for data analysis. If you are starting to learn how to make visualizations in Python, there are small adjustments to your graph parameters that will make them stand out. To get started, import the Pyplot and Seaborn libraries.
I. Choose Matplotlib colors that brighten up your graph.
II. Outline your histogram bins with a chosen edgecolor.
import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inlineplt.rcParams["patch.force_edgecolor"] = True
plt.figure(figsize=(8,8)) #adjust the size of your graphsns.distplot(df["glucose"], bins= 20,color ='tomato',
#modify colors, number of bins, and linewidth for custom looks
As we enter into wildfire season in the U.S., disaster prevention and emergency management are of critical concern. According to NPR, the U.S. Forest Service has warned that this year may be more dangerous than 2018, with one billion acres of land across America at risk of catastrophic wildfires. What can be done to minimize the imminent risk and inevitable harm ahead? Machine learning and AI are now used to develop tools and insights that optimize disaster preparation efforts and predict the behavior of natural disasters.
Never before has a U.S. President used Twitter to such a prolific and disturbing degree. What hidden insights can we glean from the text messages of a president who many believe to be mentally unraveling?
I copied Trump’s tweet data from the Trump Twitter Archive, opting to analyze the tweets starting on Jan. 20, 2017 at the top of his Presidential term. After saving the text data as a csv file, I imported the file into a Jupyter Notebook using the Pandas library.
As a data science student at the Flatiron School, I have been studying statistical distributions and probability…
Hiring the best people for the job is a priority for all organizations, especially at technology companies where the average tenure is about 2 years, resulting in the highest turnover rate (13.2%) of any industry. Those numbers increase for women and people of color who have experienced unfair management practices or stereotyping at work. High turnover costs the industry $16B a year on average and it can take new hires in certain roles more than a year to become effective at their job. …
May is Mental Health Awareness Month. It is the time of year when we are (extra) encouraged to donate to the nonprofits who serve one of the most vulnerable of populations. It is time to highlight the topics surrounding mental health, dispelling stigma, and celebrating innovations that are improving lives. In keeping with the spirit of this month, this blog post will give an overview of the mental health care system challenges in America, summarized from publicly available reports and from my own professional experience. …