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Analyzing US Census Data in Python

IntermediateSkill Level
4.8+
58 reviews
Updated 07/2023
Learn to use the Census API to work with demographic and socioeconomic data.
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PythonExploratory Data Analysis5 hr16 videos57 Exercises4,850 XP7,373Statement of Accomplishment

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Course Description

Data scientists in diverse fields, from marketing to public health to civic hacking, need to work with demographic and socioeconomic data. Government census agencies offer richly detailed, high-quality datasets, but the number of variables and intricacies of administrative geographies (what is a Census tract anyway?) can make approaching this goldmine a daunting process. This course will introduce you to the Decennial Census and the annual American Community Survey, and show you where to find data on household income, commuting, race, family structure, and other topics that may interest you. You will use Python to request this data using the Census API for large and small geographies. You will manipulate the data using pandas, and create derived data such as a measure of segregation. You will also get a taste of the mapping capabilities of geopandas.

Prerequisites

Data Manipulation with pandas
1

Decennial Census of Population and Housing

Start exploring Census data products with the Decennial Census. Use the Census API and the requests package to retrieve data, load into pandas data frames, and conduct exploratory visualization in seaborn. Learn about important Census geographies, including states, counties, and tracts.
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2

American Community Survey

3

Measuring Segregation

Explore racial segregation in America. Calculate the Index of Dissimilarity, and important measure of segregation. Learn about and use Metropolitan Statistical Areas, and important geography for urban research. Study segregation changes over time in Chicago.
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4

Exploring Census Topics

In this chapter, you will apply what you have learned to four topical studies. Explore unemployment by race and ethnicity; commuting patterns and worker density; immigration and state-to-state population flows; and rent burden in San Francisco.
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Analyzing US Census Data in Python
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Don’t just take our word for it

*4.8
from 58 reviews
84%
14%
2%
0%
0%
  • Napaporn
    2 weeks ago

  • Praveen
    2 weeks ago

  • Yang
    2 months ago

  • Muhammad Rehan
    3 months ago

  • xiangyu
    3 months ago

  • Samson
    3 months ago

    The dataset was very helpful in gaining techniques which can help explore various insights

Napaporn

Praveen

Yang

FAQs

Is this course suitable for beginners?

No. This course is primarily aimed at intermediate level learners.

What skills will I gain from this course?

By the end of the course, you will be able to find, manipulate and visualize US Census data using Python, calculate Margins of Error and the Index of Dissimilarity, and create choropleth maps with geopandas.

Does this course offer a certificate?

Upon completion of the course, you will receive a certificate of completion that demonstrates further your commitment to deepening your data science skills.

Who will benefit from this course?

Data professionals in diverse fields such as marketing, public health and civic hacking would benefit from the skills gained from completing this course. Knowledge of analyzing US Census Data would also be invaluable for data scientists, data analysts, data engineers and other roles related to data science.

Is it easy to access the data for this course?

Yes, the Decennial Census and the annual American Community Survey data can be accessed easily using the Census API and the requests package - both of which are covered in this course.

What topics are covered in this course?

This course covers topics such as health insurance coverage, gentrification, unemployment by race, commuting patterns, worker density, immigration and state-to-state population flows, and rent burden.

Is it necessary to have any prior knowledge of the Census before taking this course?

No prior knowledge of the Census is needed - the course walks you through the basics of the Census and Census data products.

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