Loved by learners at thousands of companies
The same name can be spelled out in a many ways (for example, Marc and Mark, or Elizabeth and Elisabeth). Sound can, therefore, be a better way to match names than spelling. In this project, you will use the Python package [Fuzzy](https://pypi.python.org/pypi/Fuzzy) to find out the genders of authors that have appeared in the New York Times Best Seller list for Children's Picture books. First, using fuzzy (sound) name matching, you will search for author names in a dataset provided by the US Social Security Administration that contains names and genders of all individuals who have applied for Social Security Cards. Next, we'll aggregate the author dataset by including gender. Finally, you will use the new dataset to plot the gender distribution of children's picture books authors over time. To complete this project, you should be familiar with `pandas` DataFrames, NumPy for basic statistics, and Matplotlib for plotting.
- 1Sound it out!
- 2Authoring the authors
- 3It's time to bring on the phonics... _again_!
- 4The inbetweeners
- 5Playing matchmaker
- 6Tally up
- 7Foreign-born authors?
- 8Raising the bar
Academic entrepreneur with a focus on data science
Tufool’s data science journey begun at MIT, where she obtained her earliest degrees in EECS. Later, after receiving her PhD from Imperial College, she joined the College as an Assistant Professor of Data Science and Innovation. Today, Tufool is working on an exciting project (“Chartyn”) that uses machine learning to present insightful data visuals. Find out more about Tufool and the Chartyn project.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA