Learn how to summarize categorical data.
- [Instructor] Categorical data is described by…how observations are distributed across…the variable's categories.…A very simplistic approach to sentiment analysis…could involve web scraping public product reviews.…Then classifying certain words found in the scraped data…as positive and others as negative.…Lastly, you do a categorical word count…on the product review data to score a product review…or feedback as either good or bad.…Categorical variables only assume a fixed number of values.…
For example, think of a fruit.…A fruit can be an apple, an orange,…a lemon, a pear, a prune, et cetera.…But there are infinite options.…In other words, fruits fall into one category or another.…Categorical variables are easily summarized…using counts, grouping, variable descriptions,…or cross-tabulations.…Before going into the demonstration,…I want to explain to you a little bit more…about cross-tabulation.…These tables are really called crosstabs in practice.…
A crosstab is a cross-tabulation of two or more features.…By default, a crosstab table shows frequency counts…
AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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