Learn about transforming dataset distributions.
- [Instructor] The last thing that I want to discuss…in the math and statistics portion of this course…is scaling and transforming variables.…You scale variables so that differences in magnitudes…don't produce erroneous and misleading results.…For example, imagine you're in charge of sales and marketing…for Zack's Department Store.…To measure the success of a recent holiday campaign,…you decide to compare daily sales revenues…from a data set in 1990 from one in 2016.…That's all you could get,…so you measure the average sales revenue increase…between November 15th and December 15th back in 1990.…
There is an average increase of $20 per checkout…in this time period, but in 2016 the average increase…was $200 per checkout.…Is that net gain of $180 per checkout…due to your marketing savvy?…No, it's due to other factors like monetary inflation…and in increase in brand trust since 1990.…You're trying to compare apples and oranges here…because you forgot to scale your variables.…Before comparing seasonal sales revenue changes,…
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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