Learn how to transform dataset distributions. This video covers preparing data for machine learning, normalization, and standardization.
- [Instructor] Now it's time to look at … how to scale and transform 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 dataset in 1990 from one in 2016. … That's all you could get, … so you measure the average sales revenues … 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 an 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.
- Why use Python for working with data
- Filtering and selecting data
- Concatenating and transforming data
- Data visualization best practices
- Visualizing data
- Creating a plot
- Creating statistical data graphics
- Performing basic math and linear algebra
- Correlation analysis
- Multivariate analysis
- Data sourcing via web scraping
- Introduction to natural language processing
- Collaborative analytics with Plotly
Skill Level Intermediate
Python Statistics Essential Trainingwith Michele Vallisneri2h 58m Intermediate
1. Introduction to the Data Professions
High-level course road map1m 28s
2. Data Preparation Basics
3. Data Visualization 101
4. Practical Data Visualization
5. Basic Math and Statistics
6. Data Sourcing via Web Scraping
7. Collaborative Analytics with Plotly
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