The Crystal Ball Instruction Manual Volume Two: Foundations For Data Science

• Intermission and review
• Navigating the Spyder’s web
• EDA: review and extensions
• KDEs and distributions
• Random value generation
• Synthetic data sets
• JSON (1 of 2)
• JSON (2 of 2)
• LOWESS
• Data fusion
• Long, wide, and “tidy” data
• Dates and times
• Using logarithms
• Accessing databases
• Screen scraping (1 of 2)
• Screen scraping (2 of 2)
• Probabilistic reasoning
• Causality
• Naïve Bayes (1 of 3)
• Naïve Bayes (2 of 3)
• Naïve Bayes (3 of 3)
• APIs
• kNN (1 of 2)
• kNN (2 of 2)
• Two key ML principles
• Feature selection
• Association Analysis
• “Special” data types

Welcome to Volume Two of the Crystal Ball series: Foundations for Data Science! I titled the first volume “Introduction to Data Science” because it led you through a dip-your-toes-in-the-water experience. You took a brief tour through the various elements in this diverse field and got a feel for what it was all about.

Since you’re still reading, this means you’re still interested, and ready to explore the next level. In Foundations, we’ll solidify your growing knowledge so that you have a firm base on which to build everything else. Future volumes in this series will cover applications, advanced techniques, and some special data types that require special treatment. But it all ultimately rests on the skill set you’ll have built in Volumes One and Two of this series.