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Tabla de Contenidos: “…Classifying with K-Nearest Neighbor Algorithms -- Understanding how the k-nearest neighbor algorithm works -- Knowing when to use the k-nearest neighbor algorithm -- Exploring common applications of k-nearest neighbor algorithms -- Solving Real-World Problems with Nearest Neighbor Algorithms -- Seeing k-nearest neighbor algorithms in action -- Seeing average nearest neighbor algorithms in action -- Chapter 6 Coding Up Data Insights and Decision Engines -- Seeing Where Python and R Fit into Your Data Science Strategy -- Using Python for Data Science -- Sorting out the various Python data types -- Putting loops to good use in Python -- Having fun with functions -- Keeping cool with classes -- Checking out some useful Python libraries -- Using Open Source R for Data Science -- Comprehending R's basic vocabulary -- Delving into functions and operators -- Iterating in R -- Observing how objects work -- Sorting out R's popular statistical analysis packages -- Examining packages for visualizing, mapping, and graphing in R -- Chapter 7 Generating Insights with Software Applications -- Choosing the Best Tools for Your Data Science Strategy -- Getting a Handle on SQL and Relational Databases -- Investing Some Effort into Database Design -- Defining data types -- Designing constraints properly -- Normalizing your database -- Narrowing the Focus with SQL Functions -- Making Life Easier with Excel -- Using Excel to quickly get to know your data -- Reformatting and summarizing with PivotTables -- Automating Excel tasks with macros -- Chapter 8 Telling Powerful Stories with Data -- Data Visualizations: The Big Three -- Data storytelling for decision makers -- Data showcasing for analysts -- Designing data art for activists -- Designing to Meet the Needs of Your Target Audience -- Step 1: Brainstorm (All about Eve) -- Step 2: Define the purpose…”