What this book covers
Chapter 1, Review of the Core Modules of NumPy and Pandas, introduces two of three main modules used for data manipulation, using real dataset examples to show their relevant capabilities.
Chapter 2, Review of Another Core Module – Matplotlib, introduces the last of the three modules used for data manipulation, using real dataset examples to show its relevant capabilities.
Chapter 3, Data – What Is It Really?, puts forth a technical definition of data and introduces data concepts and languages that are necessary for data preprocessing.
Chapter 4, Databases, explains the role of databases, the different kinds, and teaches you how to connect and pull data from relational databases. It also teaches you how to pull data from databases using APIs.
Chapter 5, Data Visualization, showcases some analytics examples using data visualizations to inform you of the potential of data visualization.
Chapter 6, Prediction, introduces predictive models and explains how to use Multivariate Regression and a Multi-Layered Perceptron (MLP).
Chapter 7, Classification, introduces classification models and explains how to use Decision Trees and K-Nearest Neighbors (KNN).
Chapter 8, Clustering Analysis, introduces clustering models and explains how to use K-means.
Chapter 9, Data Cleaning Level I – Cleaning Up the Table, introduces three different levels of data cleaning and covers the first level through examples.
Chapter 10, Data Cleaning Level II – Unpacking, Restructuring, and Reformulating the Table, covers the second level of data cleaning through examples.
Chapter 11, Data Cleaning Level III – Missing Values, Outliers, and Errors, covers the third level of data cleaning through examples.
Chapter 12, Data Fusion and Data Integration, covers the technique for mixing different data sources.
Chapter 13, Data Reduction, introduces data reduction and, with the help of examples, shows how its different cases and versions can be done via Python.
Chapter 14, Data Transformation and Massaging, introduces data transformation and massaging and, through many examples, shows their requirements and capabilities for analysis.
Chapter 15, Case Study 1 – Mental Health in Tech, introduces an analytic problem and preprocesses the data to solve it.
Chapter 16, Case Study 2 – Predicting COVID-19 Hospitalizations, introduces an analytic problem and preprocesses the data to solve it.
Chapter 17, Case Study 3 – United States Counties Clustering Analysis, introduces an analytic problem and preprocesses the data to solve it.
Chapter 18, Summary, Practice Case Studies, and Conclusions, introduces some possible practice cases that users can use to learn in more depth and start creating their analytics portfolios.
