Before revision: Introduction Basic Visualization and Exploratory Data Analytics Linear Algebra, Matrix Computing, and Regression Modeling Linear and Nonlinear Dimensionality Reduction Supervised Classification Black Box Machine Learning Methods Qualitative Learning Methods—Text Mining, Natural Language Processing, and Apriori Association Rules Learning Unsupervised Clustering Model Performance Assessment, Validation, and Improvement Specialized Machine Learning Topics Variable Importance and Feature Selection Big Longitudinal Data Analysis Function Optimization Deep Learning, Neural Networks Reception The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the International Statistical Institute’s ISI Review Journal and the Journal of the American Library Association.
After revision: Journal of the American Statistical Association ,,Introduction Basic Visualization and Exploratory Data Analytics Linear Algebra, Matrix Computing, and Regression Modeling Linear and Nonlinear Dimensionality Reduction Supervised Classification Black Box Machine Learning Methods Qualitative Learning Methods—Text Mining, Natural Language Processing, and Apriori Association Rules Learning Unsupervised Clustering Model Performance Assessment, Validation, and Improvement Specialized Machine Learning Topics Variable Importance and Feature Selection Big Longitudinal Data Analysis Function Optimization Deep Learning, Neural Networks Reception The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the International Statistical Institute’s ISI Review Journal and the Journal of the American Library Association.