Dothang Truong is the Associate Dean for the School of Graduate Studies and Professor of Aviation Data Science at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, artificial intelligence, data analytics, aviation safety, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for the outstanding achievement of excellence in aviation research and scholarship. He is ranked #1 globally in the specialty of Traffic Management by ScholarGPS (2024) and recognized as a Highly Ranked Scholar—Prior Five Years, placing in the top 0.05% of scholars worldwide for exceptional scholarly productivity, impact, and quality.

• PART I Introduction to Data MiningChapter 1 Introduction to Data Mining and Data ScienceChapter 2 Data Mining Processes, Methods, and SoftwareChapter 3 Data Sampling and PartitioningChapter 4 Data Visualization and ExplorationChapter 5 Data ModificationPART II Data Mining MethodsChapter 6 Model EvaluationChapter 7 Regression MethodsChapter 8 Decision TreesChapter 9 Neural NetworksChapter 10 Ensemble ModelingChapter 11 Presenting Results and Writing Data Mining ReportsChapter 12 Principal Component AnalysisChapter 13 Cluster AnalysisPART III Advanced Data Mining MethodsChapter 14 Random ForestChapter 15 Gradient BoostingChapter 16 Bayesian NetworksAppendix AAppendix BAppendix C

Dothang Truong is the Associate Dean for the School of Graduate Studies and Professor of Aviation Data Science at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, artificial intelligence, data analytics, aviation safety, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for the outstanding achievement of excellence in aviation research and scholarship. He is ranked #1 globally in the specialty of Traffic Management by ScholarGPS (2024) and recognized as a Highly Ranked Scholar—Prior Five Years, placing in the top 0.05% of scholars worldwide for exceptional scholarly productivity, impact, and quality.

• PART I Introduction to Data MiningChapter 1 Introduction to Data Mining and Data ScienceChapter 2 Data Mining Processes, Methods, and SoftwareChapter 3 Data Sampling and PartitioningChapter 4 Data Visualization and ExplorationChapter 5 Data ModificationPART II Data Mining MethodsChapter 6 Model EvaluationChapter 7 Regression MethodsChapter 8 Decision TreesChapter 9 Neural NetworksChapter 10 Ensemble ModelingChapter 11 Presenting Results and Writing Data Mining ReportsChapter 12 Principal Component AnalysisChapter 13 Cluster AnalysisPART III Advanced Data Mining MethodsChapter 14 Random ForestChapter 15 Gradient BoostingChapter 16 Bayesian NetworksAppendix AAppendix BAppendix C

Dothang Truong is the Associate Dean for the School of Graduate Studies and Professor of Aviation Data Science at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, artificial intelligence, data analytics, aviation safety, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for the outstanding achievement of excellence in aviation research and scholarship. He is ranked #1 globally in the specialty of Traffic Management by ScholarGPS (2024) and recognized as a Highly Ranked Scholar—Prior Five Years, placing in the top 0.05% of scholars worldwide for exceptional scholarly productivity, impact, and quality.

• PART I Introduction to Data MiningChapter 1 Introduction to Data Mining and Data ScienceChapter 2 Data Mining Processes, Methods, and SoftwareChapter 3 Data Sampling and PartitioningChapter 4 Data Visualization and ExplorationChapter 5 Data ModificationPART II Data Mining MethodsChapter 6 Model EvaluationChapter 7 Regression MethodsChapter 8 Decision TreesChapter 9 Neural NetworksChapter 10 Ensemble ModelingChapter 11 Presenting Results and Writing Data Mining ReportsChapter 12 Principal Component AnalysisChapter 13 Cluster AnalysisPART III Advanced Data Mining MethodsChapter 14 Random ForestChapter 15 Gradient BoostingChapter 16 Bayesian NetworksAppendix AAppendix BAppendix C