INTRODUCTION TO DATA SCIENCE

The book systematically introduces the basic contents of data science, including data preprocessing and basic methods of data analysis, handling special problems (e.g. text analysis), deep learning, and distributed systems.

In addition to systematically introducing the basic content of data science from a theoretical point of view, the book also provides a large number of data analysis practice cases.

Contents:
IntroductionData PreprocessingRegression ModelClassification ModelEnsemble MethodClustering ModelAssociation Rule MiningDimensionality ReductionFeature SelectionEM AlgorithmProbabilistic Graphical ModelText AnalysisGraph and Network AnalysisDeep LearningDistributed ComputingAppendices:Matrix OperationProbability BasisOptimization AlgorithmDistanceModel Evaluation
Readership: Undergraduate and graduate students, researchers.

Key Features: This book give a comprehensive introduction of techniques, models and algorithms in data science We also include advanced topics such as deep learning Theory combined with practical cases