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Data Science: Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data. This book has the following topics: Introduction - Introduction to Data Science, Terminology Used in Data Science, Data Science Process, Types of data classification-data science algorithms, Applications of Data Science, Data collection and management. Data collection methods, Data collection and APIs, API Categories, Exploring and fixing data, Data storage and management, Using multiple data sources, Data collection and management, Data Analysis: Introduction, Terminology and concepts, Introduction to Statistics, Central tendencies and distributions, Variance, Distribution properties and arithmetic, Central Limit Theorem (CLT), Basic Machine Learning Algorithms: Linear Regression, Basic Machine learning algorithms: SVM, Basic Machine Learning Algorithms: Naïve Bayes, Data Visualization, Types of data visualization, Data for visualization: Data types, Data encodings, Retinal variables/Visual Encodings, Mapping Variables to Encodings, Introduction to Matplotlib, Applications, Applications of Data Science, Technologies for Visualization, Bokeh (Python), Recent Trends in Various Data Collection and Analysis Techniques.