This book is the guide for people who want to learn about Phyton programming, in particular its application for data science.There are so many great ways that you can use the data you have been collecting for some time now, and being able to complete the process of data visualization will ensure that you get it all done, when you are ready to get started with Python data science.This guide book contains:What Is Data Science And Why Should You Learn It Using PythonCan I Use Probability And Statistics To Help Me With Machine Learning?Work With NumpyWork With PandasLearning About FunctionsIdentifying The Nearest NeighborsLearning About FunctionsDeep Learning Vs Machine LearningApplications Of Big Data AnalysisAnd many more!3 Menuscripts:Python Crash CourseWhat Is Python and Why Should You Learn ItVariables and OperatorsLearn About Simple Data TypesConditional Statements in Python and Control Flow StatementsWorking With FunctionsObject-Oriented ProgrammingWorking with FilesPython From ScratchHow Coding WorksPython LibrariesGetting Program Functioning to WorkCoding With PythonPhython Programming BasicsWhat Is Scikit-Learn, and Why Should I Learn About It?Essential Libraries And Tools In PythonLearn ModulesMachine Learning DatasetsTraining Simple Machine Learning Algorithms for ClassificationUnsupervised Machine LearningLearning in Artificial Neural NetworksThe PerceptronExecution and Repetitive TasksVariables and StringsPython Data ScienceWhat Is Data Science And Why Should You Learn It Using PythonStatistics and ProbabilityWork With NumpyWork With PandasLearning About FunctionsDeveloping a Machine Learning Model with PythonIdentifying the Nearest NeighborsDeep Learning vs Machine LearningOther Basics of the Python CodeModulesApplications of Big Data Analysis