Statistics for Data Science: Leverage the Power of Statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks
Synopsis "Statistics for Data Science: Leverage the Power of Statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks"
Get your statistics basics right before diving into the world of data scienceKey FeaturesNo need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;Implement statistics in data science tasks such as data cleaning, mining, and analysisLearn all about probability, statistics, numerical computations, and more with the help of R programsBook DescriptionData science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.What you will learnAnalyze the transition from a data developer to a data scientist mindsetGet acquainted with the R programs and the logic used for statistical computationsUnderstand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and moreLearn to implement statistics in data science tasks such as data cleaning, mining, and analysisLearn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networksGet comfortable with performing various statistical computations for data science programmaticallyStyle and approachStep by step comprehensive guide with real world examplesWho This Book Is ForThis book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.Table of ContentsTransitioning from Data Developer to Data ScientistDeclaring the ObjectivesA Developer's Approach to Data CleaningData Mining and the Database DeveloperStatistical Analysis for the Database DeveloperDatabase Progression to Database RegressionRegularization for Database ImprovementDatabase Development and AssessmentDatabases and Neural NetworksBoosting your DatabaseDatabase Classification using Support Vector MachinesDatabase Structures and Machine Learning
