Statistics for Data Science

Get your statistics basics right before diving into the world of data science About This Book * No 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 analysis * Learn all about probability statistics numerical computations and more with the help of R programs Who This Book Is For This 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. What You Will Learn * Analyze the transition from a data developer to a data scientist mindset * Get acquainted with the R programs and the logic used for statistical computations * Understand mathematical concepts such as variance standard deviation probability matrix calculations and more * Learn to implement statistics in data science tasks such as data cleaning mining and analysis * Learn the statistical techniques required to perform tasks such as linear regression regularization model assessment boosting SVMs and working with neural networks * Get comfortable with performing various statistical computations for data science programmatically In Detail Data 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. Style and approach Step by step comprehensive guide with real world examples About the Author James D. Miller An IBM certified expert creative innovator and accomplished Director Sr. Project Leader & Application/System Architect with +35 years of extensive applications and system design & development experience across multiple platforms and technologies. Experiences include introducing customers to new and sometimes disruptive technologies and platforms integrating with IBM Watson Analytics Cognos BI TM1 and Web architecture design systems analysis GUI design and testing Database modelling and systems analysis design and development of OLAP Client/Server Web and Mainframe applications and systems utilizing: IBM Watson Analytics IBM Cognos BI & TM1 (TM1 rules TI TM1Web and Planning Manager) Cognos Framework Manager dynaSight - ArcPlan ASP DHTML XML IIS MS Visual Basic and VBA Visual Studio PERL SPLUNK WebSuite MS SQL Server ORACLE SYBASE Server etc. Responsibilities have also included all aspects of Windows and SQL solution development & design including: analysis; GUI (and Web site) design; data modelling; table screen/form & script development; SQL (and remote stored procedures and triggers) development/testing; test preparation and management and training of programming staff. Other experience includes development o