Best Mathematical & Statistical

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. … ISL contains 12 very valuable R labs that show how to use many of the statistical learning methods with the R package ISLR … .” (David Olive, Technometrics, Vol. “Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. … the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications.” (Klaus Nordhausen, International Statistical Review, Vol. “The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. … The style is suitable for undergraduates and researchers … and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. "The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples.

Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models. Garrett received his Ph.D at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.

Key Features Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas. What you will learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn. Table of Contents Pandas Foundations Essential DataFrame Operations Beginning Data Analysis Selecting Subsets of Data Boolean Indexing Index Alignment Grouping for Aggregation, Filtration and Transformation Restructuring Data into Tidy Form Joining multiple pandas objects Time Series Visualization. Some of his projects included using targeted sentiment analysis to discover the root cause of part failure from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid the mispricing of sales items.

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A free, world-class education for anyone, anywhere: this is the goal of the Khan Academy, a passion project that grew from an ex-engineer and hedge funder's online tutoring sessions with his niece, who was struggling with algebra, into a worldwide phenomenon. "Since its founding in 2006, Sal Khan's project-the Khan academy-has revolutionized our thinking on the potential and promise of unfettered, open-access online education. In his new book The One World Schoolhouse, Khan presents his vision and blueprint for how online technology can, and should, play an integral role in educating communities across the globe, closing the opportunity gap and providing high-quality education for all. "I discovered Sal Khan and Khan Academy like most other people - by using these incredible tools with my own kids. Sal Khan's vision and energy for how technology could fundamentally transform education is contagious. "The world dreams of education reform, and Sal Khan is delivering. "Sal Khan is changing what we believe is possible in education. "When you read this book, you will understand how the dignity of each student is addressed by education's visionary, Sal Khan. "Sal Khan has developed the best and most cost-efficient way to use technology to bring high quality education, creativity and innovation to all countries, including the poorest.

With QuickBooks 2016: The Missing Manual (which covers the Windows version of QuickBooks), you’re in control: you get step-by-step instructions on how and when to use specific features, along with basic bookkeeping and accounting advice to guide you through the learning process. Spend less time on bookkeeping. Use QuickBooks’ Search and Find features, as well as the Vendor, Customer, Inventory, and Employee Centers. Bonnie Biafore is a Project Management Professional (PMP) with more than 20 year's experience helping clients, large and small, make project management excellence part of their business strategies.

Spreadsheet himself Whether you are just starting out or an Excel novice, the Excel 2016 Bible is your comprehensive, go-to guide for all your Excel 2016 needs. From learning to incorporate templates and implementing formulas to creating pivot tables and analyzing data—and so much more—this authoritative guide will be a trusted reference you'll refer to again and again. Create an Excel workbook Work with formulas and functions Make charts and incorporate graphics Visualize data using conditional formatting Use advanced Excel features Customize your Excel user interface Make the most of add-ins in your spreadsheets.

Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion).