Data Science

This book provides a guided tour along the wide range of ML methods that have proven useful in process industry. Step-by-step instructions, supported with real process datasets, show how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, soft sensing, and process control. Also available at Google Play.

Selected modern machine learning techniques and the intuition behind them. Methods are supplemented by code snippets with examples in R language. The process is shown through a comic book describing the adventures of two characters, Beta and Bit.  See the flipbook version at https://betaandbit.github.io/RML/

All statistical foundations you need to understand and use machine learning! It includes R/Pyhton software and Shiny dashboards to illustrate numerically the most important concepts.

The book is for data monetization purposes, helps you to build bridge between IT department and business design to maximise data driven value creation. Data productizement and servitization are explained with real-world example case stories. This book is primarily for data business developers and contains just bare minimum of technical terms. 

Six data analysts across the country have teamed up for a new volume for the EDDR series. Following the success of the first volume, this new book covers how to document work, navigate data governance, ensure transparency and reproducibility in analysis; how early warning systems work; and how awareness of identities shaping the work is critical.

You will study CT and X-ray scans, segment images, and analyze metadata. Even if you have not used with medical imaging before, you will have all the necessary skills upon completion of the book.

Table of Contents:Statistics and probability- DistributionSupervised machine learning- Binary classification- Regression- Singular Value Decomposition (SVD)- Logistic RegressionGradient DescentLoss measureData Science Interview QuestionsUsefull links and preparation repository

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from Software Diagnostics Institute and Software Diagnostics Library (former Crash Dump Analysis blog) written in February 2010 - October 2010. This major revision contains corrections and WinDbg output color highlighting.

Este e-book vai te introduzir rapidamente às técnicas para encontrar fontes de dados e transformá-las em matérias jornalísticas através de um 'Roubo do Jornalismo de Dados'.

El complemento imprescindible del manual Fundamentos de R.

Este manual está dedicado íntegramente a los fundamentos de R y sólo a ellos. Se espera así complementar la formación de los científicos de datos, que disponen de cientos de manuales sobre análisis de datos con R pero apenas cuentan con manuales, completos, que fundamenten en detalle la herramienta a utilizar.

DataViz: How to Choose the Right Chart for Your Data is a short guide to all the different types of charts you’ll commonly encounter in statistics.It is a snappy little non-threatening book about everything you ever wanted to know about the craft of creating inspirational graphics for your study – irrespective of your audience.

Python is a rich and powerful language, but many data scientists merely scratch the surface, and often feel uncertain about what lies beneath.  This book will go deep into the heart of Python, to truly understand its components, and how we can stitch them together to build better scientific workflows and machine learning systems.

Completely hands-on so that you can start the real work!

The book is intended to get you acquainted with the world of Supervised Machine Learning and does not assume previous knowledge of the field. The commonly leveraged Linear Regression technique used to provide predictions that are continuous in nature is detailed in the book. SAMPLE CODE INCLUDED!