Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found insidePresents case studies and instructions on how to solve data analysis problems using Python. What this book aims to do... This book is written with one goal in mind - to help beginners overcome their initial obstacles to learning Data Visualization using Python. A lot of times, newbies tend to feel intimidated by coding and data. Guided by expert instructors Michael Freeman and Joel Ross, this book will help learners install the tools required to solve professional-level data science problems, including widely used R language, RStudio integrated development ... This book's source code can be found at http://www.mango-solutions.com/wp/teach-yourself-r-in-24-hours-book. Found insideWhat you will learn Explore and apply different interactive data visualization techniques Manipulate plotting parameters and styles to create appealing plots Customize data visualization for different audiences Design data visualizations ... Found insideIn the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. By the end of the book, you'll be prepared to build your own web-based data visualizations with D3. Join a dataset with elements of a webpage, and modify the elements based on the data Map data values onto pixels and colors with D3's scale ... Found insideThis book introduces programming to readers with a background in the arts and humanities; there are no prerequisites, and no knowledge of computation is assumed. Found inside – Page iIn this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. Found inside – Page iNo prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to 150 figures produced with lattice. Found insideThis book will be a handy guide to quickly learn pandas and understand how it can empower you in the exciting world of data manipulation, analysis, and data science. Found insideThis book will teach you all the processes you need to build a predictive analytics solution: understanding the problem, preparing datasets, exploring relationships, model building, tuning, evaluation, and deployment. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain ... This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Found insideBy the end of this book, you will be able to effectively solve a broad set of data analysis problems. Style and approach The approach of this book is not step by step, but rather categorical. In Pale Blue Dot, Sagan traces the spellbinding history of our launch into the cosmos and assesses the future that looms before us as we move out into our own solar system and on to distant galaxies beyond. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Get started solving problems with the Python programming language!This book introduces some of the most famous scientific libraries for Python: * Python's math and statistics module to do calculations * Matplotlib to build 2D and 3D plots * ... This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested ... Found insideAfter introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. Found insideKey Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for ... Found insideIf you are a Python novice or an experienced developer and want to explore data visualization libraries, then this is the book for you. No prior charting or graphics experience is needed. Found insideAs the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. Found insideA far-reaching course in practical advanced statistics for biologists using R/Bioconductor, data exploration, and simulation. Found inside – Page 1This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash ... Found insideIn this book, you'll cover different ways of downloading financial data and preparing it for modeling. Found inside – Page iiiWritten for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in ... Found insideUnderstand, evaluate, and visualize data About This Book Learn basic steps of data analysis and how to use Python and its packages A step-by-step guide to predictive modeling including tips, tricks, and best practices Effectively visualize ... Found insideNow, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkison ... Found insideCovering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and ... Found insideFocusing on the exploration of data with visual methods, this book presents methods and R code for producing high-quality static graphics, interactive visualizations, and animations of time series, spatial, and space-time data. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. Found inside – Page 1You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory ... Found insideThis book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. Found insideBy the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. Found insideAfter reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment ... "Practical recipes for visualizing data"--Cover. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. Found insideWhat you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement ... Found inside – Page iThis is the first volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. Found insideThis volume discusses how surveys, which are employed in many different research areas, generate categorical data. Found inside"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"-- Found insideHelpful tips: If you are able to search the book, search for "Where are the lesson files?" Go to the very last page of the book and scroll backwards. Necessary, although some experience with programming may be helpful computing and data science: students, researchers,,. Guide to support individual practical exploration based on MATLAB programs this is the book the! 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