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Author: Clark R. Wilson Publisher: Cambridge University Press ISBN: 1108950485 Category : Science Languages : en Pages : 204
Book Description
A concise introduction to geophysical data processing - many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The textbook begins with calculus before transitioning to discrete time series via the sampling theorem, aliasing, use of complex sinusoids, development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, or statistics, this textbook draws scenarios and datasets from across the world of geophysics, and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises (using MATLAB or similar computing environment). Online supplementary resources include datasets for students, and a solutions manual and all the figures from the book as PowerPoints for course instructors.
Author: Clark R. Wilson Publisher: Cambridge University Press ISBN: 1108950485 Category : Science Languages : en Pages : 204
Book Description
A concise introduction to geophysical data processing - many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The textbook begins with calculus before transitioning to discrete time series via the sampling theorem, aliasing, use of complex sinusoids, development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, or statistics, this textbook draws scenarios and datasets from across the world of geophysics, and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises (using MATLAB or similar computing environment). Online supplementary resources include datasets for students, and a solutions manual and all the figures from the book as PowerPoints for course instructors.
Author: Graham J. Williams Publisher: CRC Press ISBN: 1351647490 Category : Business & Economics Languages : en Pages : 295
Book Description
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
Author: Graeme Simsion Publisher: Elsevier ISBN: 0080488676 Category : Computers Languages : en Pages : 561
Book Description
Data Modeling Essentials, Third Edition, covers the basics of data modeling while focusing on developing a facility in techniques, rather than a simple familiarization with "the rules". In order to enable students to apply the basics of data modeling to real models, the book addresses the realities of developing systems in real-world situations by assessing the merits of a variety of possible solutions as well as using language and diagramming methods that represent industry practice. This revised edition has been given significantly expanded coverage and reorganized for greater reader comprehension even as it retains its distinctive hallmarks of readability and usefulness. Beginning with the basics, the book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management. It includes an entirely new section discussing the development of logical and physical modeling, along with new material describing a powerful technique for model verification. It also provides an excellent resource for additional lectures and exercises. This text is the ideal reference for data modelers, data architects, database designers, DBAs, and systems analysts, as well as undergraduate and graduate-level students looking for a real-world perspective. - Thorough coverage of the fundamentals and relevant theory - Recognition and support for the creative side of the process - Expanded coverage of applied data modeling includes new chapters on logical and physical database design - New material describing a powerful technique for model verification - Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict
Author: Alberto Boschetti Publisher: Packt Publishing Ltd ISBN: 1786462834 Category : Computers Languages : en Pages : 373
Book Description
Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Author: Julio Bolton Publisher: Larsen and Keller Education ISBN: 9781641720731 Category : Languages : en Pages : 189
Book Description
A data warehouse (DW) is a system used in computing for data analysis and reporting. It is a core component of business intelligence. It stores integrated historical and current data from one or more sources. Data can be characterized according to data integration, time-variance, subject orientation, volatility, granularity, etc. It is then arranged into groups, facts and aggregate facts. The sources of data are cleansed, catalogued, transformed and used for data mining, market research, decision support and online analytical processing. The ways to analyze or retrieve the data, transform, load and extract data and manage the data dictionary are essential components of a data warehousing system. Data warehouses can be designed using the bottom-up, top-down or hybrid design models. This book aims to shed light on some of the unexplored aspects of data warehousing. Most of the topics introduced herein cover new techniques and applications of this field. Those in search of information to further their knowledge will be greatly assisted by this textbook.
Author: Amar Sahay Publisher: Business Expert Press ISBN: 1631573462 Category : Business & Economics Languages : en Pages : 440
Book Description
Data science and analytics have emerged as the most desired fields in driving business decisions. Using the techniques and methods of data science, decision makers can uncover hidden patterns in their data, develop algorithms and models that help improve processes and make key business decisions. Data science is a data driven decision making approach that uses several different areas and disciplines with a purpose of extracting insights and knowledge from structured and unstructured data. The algorithms and models of data science along with machine learning and predictive modeling are widely used in solving business problems and predicting future outcomes. This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative.
Author: Anna Manning Publisher: Apress ISBN: 1484202775 Category : Computers Languages : en Pages : 319
Book Description
This book covers the practical aspects of database design, data cleansing, data analysis, and data protection, among others. The focus is on what you really need to know to create the right database for your small business and to leverage it most effectively to spur growth and revenue. Databases for Small Business is a practical handbook for entrepreneurs, managers, staff, and professionals in small organizations who are not IT specialists but who recognize the need to ramp up their small organizations’ use of data and to round out their own business expertise and office skills with basic database proficiency. Anna Manning—a data scientist who has worked on database design and data analysis in a computer science university research lab, her own small business, and a nonprofit—walks you through the progression of steps that will enable you to extract actionable intelligence and maximum value from your business data in terms of marketing, sales, customer relations, decision making, and business strategy. Dr. Manning illustrates the steps in the book with four running case studies of a small online business, an engineering startup, a small legal firm, and a nonprofit organization. Databases for Small Business teaches non-techie entrepreneurs and professionals how to: Design a small business database from scratch Extract the maximum profit from your data Follow guidance on data protection law Effectively use data collection and data cleansing techniques Train staff to leverage your data
Author: Peter Bruce Publisher: "O'Reilly Media, Inc." ISBN: 1491952911 Category : Computers Languages : en Pages : 322
Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Author: Clark R. Wilson
Author: Clark R. Wilson
Author: Nancy Arthur Floyd
Author: Jon F. Claerbout
Author: Graham J. Williams
Author: Graeme Simsion
Author: Alberto Boschetti
Author: Julio Bolton
Author: Amar Sahay
Author: Anna Manning
Author: Peter Bruce