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Haskell Data Analysis Cookbook
by Nishant ShuklaStep-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.
Mastering Python Scientific Computing
by Hemant Kumar MehtaA complete guide for Python programmers to master scientific computing using Python APIs and tools About This Book * The basics of scientific computing to advanced concepts involving parallel and large scale computation are all covered. * Most of the Python APIs and tools used in scientific computing are discussed in detail * The concepts are discussed with suitable example programs Who This Book Is For If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming. What You Will Learn * Fundamentals and components of scientific computing * Scientific computing data management * Performing numerical computing using NumPy and SciPy * Concepts and programming for symbolic computing using SymPy * Using the plotting library matplotlib for data visualization * Data analysis and visualization using Pandas, matplotlib, and IPython * Performing parallel and high performance computing * Real-life case studies and best practices of scientific computing In Detail In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing. At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python. The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs. Style and approach This book follows a hands-on approach to explain the complex concepts related to scientific computing. It details various APIs using appropriate examples.
Mastering R for Quantitative Finance
by Edina Berlinger Ferenc IllésThis book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.
Mastering Julia
by Malcolm Sherrington<P><P>Key Features <P><P>Build statistical models with linear regression and analysis of variance (ANOVA) <P><P>Create your own modules and contribute to the Julia package system <P><P>Complete an extensive data science project through the entire cycle from ETL to analytics and data visualization <P><P>Book Description <P><P>Julia is a well-constructed programming language with fast execution speed, eliminating the classic problem of performing analysis in one language and translating it for performance into a second. This book will help you develop and enhance your programming skills in Julia to solve real-world automation challenges.This book starts off with a refresher on installing and running Julia on different platforms. Next, you will compare the different ways of working with Julia and explore Julia's key features in-depth by looking at design and build.
Building a Recommendation System with R
by Suresh K. Gorakala Michele UsuelliLearn the art of building robust and powerful recommendation engines using R About This Book * Learn to exploit various data mining techniques * Understand some of the most popular recommendation techniques * This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn * Get to grips with the most important branches of recommendation * Understand various data processing and data mining techniques * Evaluate and optimize the recommendation algorithms * Prepare and structure the data before building models * Discover different recommender systems along with their implementation in R * Explore various evaluation techniques used in recommender systems * Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.
Python Machine Learning
by Sebastian RaschkaUnlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book * Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms * Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn * Explore how to use different machine learning models to ask different questions of your data * Learn how to build neural networks using Pylearn 2 and Theano * Find out how to write clean and elegant Python code that will optimize the strength of your algorithms * Discover how to embed your machine learning model in a web application for increased accessibility * Predict continuous target outcomes using regression analysis * Uncover hidden patterns and structures in data with clustering * Organize data using effective pre-processing techniques * Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Bioinformatics with Python Cookbook
by Tiago AntaoIf you have intermediate-level knowledge of Python and are well aware of the main research and vocabulary in your bioinformatics topic of interest, this book will help you develop your knowledge further.
Teaching Mathematics at Secondary Level
by Tony GardinerTeaching Mathematics is nothing less than a mathematical manifesto. Arising in response to a limited National Curriculum, and engaged with secondary schooling for those aged 11-14 (Key Stage 3) in particular, this handbook for teachers will help them broaden and enrich their students’ mathematical education. It avoids specifying how to teach, and focuses instead on the central principles and concepts that need to be borne in mind by all teachers and textbook authors—but which are little appreciated in the UK at present. <p><p> This study is aimed at anyone who would like to think more deeply about the discipline of ‘elementary mathematics’, in England and Wales and anywhere else. By analysing and supplementing the current curriculum, Teaching Mathematics provides food for thought for all those involved in school mathematics, whether as aspiring teachers or as experienced professionals. It challenges us all to reflect upon what it is that makes secondary school mathematics educationally, culturally, and socially important.
WebRTC Integrator's Guide
by Altanai AltanaiThis book is for programmers who want to learn about real-time communication and utilize the full potential of WebRTC. It is assumed that you have working knowledge of setting up a basic telecom infrastructure as well as basic programming and scripting knowledge.
Mastering pandas
by Femi AnthonyThis book is intended for Python programmers, mathematicians, and analysts who already have a basic understanding of Python and wish to learn about its data analysis capabilities in depth.
Machine Learning with R Cookbook
by Yu-Wei ChiuIf you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.
Learning Predictive Analytics with Python
by Ashish KumarGain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python About This Book * A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices * Get to grips with the basics of Predictive Analytics with Python * Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Who This Book Is For If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite. What You Will Learn * Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries * Analyze the result parameters arising from the implementation of Predictive Analytics algorithms * Write Python modules/functions from scratch to execute segments or the whole of these algorithms * Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms * Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy * Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries * Understand the best practices while handling datasets in Python and creating predictive models out of them In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. Style and approach All the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required.
Learning R for Geospatial Analysis
by Michael DormanThis book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software--a working installation of R is all that is necessary to begin.
Mastering Machine Learning with R
by Cory LeismesterIf you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.
Mastering SciPy
by Francisco J. Blanco-SilvaImplement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stackAbout This BookMaster the theory and algorithms behind numerical recipes and how they can be applied to real-world problemsLearn to combine the most appropriate built-in functions from the SciPy stack by understanding the connection between the sources of your problem, volume of data, or computer architectureA comprehensive coverage of all the mathematical techniques needed to solve the presented topics, with a discussion of the relevant algorithms built in the SciPy stackWho This Book Is ForIf you are a mathematician, engineer, or computer scientist with a proficiency in Python and familiarity with IPython, this is the book for you. Some basic knowledge of numerical methods in scientific computing would be helpful.What You Will LearnMaster relevant algorithms used in symbolic or numerical mathematics to address approximation, interpolation, differentiation, integration, root-finding, and optimization of scalar or multi-variate functionsDevelop different algorithms and strategies to efficiently store and manipulate large matrices of data, in particular to solve systems of linear equations, or compute their eigenvalues/eigenvectorsUnderstand how to model physical problems with systems of differential equations and distinguish the factors that dictate the strategies to solve themPerform statistical analysis, hypothesis test design and resolution, or data mining at a higher level, and apply them to real-life problems in the field of data analysisGain insights on the power of distances, Delaunay triangulations and Voronoi diagrams for Computational Geometry, and apply them to various engineering problemsFamiliarize yourself with different techniques in signal/image processing, including filtering audio, images, or video to extract information, features, or remove componentsIn DetailThe SciPy stack is a collection of open source libraries of the powerful scripting language Python, together with its interactive shells. This environment offers a cutting-edge platform for numerical computation, programming, visualization and publishing, and is used by some of the world's leading mathematicians, scientists, and engineers. It works on any operating system that supports Python and is very easy to install, and completely free of charge! It can effectively transform into a data-processing and system-prototyping environment, directly rivalling MATLAB and Octave.This book goes beyond a mere description of the different built-in functions coded in the libraries from the SciPy stack. It presents you with a solid mathematical and computational background to help you identify the right tools for each problem in scientific computing and visualization. You will gain an insight into the best practices with numerical methods depending on the amount or type of data, properties of the mathematical tools employed, or computer architecture, among other factors.The book kicks off with a concise exploration of the basics of numerical linear algebra and graph theory for the treatment of problems that handle large data sets or matrices. In the subsequent chapters, you will delve into the depths of algorithms in symbolic algebra and numerical analysis to address modeling/simulation of various real-world problems with functions (through interpolation, approximation, or creation of systems of differential equations), and extract their representing features (zeros, extrema, integration or differentiation).Lastly, you will move on to advanced concepts of data analysis, image/signal processing, and computational geometry.Style and approachPacked with real-world examples, this book explores the mathematical techniques needed to solve the presented topics, and focuses on the algorithms built in the SciPy stack.
Gephi Cookbook
by Devangana KhokharIf you want to learn network analysis and visualization along with graph concepts from scratch, then this book is for you. This is ideal for those of you with little or no understanding of Gephi and this domain, but will also be beneficial for those interested in expanding their knowledge and experience.
Learning SciPy for Numerical and Scientific Computing - Second Edition
by Erik A Christensen Sergio J. Rojas G.This book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.
IPython Notebook Essentials
by L. Felipe MartinsIf you are a professional, student, or educator who wants to learn to use IPython Notebook as a tool for technical and scientific computing, visualization, and data analysis, this is the book for you. This book will prove valuable for anyone that needs to do computations in an agile environment.
Getting Started with Python Data Analysis
by Phuong VothihongIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.
Haskell Design Patterns
by Ryan LemmerIf you're a Haskell programmer with a firm grasp of the basics and are ready to move more deeply into modern idiomatic Haskell programming, then this book is for you.
R Graphs Cookbook Second Edition
by Jaynal Abedin Hrishi V. MittalTargeted at those with an existing familiarity with R programming, this practical guide will appeal directly to programmers interested in learning effective data visualization techniques with R and a wide-range of its associated libraries.
Learning Apache Cassandra
by Mat BrownIf you're an application developer familiar with SQL databases such as MySQL or Postgres, and you want to explore distributed databases such as Cassandra, this is the perfect guide for you. Even if you've never worked with a distributed database before, Cassandra's intuitive programming interface coupled with the step-by-step examples in this book will have you building highly scalable persistence layers for your applications in no time.
Predictive Analytics Using Rattle and Qlik Sense
by Ferran Garcia PagansIf you are a business analyst who wants to understand how to improve your data analysis and how to apply predictive analytics, then this book is ideal for you. This book assumes you have some basic knowledge of statistics and a spreadsheet editor such as Excel, but knowledge of QlikView is not required.
Microsoft Dynamics AX 2012 R3 Financial Management
by Mohamed AamerThis book is intended for application consultants, controllers, CFOs, and other professionals who are engaged in a Microsoft Dynamics AX implementation project. Basic knowledge of financial terms, concepts, and Microsoft Dynamics AX terminologies is required.
Microsoft Dynamics CRM Customization Essentials
by Nicolae TarlaIf you are new to Dynamics CRM or a seasoned user looking to enhance your knowledge of the platform, then this book is for you. It is also for skilled developers who are looking to move to the Microsoft stack to build business solution software.