Big data
Miguel Angel Garcia, Barry Harmsen
Business Intelligence technologies are a must-have in every business to make informed decisions and keep up-to speed with the ever-evolving markets. QlikView's disruptive technology makes it a key player and leader in the industry; with its flexibility and powerful analytics environment, building QlikView apps can be mastered by both, business users as well as developers.This book will help you learn QlikView Development from a basic to a practitioner level using a step-by-step approach in a practical environment, and apply proven best practices on each topic.Throughout the book, we will build a QlikView app based on real data about Airline Operations that will help HighCloud Airlines make informed business decisions and analysis-guided strategies. HighCloud Airlines executives are evaluating if entering the US market is a good strategy and, if so, which line of business should they focus their investments on; they need QlikView to make the best decision.The application will be evolving chapter by chapter, along with your skills, going from a simple proof of concept to creating a Data Model, adding a custom style, building a Dashboard and handling and manipulating the source data via script. We will meet the HighCloud Airlines requirement by using many different data visualization objects and time-saving techniques.The whole application uses real data taken from the Bureau of Transportations statistics of the US and encompasses the operations of Airlines both domestic and international. With three years worth of data, you will help HighCloud Airlines discover where people travel the most, which are the Carriers with the most market share, what is the average load factor per airline, which aircraft is the most used to perform flights, which are the busiest airports, and a whole universe of new insights.
Miguel Angel Garcia, Barry Harmsen, Stephen Redmond,...
QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations.Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources.With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro.This Learning Path includes content from the following Packt products:• QlikView for Developers by Miguel Ángel García, Barry Harmsen • Mastering QlikView by Stephen Redmond • Mastering QlikView Data Visualization by Karl Pover
Stephen Redmond
QlikView has been around since 1993, but has only really taken off in recent years as a leader in the in-memory BI space and, more recently, in the data discovery area. QlikView features the ability to consolidate relevant data from multiple sources into a single application, as well as an associative data model to allow you to explore the data to a way your brain works, state-of-the-art visualizations, dashboard, analysis and reports, and mobile data access.QlikView for Developers Cookbook builds on your initial training and experiences with QlikView to help you become a better developer. This book features plenty of hands-on examples of many challenging functions.Assuming a basic understanding of QlikView development, this book provides a range of step-by-step exercises to teach you different subjects to help build your QlikView developer expertise.From advanced charting and layout to set analysis; from advanced aggregations through to scripting, performance, and security, this book will cover all the areas that you need to know about.The recipes in this book will give you a lot of the information that you need to become an excellent QlikView developer.
Diane B Blackwood
This book is an effective step-by-step tutoring guide for financial analysis using Qlikview.It begins by teaching you the crucial concepts of Qlikview Finance to help you develop an effective understanding of financial data analysis and finance. The book then goes on to cover real-world, practical examples on the use of Qlikview for financial planning and analysis, expense management, risk management, and more. Moving on, topics such as Asset Management QlikView Dashboard and Retail Sales Analysis are covered in a strategic way. We then shift the focus to deal with the concepts of Inventory, Supply Chain, and Plant Coverage Dashboards. The book then reaches its conclusion by dealing with ways to share your QlikView insights. By the end of this book, you will have a good understanding of how to use Qlikview for numerous applications in finance.
Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
Antoine Jacquier, Oleksiy Kondratyev
With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware.Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware.This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm.This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun!
Dan MacLean
Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples.This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
Dan MacLean
The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools.This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses.By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.
R: Data Analysis and Visualization. Master the art of building analytical models using R
Tony Fischetti, Brett Lantz, Hrishi V. Mittal,...
The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility.The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework.With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs.The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions.Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on.
Kuntal Ganguly, Davor Lozić, Mzabalazo Z. Ngwenya,...
Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data.This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way.By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.
R Data Analysis Projects. Build end to end analytics systems to get deeper insights from your data
Mark Hodnett, Gopi Subramanian
R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle.You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.By the end of this book, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.
R Data Mining. Implement data mining techniques through practical use cases and real-world datasets
Enrico Pegoraro, Andrea Cirillo
R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
R Data Science Essentials. R Data Science Essentials
Raja B. Koushik, Sharan Kumar Ravindran
With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Atmajitsinh Gohil
If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.
Vitor Bianchi Lanzetta
R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions.This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using ShinyBy the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.
R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet
PKS Prakash, Achyutuni Sri Krishna Rao
Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Mark Hodnett, Joshua F. Wiley
Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem.This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
R Deep Learning Projects. Master the techniques to design and develop neural network models in R
Yuxi (Hayden) Liu, Pablo Maldonado
R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains.This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.
R Deep Learning Projects. Master the techniques to design and develop neural network models in R
Yuxi (Hayden) Liu, Pablo Maldonado
R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains.This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.
Prabhanjan Narayanachar Tattar, Yu-Wei, Chiu (David Chiu)
This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
This book is targeted at R programmers who want to learn the graphing capabilities of R. This book will presume that you have working knowledge of R.
Jaynal Abedin, Hrishi V. Mittal
Targeted 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.
Raghav Bali
Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Michele Usuelli
If your company is like most, the number one reason that projects have failed over the years don’t have to do with technology. They have to do with people. People didn’t like the new technology. People weren’t trained properly on the change. People hadn’t received adequate communications and didn’t understand the change. Sound familiar?
Dr. Sunil Kumar Chinnamgari
R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
R: Mining spatial, text, web, and social media data. Create and customize data mining algorithms
Richard Heimann, Nathan H. Danneman, Pradeepta Mishra,...
Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects.After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Learning Data Mining with R by Bater Makhabel ? R Data Mining Blueprints by Pradeepta Mishra? Social Media Mining with R by Nathan Danneman and Richard Heimann