Big data
Rahul Malewar
Informatica PowerCenter is an industry-leading ETL tool, known for its accelerated data extraction, transformation, and data management strategies. This book will be your quick guide to exploring Informatica PowerCenter’s powerful features such as working on sources, targets, transformations, performance optimization, scheduling, deploying for processing, and managing your data at speed. First, you’ll learn how to install and configure tools. You will learn to implement various data warehouse and ETL concepts, and use PowerCenter 10.x components to build mappings, tasks, workflows, and so on. You will come across features such as transformations, SCD, XML processing, partitioning, constraint-based loading, Incremental aggregation, and many more. Moreover, you’ll also learn to deliver powerful visualizations for data profiling using the advanced monitoring dashboard functionality offered by the new version. Using data transformation technique, performance tuning, and the many new advanced features, this book will help you understand and process data for training or production purposes. The step-by-step approach and adoption of real-time scenarios will guide you through effectively accessing all core functionalities offered by Informatica PowerCenter version 10.x.
Scott Gottreu
If you are a developer with a good understanding of JavaScript and jQuery and have been burdened with the task of analyzing and presenting some data, this book will provide you with the start you need to create some very attractive data visualizations.
Learning Jupyter. Select, Share, Interact and Integrate with Jupyter Not
Toomey
Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more.This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode.Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.
Bahaaldine Azarmi
Kibana is an open source data visualization platform that allows you to interact with your data through stunning, powerful graphics. Its simple, browser-based interface enables you to quickly create and share dynamic dashboards that display changes to Elasticsearch queries in real time.In this book, you’ll learn how to use the Elastic stack on top of a data architecture to visualize data in real time. All data architectures have different requirements and expectations when it comes to visualizing the data, whether it’s logging analytics, metrics, business analytics, graph analytics, or scaling them as per your business requirements. This book will help you master Elastic visualization tools and adapt them to the requirements of your project. You will start by learning how to use the basic visualization features of Kibana 5. Then you will be shown how to implement a pure metric analytics architecture and visualize it using Timelion, a very recent and trendy feature of the Elastic stack. You will learn how to correlate data using the brand-new Graph visualization and build relationships between documents. Finally, you will be familiarized with the setup of a Kibana development environment so that you can build a custom Kibana plugin.By the end of this book you will have all the information needed to take your Elastic stack skills to a new level of data visualization.
Leif Larsen Henning Larsen
Take your app development to the next level with Learning Microsoft Cognitive Services. Using Leif's knowledge of each of the powerful APIs, you'll learn how to create smarter apps with more human-like capabilities. ? Discover what each API has to offer and learn how to add it to your app ? Study each AI using theory and practical examples ? Learn current API best practices
Leif Larsen Henning Larsen
Microsoft has revamped its Project Oxford to launch the all new Cognitive Services platform-a set of 30 APIs to add speech, vision, language, and knowledge capabilities to apps.This book will introduce you to 24 of the APIs released as part of Cognitive Services platform and show you how to leverage their capabilities. More importantly, you'll see how the power of these APIs can be combined to build real-world apps that have cognitive capabilities. The book is split into three sections: computer vision, speech recognition and language processing, and knowledge and search.You will be taken through the vision APIs at first as this is very visual, and not too complex. The next part revolves around speech and language, which are somewhat connected. The last part is about adding real-world intelligence to apps by connecting them to Knowledge and Search APIs.By the end of this book, you will be in a position to understand what Microsoft Cognitive Service can offer and how to use the different APIs.
Leif Larsen
Microsoft Cognitive Services is a set of APIs for integrating artificial intelligence in your applications to solve logical business problems. If you’re new to developing applications with AI, Learning Microsoft Cognitive Services will give you a comprehensive introduction to Microsoft’s AI stack and get you up-to-speed in no time.The book introduces you to 24 APIs, including Emotion, Language, Vision, Speech, Knowledge, and Search. Using Visual Studio, you can develop applications with enhanced capabilities for image processing, speech recognition, text processing, and much more. Moving forward, you will work with datasets that enable your applications to process various data in the form of image, video, or text.By the end of the book, you’ll be able to confidently explore Cognitive Services APIs for building intelligent applications that can be deployed for real-world business uses.
Jerome Baton, Rik Van Bruggen
Neo4j is a graph database that allows traversing huge amounts of data with ease. This book aims at quickly getting you started with the popular graph database Neo4j.Starting with a brief introduction to graph theory, this book will show you the advantages of using graph databases along with data modeling techniques for graph databases. You'll gain practical hands-on experience with commonly used and lesser known features for updating graph store with Neo4j's Cypher query language. Furthermore, you'll also learn to create awesome procedures using APOC and extend Neo4j's functionality, enabling integration, algorithmic analysis, and other advanced spatial operation capabilities on data.Through the course of the book you will come across implementation examples on the latest updates in Neo4j, such as in-graph indexes, scaling, performance improvements, visualization, data refactoring techniques, security enhancements, and much more. By the end of the book, you'll have gained the skills to design and implement modern spatial applications, from graphing data to unraveling business capabilities with the help of real-world use cases.
Michael Heydt
If you are a Python programmer who wants to get started with performing data analysis using pandas and Python, this is the book for you. Some experience with statistical analysis would be helpful but is not mandatory.
Learning pandas. High performance data manipulation and analysis using Python - Second Edition
Nicola Rainiero, Sonali Dayal, Michael Heydt
You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance.With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science.
Dan Keeley, Diethard Steiner, María Carina Roldán,...
Pentaho Data Integration(PDI) is an intuitive and graphical environment packed with drag-and-drop design and powerful Extract-Tranform-Load (ETL) capabilities. This book shows and explains the new interactive features of Spoon, the revamped look and feel, and the newest features of the tool including transformations and jobs Executors and the invaluable Metadata Injection capability.We begin with the installation of PDI software and then move on to cover all the key PDI concepts. Each of the chapter introduces new features, enabling you to gradually get practicing with the tool. First, you will learn to do all kind of data manipulation and work with simple plain files. Then, the book teaches you how you can work with relational databases inside PDI. Moreover, you will be given a primer on data warehouse concepts and you will learn how to load data in a data warehouse. During the course of this book, you will be familiarized with its intuitive, graphical and drag-and-drop design environment.By the end of this book, you will learn everything you need to know in order to meet your data manipulation requirements. Besides, your will be given best practices and advises for designing and deploying your projects.
Ashish Kumar
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.
David Bellot
Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We’ll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we’ll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you’ll see the advantage of going probabilistic when you want to do prediction. Next, you’ll master using R packages and implementing its techniques. Finally, you’ll be presented with machine learning applications that have a direct impact in many fields. Here, we’ll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.
Tomasz Drabas, Denny Lee
Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Anita Graser
QGIS is a user-friendly open source geographic information system (GIS) that runs on Linux, Unix, Mac OS X, and Windows. The popularity of open source geographic information systems and QGIS in particular has been growing rapidly over the last few years.Learning QGIS Third Edition is a practical, hands-on guide updated for QGIS 2.14 that provides you with clear, step-by-step exercises to help you apply your GIS knowledge to QGIS. Through clear, practical exercises, this book will introduce you to working with QGIS quickly and painlessly.This book takes you from installing and configuring QGIS to handling spatial data to creating great maps. You will learn how to load and visualize existing spatialdata and create data from scratch. You will get to know important plugins, perform common geoprocessing and spatial analysis tasks and automate them with Processing.We will cover how to achieve great cartographic output and print maps. Finally, you will learn how to extend QGIS using Python and even create your own plugin.
PRASHANT VATS, Dr. Param Jeet
The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language.You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financialmodels in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate.We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging.By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.