In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.Peer under the hood of the systems you already use, and learn how to use and operate them more effectivelyMake informed decisions by identifying the strengths and weaknesses of different toolsNavigate the trade-offs around consistency, scalability, fault tolerance, and complexityUnderstand the distributed systems research upon which modern databases are builtPeek behind the scenes of major online services, and learn from their architectures
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples
Domain-driven design is a well-established approach to designing software that ensures that domain experts and developers work together effectively to create high-quality software. This book is the first to combine DDD with techniques from statically typed functional programming. This book is perfect for newcomers to DDD or functional programming - all the techniques you need will be introduced and explained.
Model a complex domain accurately using the F# type system, creating compilable code that is also readable documentation---ensuring that the code and design never get out of sync. Encode business rules in the design so that you have "compile-time unit tests," and eliminate many potential bugs by making illegal states unrepresentable. Assemble a series of small, testable functions into a complete use case, and compose these individual scenarios into a large-scale design. Discover why the combination of functional programming and DDD leads naturally to service-oriented and hexagonal architectures. Finally, create a functional domain model that works with traditional databases, NoSQL, and event stores, and safely expose your domain via a website or API.
Solve real problems by focusing on real-world requirements for your software.
What You Need:
The code in this book is designed to be run interactively on Windows, Mac and Linux.You will need a recent version of F# (4.0 or greater), and the appropriate .NET runtime for your platform.Full installation instructions for all platforms at fsharp.org.
Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.Examine the foundations of machine learning and neural networksLearn how to train feed-forward neural networksUse TensorFlow to implement your first neural networkManage problems that arise as you begin to make networks deeperBuild neural networks that analyze complex imagesPerform effective dimensionality reduction using autoencodersDive deep into sequence analysis to examine languageLearn the fundamentals of reinforcement learning
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates
By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.
New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.Develop an understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyImport data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data
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 LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn 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 Keras, 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.
Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science.
You’ll learn how to:Automate and schedule data ingest, using an App Engine applicationCreate and populate a dashboard in Google Data StudioBuild a real-time analysis pipeline to carry out streaming analyticsConduct interactive data exploration with Google BigQueryCreate a Bayesian model on a Cloud Dataproc clusterBuild a logistic regression machine-learning model with SparkCompute time-aggregate features with a Cloud Dataflow pipelineCreate a high-performing prediction model with TensorFlowUse your deployed model as a microservice you can access from both batch and real-time pipelines
Over five sections, Sleeper—recognized as a Tableau Zen Master, Tableau Public Visualization of the Year author, and Tableau Iron Viz Champion—provides visualization tips, tutorials, and strategies to help you avoid the pitfalls and take your Tableau knowledge to the next level.
Practical Tableau sections include:Fundamentals: get started with Tableau from the beginningChart types: use step-by-step tutorials to build a variety of charts in TableauTips and tricks: learn innovative uses of parameters, color theory, how to make your Tableau workbooks run efficiently, and moreFramework: explore the INSIGHT framework, a proprietary process for building Tableau dashboardsStorytelling: learn tangible tactics for storytelling with data, including specific and actionable tips you can implement immediately
This is not a book. It is a one-of-a-kind immersive learning experience through which you can become—or teach others to be—a powerful data storyteller.
Let’s practice! helps you build confidence and credibility to create graphs and visualizations that make sense and weave them into action-inspiring stories. Expanding upon best seller storytelling with data’s foundational lessons, Let’s practice! delivers fresh content, a plethora of new examples, and over 100 hands-on exercises. Author and data storytelling maven Cole Nussbaumer Knaflic guides you along the path to hone core skills and become a well-practiced data communicator. Each chapter includes:
● Practice with Cole: exercises based on real-world examples first posed for you to consider and solve, followed by detailed step-by-step illustration and explanation
● Practice on your own: thought-provoking questions and even more exercises to be assigned or worked through individually, without prescribed solutions
● Practice at work: practical guidance and hands-on exercises for applying storytelling with data lessons on the job, including instruction on when and how to solicit useful feedback and refine for greater impact
The lessons and exercises found within this comprehensive guide will empower you to master—or develop in others—data storytelling skills and transition your work from acceptable to exceptional. By investing in these skills for ourselves and our teams, we can all tell inspiring and influential data stories!
Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem.Explore Kafka producers and consumers for writing and reading messagesUnderstand Kafka patterns and use-case requirements to ensure reliable data deliveryGet best practices for building data pipelines and applications with KafkaManage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasksLearn the most critical metrics among Kafka’s operational measurementsExplore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.Use R’s default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing
Tableau is the gold standard of business intelligence and visual analytics tools in every industry. It enables rapid data visualization and interpretation with charts, graphs, dashboards, and much more. Updated with the latest features of Tableau, this book takes you from the foundations of the Tableau 2019 paradigm through to advanced topics.
This third edition of the bestselling guide by Tableau Zen Master, Joshua Milligan, will help you come to grips with updated features, such as set actions and transparent views. Beginning with installation, you'll create your first visualizations with Tableau and then explore practical examples and advanced techniques. You'll create bar charts, tree maps, scatterplots, time series, and a variety of other visualizations. Next, you'll discover techniques to overcome challenges presented by data structure and quality and engage in effective data storytelling and decision making with business critical information. Finally, you'll be introduced to Tableau Prep, and learn how to use it to integrate and shape data for analysis.
By the end of this book, you will be equipped to leverage the powerful features of Tableau 2019 for decision making.What you will learnDevelop stunning visualizations that explain complexity with clarityExplore the exciting new features of Tableau Desktop and Tableau PrepConnect to various data sources to bring all your data togetherUncover techniques to prep and structure your data for easy analysisCreate and use calculations to solve problems and enrich analyticsMaster advanced topics such as sets, LOD calcs, and much moreEnable smart decisions with clustering, distribution, and forecastingShare your data stories to build a culture of trust and actionWho this book is for
This Tableau book is for anyone who wants to understand data. If you’re new to Tableau, don’t worry. This book builds on the foundations to help you understand how Tableau really works and then builds on that knowledge with practical examples before moving on to advanced techniques. Working experience with databases will be useful but is not necessary to get the most out of this book.
You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance.
If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications.
With this book, you will:Familiarize yourself with the Spark programming modelBecome comfortable within the Spark ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public data setsDiscover which machine learning tools make sense for particular problemsAcquire code that can be adapted to many uses
This book covers:Arrays and lists: the most common data structuresStacks and queues: more complex list-like data structuresLinked lists: how they overcome the shortcomings of arraysDictionaries: storing data as key-value pairsHashing: good for quick insertion and retrievalSets: useful for storing unique elements that appear only onceBinary Trees: storing data in a hierarchical mannerGraphs and graph algorithms: ideal for modeling networksAlgorithms: including those that help you sort or search dataAdvanced algorithms: dynamic programming and greedy algorithms
Many businesses launch NoSQL databases without understanding the techniques for using their features most effectively. This book demonstrates the benefits of document embedding, polymorphic schemas, and other MongoDB patterns for tackling specific big data use cases, including:Operational intelligence: Perform real-time analytics of business dataEcommerce: Use MongoDB as a product catalog master or inventory management systemContent management: Learn methods for storing content nodes, binary assets, and discussionsOnline advertising networks: Apply techniques for frequency capping ad impressions, and keyword targeting and biddingSocial networking: Learn how to store a complex social graph, modeled after Google+Online gaming: Provide concurrent access to character and world data for a multiplayer role-playing game
You know right away when you see an effective chart or graphic. It hits you with an immediate sense of its meaning and impact. But what actually makes it clearer, sharper, and more effective? If you're ready to create your own "good charts"--data visualizations that powerfully communicate your ideas and research and that advance your career--the Good Charts Workbook is the hands-on guide you've been looking for.
The original Good Charts changed the landscape by helping readers understand how to think visually and by laying out a process for creating powerful data visualizations. Now, the Good Charts Workbook provides tools, exercises, and practical insights to help people in all kinds of enterprises gain the skills they need to get started.
Harvard Business Review Senior Editor and dataviz expert Scott Berinato leads you, step-by-step, through the key challenges in creating good charts--controlling color, crafting for clarity, choosing chart types, practicing persuasion, capturing concepts--with warm-up exercises and mini-challenges for each. The Workbook includes helpful prompts and reminders throughout, as well as white space for users to practice the Good Charts talk-sketch-prototype process.
Good Charts Workbook is the must-have manual for better understanding the dataviz around you and for creating better charts to make your case more effectively.
Tableau For Dummies brings order to the chaotic world of data. Understanding your data and organizing it into formats and visualizations that make sense to you are crucial to making a real impact on your business with the information that's already at your fingertips. This easy-to-use reference explores the user interface, and guides you through the process of connecting your data sources to the software. Additionally, this approachable, yet comprehensive text shows you how to use graphs, charts, and other images to bring visual interest to your data, how to create dashboards from multiple data sources, and how to export the visualizations that you have developed into multiple formats that translate into positive change for your business.
The mission of Tableau Software is to grant you access to data that, when put into action, will help you build your company. Learning to use the data available to you helps you make informed, grounded business decisions that can spell success for your company.Navigate the user interface to efficiently access the features you need Connect to various spreadsheets, databases, and other data sources to create a multi-dimensional snapshot of your business Develop visualizations with easy to use drag and drop features Start building your data with templates and sample workbooks to spark your creativity and help you organize your information
Tableau For Dummies is a step-by-step resource that helps you make sense of the data landscape—and put your data to work in support of your business.
Through step-by-step tutorials, you’ll learn how to build practical applications and experiences grounded in theory and backed by industry use cases. In each section of the book, industry specialists, including Timoni West, Victor Prisacariu, and Nicolas Meuleau, join the authors to explain the technology behind spatial computing.
In three parts, this book covers:Art and design: Explore spatial computing and design interactions, human-centered interaction and sensory design, and content creation tools for digital artTechnical development: Examine differences between ARKit, ARCore, and spatial mapping-based systems; learn approaches to cross-platform development on head-mounted displaysUse cases: Learn how data and machine learning visualization and AI work in spatial computing, training, sports, health, and other enterprise applications
Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, “sound science.” In A Vast Machine Paul Edwards has news for these skeptics: without models, there are no data. Today, no collection of signals or observations—even from satellites, which can “see” the whole planet with a single instrument—becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere—to measure it, trace its past, and model its future.
On the eve of Google's IPO in 2004, Larry Page and Sergey Brin vowed not to be evil. Today, a growing number of technologists would go further, trying to ensure that their work actively improves people's lives. Technology, so pervasive and ubiquitous, has the capacity to increase stress and suffering; but it also has the less-heralded potential to improve the well-being of individuals, society, and the planet. In this book, Rafael Calvo and Dorian Peters investigate what they term “positive computing”—the design and development of technology to support psychological well-being and human potential.
Calvo and Peters explain that technologists' growing interest in social good is part of a larger public concern about how our digital experience affects our emotions and our quality of life—which itself reflects an emerging focus on humanistic values in many different disciplines. Synthesizing theory, knowledge, and empirical methodologies from a variety of fields, they offer a rigorous and coherent foundational framework for positive computing. Sidebars by experts from psychology, neuroscience, human–computer interaction, and other disciplines supply essential context. Calvo and Peters examine specific well-being factors, including positive emotions, self-awareness, mindfulness, empathy, and compassion, and explore how technology can support these factors. Finally, they offer suggestions for future research and funding.
Timothy N. Bickmore, Jeremy Bailenson, danah boyd, Jane Burns, David R. Caruso, Mihaly Csikszentmihalyi, Felicia Huppert, Mary-Helen Immordino-Yang, Adele Krusche and J. Mark G. Williams, Jane McGonigal, Jonathan Nicholas, Don Norman, Yvonne Rogers
Bradley Holt, co-founder of the creative services firm Found Line, is a web developer and entrepreneur ten years of PHP and MySQL experience. He began using CouchDB before the release of version 1.0. Bradley is an active member of the PHP community, and can be reached at bradley-holt.com.
SQLite is a small, embeddable, SQL-based, relational database management system. It has been widely used in low- to medium-tier database applications, especially in embedded devices. This book provides a comprehensive description of SQLite database system. It describes design principles, engineering trade-offs, implementation issues, and operations of SQLite.
Implementing Splunk Second Edition is a learning guide that introduces you to all the latest features and improvements of Splunk 6.2. The book starts by introducing you to various concepts such as charting, reporting, clustering, and visualization. Every chapter is dedicated to enhancing your knowledge of a specific concept, including data models and pivots, speeding up your queries, backfilling, data replication, and so on. By the end of the book, you'll have a very good understanding of Splunk and be able to perform efficient data analysis.
R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple.
R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze.
Table of Contents
Chapter 1: What is R Programming Language? Introduction & Basics
Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows
Chapter 3: R Data Types, Arithmetic & Logical Operators with Example
Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice
Chapter 5: Factor in R: Categorical & Continuous Variables
Chapter 6: R Data Frame: Create, Append, Select, Subset
Chapter 7: List in R: Create, Select Elements with Example
Chapter 8: R Sort a Data Frame using Order()
Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread)
Chapter 10: Merge Data Frames in R: Full and Partial Match
Chapter 11: Functions in R Programming (with Example)
Chapter 12: IF, ELSE, ELSE IF Statement in R
Chapter 13: For Loop in R with Examples for List and Matrix
Chapter 14: While Loop in R with Example
Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples
Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files
Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm
Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File
Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example
Chapter 20: R Aggregate Function: Summarise & Group_by() Example
Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example
Chapter 22: Scatter Plot in R using ggplot2 (with Example)
Chapter 23: How to make Boxplot in R (with EXAMPLE)
Chapter 24: Bar Chart & Histogram in R (with Example)
Chapter 25: T Test in R: One Sample and Paired (with Example)
Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples)
Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example]
Chapter 28: Decision Tree in R with Example
Chapter 29: R Random Forest Tutorial with Example
Chapter 30: Generalized Linear Model (GLM) in R with Example
Chapter 31: K-means Clustering in R with Example
Chapter 32: R Vs Python: What's the Difference?
Chapter 33: SAS vs R: What's the Difference?
Research shows that visual information is more quickly and easily understood, and much more likely to be remembered. This innovative book presents the design process and the best software tools for creating infographics that communicate. Including a special section on how to construct the increasingly popular infographic resume, the book offers graphic designers, marketers, and business professionals vital information on the most effective ways to present data.Explains why infographics and data visualizations work Shares the tools and techniques for creating great infographics Covers online infographics used for marketing, including social media and search engine optimization (SEO) Shows how to market your skills with a visual, infographic resume Explores the many internal business uses of infographics, including board meeting presentations, annual reports, consumer research statistics, marketing strategies, business plans, and visual explanations of products and services to your customers
With Cool Infographics, you'll learn to create infographics to successfully reach your target audience and tell clear stories with your data.
Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
If you are a competent developer with experience of working with data structure servers and want to boost your project's performance by learning about features of Redis, then this book is for you.What You Will LearnBuild analytics applications using Bitmaps and HyperloglogsEnhance scalability with Twemproxy, Redis Sentinel, and Redis ClusterBuild a Time Series implementation in Node.js and RedisCreate your own Redis commands by extending Redis with LuaGet to know security techniques to protect your data (SSL encryption, firewall rules, basic authorization)Persist data to disk and learn the trade-offs of AOF and RDBUnderstand how to use Node.js, PHP, Python, and Ruby clients for RedisAvoid common pitfalls when designing your next solutionIn Detail
Redis is the most popular in-memory key-value data store. It's very lightweight and its data types give it an edge over the other competitors. If you need an in-memory database or a high-performance cache system that is simple to use and highly scalable, Redis is what you need.
Redis Essentials is a fast-paced guide that teaches the fundamentals on data types, explains how to manage data through commands, and shares experiences from big players in the industry.
We start off by explaining the basics of Redis followed by the various data types such as Strings, hashes, lists, and more. Next, Common pitfalls for various scenarios are described, followed by solutions to ensure you do not fall into common traps.
After this, major differences between client implementations in PHP, Python, and Ruby are presented. Next, you will learn how to extend Redis with Lua, get to know security techniques such as basic authorization, firewall rules, and SSL encryption, and discover how to use Twemproxy, Redis Sentinel, and Redis Cluster to scale infrastructures horizontally. At the end of this book, you will be able to utilize all the essential features of Redis to optimize your project's performance.Style and approach
A practical guide that offers the foundation upon which you can begin to understand the capabilities of Redis using a step-by-step approach. This book is full of real-world problems and in-depth knowledge of the concepts and features of Redis, with plenty of examples.
This book is for aspiring data professionals who want to learn and implement self-service analytics from scratch, without any coding. Those who have some experience with Alteryx and want to gain more proficiency will also find this book to be useful. A basic understanding of the data science concepts is all you need to get started with this book.What You Will LearnCreate efficient workflows with Alteryx to answer complex business questionsLearn how to speed up the cleansing, data preparing, and shaping processBlend and join data into a single dataset for self-service analysisWrite advanced expressions in Alteryx leading to an optimal workflow for efficient processing of huge dataDevelop high-quality, data-driven reports to improve consistency in reporting and analysisExplore the flexibility of macros by automating analytic processesApply predictive analytics from spatial, demographic, and behavioral analysis and quickly publish, scheduleShare your workflows and insights with relevant stakeholdersIn Detail
Alteryx, as a leading data blending and advanced data analytics platform, has taken self-service data analytics to the next level. Companies worldwide often find themselves struggling to prepare and blend massive datasets that are time-consuming for analysts. Alteryx solves these problems with a repeatable workflow designed to quickly clean, prepare, blend, and join your data in a seamless manner.
This book will set you on a self-service data analytics journey that will help you create efficient workflows using Alteryx, without any coding involved. It will empower you and your organization to take well-informed decisions with the help of deeper business insights from the data.Starting with the fundamentals of using Alteryx such as data preparation and blending, you will delve into the more advanced concepts such as performing predictive analytics. You will also learn how to use Alteryx's features to share the insights gained with the relevant decision makers. To ensure consistency, we will be using data from the Healthcare domain throughout this book.
The knowledge you gain from this book will guide you to solve real-life problems related to Business Intelligence confidently. Whether you are a novice with Alteryx or an experienced data analyst keen to explore Alteryx's self-service analytics features, this book will be the perfect companion for you.Style and approach
Comprehensive, step by step guide filled with real-world examples to step through the complex business questions using one of the leading data analytics platform.
People keep track. In the eighteenth century, Benjamin Franklin kept charts of time spent and virtues lived up to. Today, people use technology to self-track: hours slept, steps taken, calories consumed, medications administered. Ninety million wearable sensors were shipped in 2014 to help us gather data about our lives. This book examines how people record, analyze, and reflect on this data, looking at the tools they use and the communities they become part of. Gina Neff and Dawn Nafus describe what happens when people turn their everyday experience—in particular, health and wellness-related experience—into data, and offer an introduction to the essential ideas and key challenges of using these technologies. They consider self-tracking as a social and cultural phenomenon, describing not only the use of data as a kind of mirror of the self but also how this enables people to connect to, and learn from, others.
Neff and Nafus consider what's at stake: who wants our data and why; the practices of serious self-tracking enthusiasts; the design of commercial self-tracking technology; and how self-tracking can fill gaps in the healthcare system. Today, no one can lead an entirely untracked life. Neff and Nafus show us how to use data in a way that empowers and educates.
Knowledge flow- A mobile learning platform provides Apps and Books.
Knowledge flow provides learning book of Data Structures and Algorithms. This book is for all information technology and computer science students and professionals across the world. Data structure is the programmatic way of storing data and this book of data structure provides an easy way to understanding data structure.
1. Introduction to Data Structures and Algorithms
2. Data structure for string and pattern matching Algorithm
3. Arrays and Pointers
4. Linked Lists
5. Stacks and Queues
7. Graphs in Data Structure
8. Sort and Search
9. AVL Search Trees
10. Warshall’s Algorithm
After a brief history of Python and key differences between Python 2 and Python 3, you'll understand how Python has been used in applications such as YouTube and Google App Engine. As you work with the language, you'll learn about control statements, delve into controlling program flow and gradually work on more structured programs via functions.
As you settle into the Python ecosystem, you'll learn about data structures and study ways to correctly store and represent information. By working through specific examples, you'll learn how Python implements object-oriented programming (OOP) concepts of abstraction, encapsulation of data, inheritance, and polymorphism. You'll be given an overview of how imports, modules, and packages work in Python, how you can handle errors to prevent apps from crashing, as well as file manipulation.
By the end of this book, you'll have built up an impressive portfolio of projects and armed yourself with the skills you need to tackle Python projects in the real world.What you will learnUse control statementsManipulate primitive and non-primitive data structuresUse loops to iterate over objects or data for accurate resultsWrite encapsulated and succinct Python functionsBuild Python classes using object-oriented programmingManipulate files on the file system (open, read, write, and delete)Who this book is for
Python Fundamentals is great for anyone who wants to start using Python to build anything from simple command-line programs to web applications. Prior knowledge of Python isn't required.
If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.What You Will LearnLearn about Apache Spark and the Spark 2.0 architectureBuild and interact with Spark DataFrames using Spark SQLLearn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectivelyRead, transform, and understand data and use it to train machine learning modelsBuild machine learning models with MLlib and MLLearn how to submit your applications programmatically using spark-submitDeploy locally built applications to a clusterIn Detail
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.Style and approach
This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.
Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess.
Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.