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Practical Cryptography: Algorithms and Implementations Using C++
by Saiful Azad Al-Sakib Khan PathanCryptography, the science of encoding and decoding information, allows people to do online banking, online trading, and make online purchases, without worrying that their personal information is being compromised. The dramatic increase of information transmitted electronically has led to an increased reliance on cryptography. This book discusses th
Practical Cyber Forensics: An Incident-Based Approach to Forensic Investigations
by Niranjan ReddyBecome an effective cyber forensics investigator and gain a collection of practical, efficient techniques to get the job done. Diving straight into a discussion of anti-forensic techniques, this book shows you the many ways to effectively detect them. Now that you know what you are looking for, you’ll shift your focus to network forensics, where you cover the various tools available to make your network forensics process less complicated. Following this, you will work with cloud and mobile forensic techniques by considering the concept of forensics as a service (FaSS), giving you cutting-edge skills that will future-proof your career.Building on this, you will learn the process of breaking down malware attacks, web attacks, and email scams with case studies to give you a clearer view of the techniques to be followed. Another tricky technique is SSD forensics, so the author covers this in detail to give you the alternative analysis techniques you’ll need. To keep you up to speed on contemporary forensics, Practical Cyber Forensics includes a chapter on Bitcoin forensics, where key crypto-currency forensic techniques will be shared. Finally, you will see how to prepare accurate investigative reports. What You Will LearnCarry out forensic investigation on Windows, Linux, and macOS systemsDetect and counter anti-forensic techniques Deploy network, cloud, and mobile forensicsInvestigate web and malware attacksWrite efficient investigative reportsWho This Book Is ForIntermediate infosec professionals looking for a practical approach to investigative cyber forensics techniques.
Practical Cyber Intelligence: A Hands-on Guide to Digital Forensics
by Adam Tilmar JakobsenOverview of the latest techniques and practices used in digital forensics and how to apply them to the investigative process Practical Cyber Intelligence provides a thorough and practical introduction to the different tactics, techniques, and procedures that exist in the field of cyber investigation and cyber forensics to collect, preserve, and analyze digital evidence, enabling readers to understand the digital landscape and analyze legacy devices, current models, and models that may be created in the future. Readers will learn how to determine what evidence exists and how to find it on a device, as well as what story it tells about the activities on the device. Over 100 images and tables are included to aid in reader comprehension, and case studies are included at the end of the book to elucidate core concepts throughout the text. To get the most value from this book, readers should be familiar with how a computer operates (e.g., CPU, RAM, and disk), be comfortable interacting with both Windows and Linux operating systems as well as Bash and PowerShell commands and have a basic understanding of Python and how to execute Python scripts. Practical Cyber Intelligence includes detailed information on: OSINT, the method of using a device’s information to find clues and link a digital avatar to a person, with information on search engines, profiling, and infrastructure mappingWindow forensics, covering the Windows registry, shell items, the event log and much more Mobile forensics, understanding the difference between Android and iOS and where key evidence can be found on the device Focusing on methodology that is accessible to everyone without any special tools, Practical Cyber Intelligence is an essential introduction to the topic for all professionals looking to enter or advance in the field of cyber investigation, including cyber security practitioners and analysts and law enforcement agents who handle digital evidence.
Practical Cyber Intelligence: How action-based intelligence can be an effective response to incidents
by Wilson BautistaYour one stop solution to implement a Cyber Defense Intelligence program in to your organisation.Key FeaturesIntelligence processes and procedures for response mechanismsMaster F3EAD to drive processes based on intelligenceThreat modeling and intelligent frameworksCase studies and how to go about building intelligent teamsBook DescriptionCyber intelligence is the missing link between your cyber defense operation teams, threat intelligence, and IT operations to provide your organization with a full spectrum of defensive capabilities. This book kicks off with the need for cyber intelligence and why it is required in terms of a defensive framework.Moving forward, the book provides a practical explanation of the F3EAD protocol with the help of examples. Furthermore, we learn how to go about threat models and intelligence products/frameworks and apply them to real-life scenarios. Based on the discussion with the prospective author I would also love to explore the induction of a tool to enhance the marketing feature and functionality of the book.By the end of this book, you will be able to boot up an intelligence program in your organization based on the operation and tactical/strategic spheres of Cyber defense intelligence.What you will learn Learn about the Observe-Orient-Decide-Act (OODA) loop and it's applicability to security Understand tactical view of Active defense concepts and their application in today's threat landscape Get acquainted with an operational view of the F3EAD process to drive decision making within an organization Create a Framework and Capability Maturity Model that integrates inputs and outputs from key functions in an information security organization Understand the idea of communicating with the Potential for Exploitability based on cyber intelligenceWho this book is forThis book targets incident managers, malware analysts, reverse engineers, digital forensics specialists, and intelligence analysts; experience in, or knowledge of, security operations, incident responses or investigations is desirable so you can make the most of the subjects presented.
Practical Cybersecurity Architecture: A guide to creating and implementing robust designs for cybersecurity architects
by Ed Moyle Diana KelleyPlan and design robust security architectures to secure your organization's technology landscape and the applications you developKey FeaturesLeverage practical use cases to successfully architect complex security structuresLearn risk assessment methodologies for the cloud, networks, and connected devicesUnderstand cybersecurity architecture to implement effective solutions in medium-to-large enterprisesBook DescriptionCybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization.With this book, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs.By the end of this book, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.What you will learnExplore ways to create your own architectures and analyze those from othersUnderstand strategies for creating architectures for environments and applicationsDiscover approaches to documentation using repeatable approaches and toolsDelve into communication techniques for designs, goals, and requirementsFocus on implementation strategies for designs that help reduce riskBecome well-versed with methods to apply architectural discipline to your organizationWho this book is forIf you are involved in the process of implementing, planning, operating, or maintaining cybersecurity in an organization, then this security book is for you. This includes security practitioners, technology governance practitioners, systems auditors, and software developers invested in keeping their organizations secure. If you're new to cybersecurity architecture, the book takes you through the process step by step; for those who already work in the field and have some experience, the book presents strategies and techniques that will help them develop their skills further.
Practical D3.js
by Tarek Amr Rayna StamboliyskaPractical D3. js is your indispensable guide to mastering the efficient use of this exciting JavaScript data visualization library. You will learn what data visualization is, how to work with it, and how to think like a D3. js expert, both practically and theoretically. This book does not just show you how to use D3. js, it teaches you how to think like a data scientist and work with the data in the real world. You will learn how to get the data, how to clean and refine it, and how to display it in the best charts and layouts. Uniquely, this book intertwines the technical details of D3. js with practical topics such as data journalism and the use of open government data. Written by leading data scientists Tarek Amr and Rayna Stamboliyska, Practical D3. js is your indispensable guide to using D3. js in the real world - add it to your library today. What you'll learn How to think like a data scientist and present data in the best way What structure and design strategies you can use for compelling data visualization How to use data binding, animations and events, scales, and color pickers How to use shapes, path generators, arcs and polygons Who this book is for This book is for anyone who wants to learn to master the use of D3. js in a practical manner, while still learning the important theoretical aspects needed to enable them to work with their data in the best possible way.
Practical Data Analysis
by Hector CuestaEach chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis.Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle.
Practical Data Analysis - Second Edition
by Hector Cuesta Dr Sampath KumarA practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book * Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data * Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images * A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn * Acquire, format, and visualize your data * Build an image-similarity search engine * Generate meaningful visualizations anyone can understand * Get started with analyzing social network graphs * Find out how to implement sentiment text analysis * Install data analysis tools such as Pandas, MongoDB, and Apache Spark * Get to grips with Apache Spark * Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.
Practical Data Analysis Cookbook
by Tomasz DrabasOver 60 practical recipes on data exploration and analysis About This Book * Clean dirty data, extract accurate information, and explore the relationships between variables * Forecast the output of an electric plant and the water flow of American rivers using pandas, NumPy, Statsmodels, and scikit-learn * Find and extract the most important features from your dataset using the most efficient Python libraries Who This Book Is For If you are a beginner or intermediate-level professional who is looking to solve your day-to-day, analytical problems with Python, this book is for you. Even with no prior programming and data analytics experience, you will be able to finish each recipe and learn while doing so. What You Will Learn * Read, clean, transform, and store your data usng Pandas and OpenRefine * Understand your data and explore the relationships between variables using Pandas and D3.js * Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels * Reduce the dimensionality of your dataset and extract the most important features with pandas, NumPy, and mlpy * Predict the output of a power plant with regression models and forecast water flow of American rivers with time series methods using pandas, NumPy, Statsmodels, and scikit-learn * Explore social interactions and identify fraudulent activities with graph theory concepts using NetworkX and Gephi * Scrape Internet web pages using urlib and BeautifulSoup and get to know natural language processing techniques to classify movies ratings using NLTK * Study simulation techniques in an example of a gas station with agent-based modeling In Detail Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data--arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer. Style and approach This hands-on recipe guide is divided into three sections that tackle and overcome real-world data modeling problems faced by data analysts/scientist in their everyday work. Each independent recipe is written in an easy-to-follow and step-by-step fashion.
Practical Data Analysis Using Jupyter Notebook: Learn how to speak the language of data by extracting useful and actionable insights using Python
by Marc Wintjen Andrew VlahutinUnderstand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key Features Find out how to use Python code to extract insights from data using real-world examples Work with structured data and free text sources to answer questions and add value using data Perform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing data Book Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learn Understand the importance of data literacy and how to communicate effectively using data Find out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysis Wrangle data and create DataFrames using pandas Produce charts and data visualizations using time-series datasets Discover relationships and how to join data together using SQL Use NLP techniques to work with unstructured data to create sentiment analysis models Discover patterns in real-world datasets that provide accurate insights Who this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
Practical Data Analysis and Reporting with BIRT
by John WardThis book is a concise and practical guide aimed at getting the results you want as quickly as possible. It steers the reader through each point of reporting from setup, to scripting, designing, formatting, and deploying BIRT reports using a common example that runs through the book. This book is for Java developers who want to create rich reports and get started with BIRT to do this. Readers will need a basic understanding of SQL to follow along.
Practical Data Mining
by Jr., Monte HancockUsed by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in tech
Practical Data Mining Techniques and Applications
by Dr Ketan ShahData mining techniques and algorithms are extensively used to build real-world applications. A practical approach can be applied to data mining techniques to build applications. Once deployed, an application enables the developers to work on the users’ goals and mold the algorithms with respect to users’ perspectives. Practical Data Mining Techniques and Applications focuses on various concepts related to data mining and how these techniques can be used to develop and deploy applications. The book provides a systematic composition of fundamental concepts of data mining blended with practical applications. The aim of this book is to provide access to practical data mining applications and techniques to help readers gain an understanding of data mining in practice. Readers also learn how relevant techniques and algorithms are applied to solve problems and to provide solutions to real-world applications in different domains. This book can help academicians to extend their knowledge of the field as well as their understanding of applications based on different techniques to gain greater insight. It can also help researchers with real-world applications by diving deeper into the domain. Computing science students, application developers, and business professionals may also benefit from this examination of applied data science techniques. By highlighting an overall picture of the field, introducing various mining techniques, and focusing on different applications and research directions using these methods, this book can motivate discussions among academics, researchers, professionals, and students to exchange and develop their views regarding the dynamic field that is data mining.
Practical Data Privacy: Enhancing Privacy And Security In Data
by Katharine JarmulBetween major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.Practical Data Privacy answers important questions such as:What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases?What does "anonymized data" really mean? How do I actually anonymize data?How does federated learning and analysis work?Homomorphic encryption sounds great, but is it ready for use?How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help?How do I ensure that my data science projects are secure by default and private by design?How do I work with governance and infosec teams to implement internal policies appropriately?
Practical Data Science
by Andreas François VermeulenLearn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.What You'll LearnBecome fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteriaMaster the methods for retrieving actionable business knowledgeCoordinate the handling of polyglot data types in a data lake for repeatable resultsWho This Book Is ForData scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
Practical Data Science Cookbook
by Sean Patrick Murphy Tony OjedaIf you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of data science projects, the steps in the data science pipeline, and the programming examples presented in this book. Since the book is formatted to walk you through the projects with examples and explanations along the way, no prior programming experience is required.
Practical Data Science Cookbook - Second Edition
by Sean Patrick Murphy Tony Ojeda Benjamin Bengfort Abhijit Dasgupta Prabhanjan TattarOver 85 recipes to help you complete real-world data science projects in R and Python About This Book • Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data • Get beyond the theory and implement real-world projects in data science using R and Python • Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn • Learn and understand the installation procedure and environment required for R and Python on various platforms • Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python • Build a predictive model and an exploratory model • Analyze the results of your model and create reports on the acquired data • Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
Practical Data Science with Python 3: Synthesizing Actionable Insights from Data
by Ervin VargaGain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data science practicesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3.
Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data
by Nathan GeorgeLearn to effectively manage data and execute data science projects from start to finish using PythonKey FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor's, Master's, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Practical Data Science with R
by Nina Zumel John Mount"Practical Data Science with R" lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Practical Data Science with R, Second Edition
by Nina Zumel John MountSummary Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You&’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you&’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you&’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the reader You&’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the author Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science.
Practical Data Science with SAP: Machine Learning Techniques for Enterprise Data
by Greg Foss Paul ModdermanLearn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data.Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths.You'll explore:Examples of how data analysis can help you solve several SAP challengesNatural language processing for unlocking the secrets in textData science techniques for data clustering and segmentationMethods for detecting anomalies in your SAP dataData visualization techniques for making your data come to life
Practical Data Security (Routledge Revivals)
by John GordonFirst published in 1993, this volume emerged in response to the genesis of the Internet and provides early considerations on issues including computer viruses, cyber security and network encryption management, with a particular focus on applying risk analysis to the data security of financial institutions. With the stage set by the UK Data Protection Act of 1984 and the Computer Misuse Act of 1990, this volume provides a series of useful contributions for large companies and home PCs and provides a clear introduction setting out the context and the relevant terminology.
Practical DataOps: Delivering Agile Data Science at Scale
by Harvinder AtwalGain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making.Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles.This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goalsRecognize and eliminate barriers to delivering data to users at scaleWork on the right things for the right stakeholders through agile collaborationCreate trust in data via rigorous testing and effective data managementBuild a culture of learning and continuous improvement through monitoring deployments and measuring outcomesCreate cross-functional self-organizing teams focused on goals not reporting linesBuild robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data productsWho This Book Is ForData science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.
Practical Database Auditing for Microsoft SQL Server and Azure SQL: Troubleshooting, Regulatory Compliance, and Governance
by Josephine BushKnow how to track changes and key events in your SQL Server databases in support of application troubleshooting, regulatory compliance, and governance. This book shows how to use key features in SQL Server ,such as SQL Server Audit and Extended Events, to track schema changes, permission changes, and changes to your data. You’ll even learn how to track queries run against specific tables in a database. Not all changes and events can be captured and tracked using SQL Server Audit and Extended Events, and the book goes beyond those features to also show what can be captured using common criteria compliance, change data capture, temporal tables, or querying the SQL Server log. You will learn how to audit just what you need to audit, and how to audit pretty much anything that happens on a SQL Server instance. This book will also help you set up cloud auditing with an emphasis on Azure SQL Database, Azure SQL Managed Instance, and AWS RDS SQL Server. You don’t need expensive, third-party auditing tools to make auditing work for you, and to demonstrate and provide value back to your business. This book will help you set up an auditing solution that works for you and your needs. It shows how to collect the audit data that you need, centralize that data for easy reporting, and generate audit reports using built-in SQL Server functionality for use by your own team, developers, and organization’s auditors. What You Will LearnUnderstand why auditing is important for troubleshooting, compliance, and governanceTrack changes and key events using SQL Server Audit and Extended EventsTrack SQL Server configuration changes for governance and troubleshootingUtilize change data capture and temporal tables to track data changes in SQL Server tablesCentralize auditing data from all your databases for easy querying and reportingConfigure auditing on Azure SQL, Azure SQL Managed Instance, and AWS RDS SQL Server Who This Book Is ForDatabase administrators who need to know what’s changing on their database servers, and those who are making the changes; database-savvy DevOps engineers and developers who are charged with troubleshooting processes and applications; developers and administrators who are responsible for generating reports in support of regulatory compliance reporting and auditing