Название: MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203
Автор: Benjamin Perkins
Издательство: Sybex
Год: 2023
Страниц: 1008
Язык: английский
Формат: epub (true)
Размер: 84.9 MB

Prepare for the Azure Data Engineering certification―and an exciting new career in analytics―with this must-have study aide In the MCA Microsoft Certified Associate Azure Data Engineer Study Exam DP-203 , accomplished data engineer and tech educator Benjamin Perkins delivers a hands-on, practical guide to preparing for the challenging Azure Data Engineer certification and for a new career in an exciting and growing field of tech. In the book, you’ll explore all the objectives covered on the DP-203 exam while learning the job roles and responsibilities of a newly minted Azure data engineer. From integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions, you’ll get up to speed quickly and efficiently with Sybex’s easy-to-use study aids and tools. This Study Guide also A one-of-a-kind study aid designed to help you get straight to the crucial material you need to succeed on the exam and on the job, the MCA Microsoft Certified Associate Azure Data Engineer Study Exam DP-203 belongs on the bookshelves of anyone hoping to increase their data analytics skills, advance their data engineering career with an in-demand certification, or hoping to make a career change into a popular new area of tech.

Название: Data in Context: Models as Enablers for Managing and Using Data
Автор: Bas van Gils
Издательство: Springer
Год: 2023
Страниц: 220
Язык: английский
Формат: pdf (true)
Размер: 10.0 MB

This book is about data fundamentals. The author describes in an accessible manner the fundamentals of what data is, the difference between information and data, and the pragmatic application of abstract data modeling techniques in the day-to-day business. The data management techniques are described both from a scientific, theoretical perspective and from an a practical, application-related perspective. The theoretical concepts are illustrated with concrete examples. This combination of theory and practical application provides an own unique signature to this data management book, which distinguishes it from many other books. The field of Data Science is extensive and pulls together several disciplines as I will demonstrate shortly. A full account of what Data Science is, how it works, and all the techniques that are involved is beyond the scope of this chapter. Several definitions of Data Science are currently in use.

Название: Modern Business Analytics: Practical Data Science for Decision-making
Автор: Matt Taddy, Leslie Hendrix, Matthew C. Harding
Издательство: McGraw Hill
Год: 2023
Страниц: 465
Язык: английский
Формат: pdf (true)
Размер: 34.6 MB

Written by Matt Taddy, successful author of the McGraw Hill Professional title, Business Data Science graduate of University of Chicago and Amazon Chief Economist. This new higher-ed text takes a practical, modern approach to Data Science and business analytics for the graduate-level business analytics student or professional. It takes a learn-by-doing approach, with real data analysis examples that explain the "why", rather than the "what" in the decision-making discussions. It uses R as the primary technology throughout the text and includes an end-of-chapter reference to the basic R recipes in each chapter. The text uses tools from economics and statistics in combination with Machine Learning Techniques to create a platform for using data to make decisions. The practice of data analytics is changing and modernizing. Innovations in computation and Machine Learning are creating new opportunities for the data analyst: exposing previously unexplored data to scientific analysis, scaling tasks through automation, and allowing deeper and more accurate modeling. Spreadsheet models and pivot tables are being replaced by code scripts in languages like R and Python. There has been massive growth in digitized information, accompanied by development of systems for storage and analysis of this data. There has also been an intellectual convergence across fields - Machine Learning and Computer Science, statistics, and social sciences and economics - that has raised the breadth and quality of applied analysis everywhere. This is the Data Science approach to analytics, and it allows leaders to go deeper than ever to understand their operations, products, and customers.

Название: Mastering SQL: A Beginner's Guide
Автор: Sufyan bin Uzayr
Издательство: CRC Press
Год: 2024
Страниц: 269
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Mastering SQL helps readers gain a firm understanding of the Structured Query Language. Structured Query Language, more often known as SQL, is the de facto standard language for working with databases. It is a specialized language for handling data-related tasks like creating a database, putting information into tables, modifying and extracting that information, and much more. MySQL, PostgreSQL, Oracle, SQL light, etc. are only a few examples of SQL implementations. Structured Query Language (SQL) is a query language for working with relational databases. We can use SQL to update, delete, and retrieve data from a database. A table is used to define the data/object. These tables consist of rows and columns and are uniquely identified by their field names. SQL is a fast and efficient database system. SQL allows for the rapid and efficient retrieval of huge numbers of data entries from a database. It’s a relational database. Thus, data is described in a more orderly fashion than in an unstructured database like MongoDB. Insertions, deletions, inquiries, manipulations, and computations of data through analytical queries in a relational database may all be performed in a matter of seconds.

Название: Delta Lake: Up and Running: Modern Data Lakehouse Architectures with Delta Lake (5th Early Release)
Автор: Bennie Haelen, Dan Davis
Издательство: O’Reilly Media, Inc.
Год: 2023-07-25
Страниц: 157
Язык: английский
Формат: epub
Размер: 10.2 MB

With the rapid growth of big data and AI, organizations are quickly building data products and solutions in an ad-hoc manner. But as these data organizations mature, it's apparent that their analysis and Machine Learning (ML) models are only as reliable as the data they're built upon. The solution? Delta Lake, an open-source format that enables building a lakehouse architecture on top of existing storage systems such as S3, ADLS, and GCS. In this practical book, author Bennie Haelen shows data engineers, data scientists, and data analysts how to get Delta Lake and its unique features up and running. The ultimate goal of building data pipelines and applications is to query processed data and gain insights from it. You'll learn how the choice of storage solution determines the robustness and performance of the data pipeline, from raw data to insights. Delta Lake brings capabilities such as transactional reliability and support for UPSERTs and MERGEs to data lakes while maintaining the dynamic horizontal scalability and separation of storage and compute of data lakes. Delta Lake is one the enablers for building Data lakehouses, an open data architecture that combines the best of data warehouses and data lakes.

Название: Statistical Models and Methods for Data Science
Автор: Leonardo Grilli, Monia Lupparelli, Carla Rampichini
Издательство: Springer
Год: 2023
Страниц: 186
Язык: английский
Формат: pdf (true), epub
Размер: 18.0 MB

This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with Data Science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk. Analyzing categorical data in Machine Learning generally requires a coding strategy. This problem is common to multivariate statistical techniques, and several approaches have been suggested in the literature. This article proposes a method for analyzing categorical variables with neural networks. Both a supervised and unsupervised approaches were considered, in which the variables can have high cardinality. Some simulated data applications illustrate the interest in the proposal.

Название: Secure Data Mining
Автор: Jocelyn O. Padallan
Издательство: Arcler Press
Год: 2022
Страниц: 244
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Data mining is a process to extract useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. However, privacy concerns may prevent people from sharing the data and some types of information about the data. How we conduct data mining without breaching data privacy presents a challenge. Secure Data Mining provides solutions to the problem of data mining without compromising data privacy. This professional book is designed for practitioners and researchers in industry, as well as a secondary textbook for advanced-level students in Computer Science. Fundamentals and basic concepts regarding data mining are given in Chapter 1 which include data types, information gained from the data, and usefulness of the data mined. Chapter 2 provides detailed knowledge about the security of the data in the process of data mining. A number of approaches of security including classification and detection of data, clustering of data, intrusion detection systems etc. are discussed in this chapter. Classification approaches of the data are discussed in Chapter 3 of this book. Categorization of data and categorization techniques, preprocessing of data and feature selection are the presented in this chapter. Chapter 4 discusses the application of secure data mining in fraud detection. This chapter gives overview of the existing fraud detection systems and compares it with the secure system of fraud detection.

Название: Data Fabric and Data Mesh Approaches with AI: A Guide to AI-based Data Cataloging, Governance, Integration, Orchestration, and Consumption
Автор: Eberhard Hechler, Maryela Weihrauch, Yan (Catherine) Wu
Издательство: Apress
Год: 2023
Страниц: 440
Язык: английский
Формат: pdf (true), epub
Размер: 41.6 MB

Understand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance—all designed to deliver "data as a product" within hybrid cloud landscapes. This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses Artificial Intelligence (AI) and Machine Learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.

Название: Think Like a Data Analyst (MEAP v3)
Автор: Mona Khalil
Издательство: Manning Publications
Год: 2023
Страниц: 207
Язык: английский
Формат: pdf, epub
Размер: 17.9 MB

Learn the technical and soft skills you need to succeed in your career as a data analyst. Think Like a Data Analyst is full of sage advice on how to be an effective data analyst in a real production environment. Inside, you’ll find methods that maximize the impact of your work, from choosing the right analysis approach to effectively communicating with stakeholders. You’ll soon understand the nuances and challenges of real data science projects, with the kind of insights that only come from years of experience. Without doubt, technical skills in Python, R, SQL, along with knowledge of statistics and data science are vital to your success as an analyst. However, they’re only part of the picture. This one-of-a-kind guide reveals the soft skills, best practices, and tools that help you maximize your effectiveness and deliver accurate data-driven decisions in your organization.

Название: The Way of data: From Technology to Applications
Автор: Wu Hequan, Zheng Weimin
Издательство: World Scientific Publishing
Год: 2023
Страниц: 200
Язык: английский
Формат: pdf (true)
Размер: 18.9 MB

Everyone is already in the era of Big Data. It is gradually changing people's lifestyles. Therefore, it is necessary to explore the development path of Big Data, balance the relationship between technology, policy, and the market, so that it can better serve human society.This comprehensive book introduces what Big Data is, Big Data processing systems, Big Data management technologies, and Big Data analysis methods in an easy-to-understand language. It explains the specific applications of Big Data in smart government affairs, economic development, and the improvement of people's livelihood and welfare. Big Data, as described by Gartner, the world’s leading IT research and advisory firm, is a massive, high-growth, and diverse information asset that requires new processing models to provide greater decision-making, insight discovery, and process optimization.