Data Science And Engineering Workspace

  data science and engineering workspace: Guerrilla Analytics Enda Ridge, 2014-09-25 Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. In this book, you will learn about: The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting. Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny. Practice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research. Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions. Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects - The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting - Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny - Practice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research - Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions - Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects
  data science and engineering workspace: Data Engineering and Data Science Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, M. Niranjanamurthy, 2023-08-29 DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
  data science and engineering workspace: Mastering Data Engineering and Analytics with Databricks Manoj Kumar, 2024-09-30 TAGLINE Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges KEY FEATURES ● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow. ● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action. ● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines. ● Offers proven strategies to optimize workflows and avoid common pitfalls. DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. WHAT WILL YOU LEARN ● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases. ● Optimize query performance and efficiently manage cloud resources for cost-effective data processing. ● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation. ● Build and deploy real-time data processing solutions for timely and actionable insights. ● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. WHO IS THIS BOOK FOR? This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content. TABLE OF CONTENTS SECTION 1 1. Introducing Data Engineering with Databricks 2. Setting Up a Databricks Environment for Data Engineering 3. Working with Databricks Utilities and Clusters SECTION 2 4. Extracting and Loading Data Using Databricks 5. Transforming Data with Databricks 6. Handling Streaming Data with Databricks 7. Creating Delta Live Tables 8. Data Partitioning and Shuffling 9. Performance Tuning and Best Practices 10. Workflow Management 11. Databricks SQL Warehouse 12. Data Storage and Unity Catalog 13. Monitoring Databricks Clusters and Jobs 14. Production Deployment Strategies 15. Maintaining Data Pipelines in Production 16. Managing Data Security and Governance 17. Real-World Data Engineering Use Cases with Databricks 18. AI and ML Essentials 19. Integrating Databricks with External Tools Index
  data science and engineering workspace: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
  data science and engineering workspace: Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020-07-16 Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow
  data science and engineering workspace: Data Science Strategy For Dummies Ulrika Jägare, 2019-07-11 All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.
  data science and engineering workspace: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
  data science and engineering workspace: Azure Cookbook Reza Salehi, 2022-10-10 How do you deal with the problems you face when using Azure? This practical guide provides over 75 recipes to help you to work with common Azure issues in everyday scenarios. That includes key tasks like setting up permissions for a storage account, working with Cosmos DB APIs, managing Azure role-based access control, governing your Azure subscriptions using Azure Policy, and much more. Author Reza Salehi has assembled real-world recipes that enable you to grasp key Azure services and concepts quickly. Each recipe includes CLI scripts that you can execute in your own Azure account. Recipes also explain the approach and provide meaningful context. The solutions in this cookbook will take you beyond theory and help you understand Azure services in practice. You'll find recipes that let you: Store data in an Azure storage account or in a data lake Work with relational and nonrelational databases in Azure Manage role-based access control (RBAC) for Azure resources Safeguard secrets in Azure Key Vault Govern your Azure subscription using Azure Policy Use CLI code to construct your application or fix a particular problem
  data science and engineering workspace: Optimizing Databricks Workloads Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi, 2021-12-24 Accelerate computations and make the most of your data effectively and efficiently on Databricks Key FeaturesUnderstand Spark optimizations for big data workloads and maximizing performanceBuild efficient big data engineering pipelines with Databricks and Delta LakeEfficiently manage Spark clusters for big data processingBook Description Databricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud. In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains. By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently. What you will learnGet to grips with Spark fundamentals and the Databricks platformProcess big data using the Spark DataFrame API with Delta LakeAnalyze data using graph processing in DatabricksUse MLflow to manage machine learning life cycles in DatabricksFind out how to choose the right cluster configuration for your workloadsExplore file compaction and clustering methods to tune Delta tablesDiscover advanced optimization techniques to speed up Spark jobsWho this book is for This book is for data engineers, data scientists, and cloud architects who have working knowledge of Spark/Databricks and some basic understanding of data engineering principles. Readers will need to have a working knowledge of Python, and some experience of SQL in PySpark and Spark SQL is beneficial.
  data science and engineering workspace: Data Science and Machine Learning for Non-Programmers Dothang Truong, 2024-02-23 As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
  data science and engineering workspace: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
  data science and engineering workspace: Exam Prep AZ-305 Lalit Rawat, 2024-07-24 DESCRIPTION “Exam Prep AZ-305: Designing Microsoft Azure Infrastructure Solutions” book is a comprehensive guide for IT professionals preparing for the Microsoft Azure AZ-305 certification exam. This book offers detailed insights into designing scalable, secure, and resilient infrastructure solutions on the Azure platform, aligning with the latest exam objectives. It covers critical topics such as designing governance, security, storage, and networking solutions, ensuring readers have the necessary knowledge to architect effective Azure solutions. Through a blend of theoretical concepts and practical exercises, this guide equips readers with the skills needed to apply Azure best practices in real-world scenarios. Each chapter covers specific areas of infrastructure design, providing step-by-step instructions, expert tips, and real-life examples to illustrate complex concepts. This practical approach not only helps in mastering the exam content but also enhances the reader's ability to solve real-world challenges in their job roles. It not only prepares you for certification but also empowers you to design and implement robust Azure infrastructure solutions, thereby enhancing your capabilities and career prospects in the evolving field of cloud technology. KEY FEATURES ● Expertise in Azure networking, storage, compute, identity management, monitoring, security, hybrid cloud solutions, and disaster recovery. ● Learn to design and implement robust Azure infrastructure solutions. ● Prepare for the AZ-305 Azure Infrastructure Architect certification exam. ● Utilize up-to-date Microsoft AZ-305 curriculum. WHAT YOU WILL LEARN ● Master Azure governance principles. ● Design secure authentication and authorization solutions. ● Architect scalable compute solutions on Azure. ● Implement effective data storage and integration strategies. ● Design robust backup and disaster recovery solutions. ● Learn key migration strategies for transitioning to Azure. WHO THIS BOOK IS FOR Whether you are an aspiring cloud architect, a seasoned IT professional, or someone looking to advance their career in cloud computing, this book serves as an essential resource. TABLE OF CONTENTS 1. Designing Governance 2. Designing Authentication and Authorization Solutions 3. Designing a Solution Monitor of Azure Resources 4. Designing an Azure Compute Solution 5. Designing a Data Storage Solution for Non-relational Data 6. Designing Data Integration 7. Designing Data Storage Solutions for Relational Data 8. Designing Network Solutions 9. Designing a Solution for Backup and Disaster Recovery 10. Designing Migration 11. Azure Well-Architected Framework 12. Exam Preparation Guidelines and Assessment Questions 13. Azure Architect Exam Mock Test
  data science and engineering workspace: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
  data science and engineering workspace: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
  data science and engineering workspace: Data Engineering with Scala and Spark Eric Tome, Rupam Bhattacharjee, David Radford, 2024-01-31 Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key Features Transform data into a clean and trusted source of information for your organization using Scala Build streaming and batch-processing pipelines with step-by-step explanations Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD) Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learn Set up your development environment to build pipelines in Scala Get to grips with polymorphic functions, type parameterization, and Scala implicits Use Spark DataFrames, Datasets, and Spark SQL with Scala Read and write data to object stores Profile and clean your data using Deequ Performance tune your data pipelines using Scala Who this book is for This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.
  data science and engineering workspace: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
  data science and engineering workspace: Ultimate Data Engineering with Databricks Mayank Malhotra, 2024-02-14 Navigating Databricks with Ease for Unparalleled Data Engineering Insights. KEY FEATURES ● Navigate Databricks with a seamless progression from fundamental principles to advanced engineering techniques. ● Gain hands-on experience with real-world examples, ensuring immediate relevance and practicality. ● Discover expert insights and best practices for refining your data engineering skills and achieving superior results with Databricks. DESCRIPTION Ultimate Data Engineering with Databricks is a comprehensive handbook meticulously designed for professionals aiming to enhance their data engineering skills through Databricks. Bridging the gap between foundational and advanced knowledge, this book employs a step-by-step approach with detailed explanations suitable for beginners and experienced practitioners alike. Focused on practical applications, the book employs real-world examples and scenarios to teach how to construct, optimize, and maintain robust data pipelines. Emphasizing immediate applicability, it equips readers to address real data challenges using Databricks effectively. The goal is not just understanding Databricks but mastering it to offer tangible solutions. Beyond technical skills, the book imparts best practices and expert tips derived from industry experience, aiding readers in avoiding common pitfalls and adopting strategies for optimal data engineering solutions. This book will help you develop the skills needed to make impactful contributions to organizations, enhancing your value as data engineering professionals in today's competitive job market. WHAT WILL YOU LEARN ● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines. ● Design and implement high-performance data solutions for scalability. ● Apply essential best practices for ensuring data integrity in pipelines. ● Explore advanced Databricks features for tackling complex data tasks. ● Learn to optimize data pipelines for streamlined workflows. WHO IS THIS BOOK FOR? This book caters to a diverse audience, including data engineers, data architects, BI analysts, data scientists and technology enthusiasts. Suitable for both professionals and students, the book appeals to those eager to master Databricks and stay at the forefront of data engineering trends. A basic understanding of data engineering concepts and familiarity with cloud computing will enhance the learning experience. TABLE OF CONTENTS 1. Fundamentals of Data Engineering 2. Mastering Delta Tables in Databricks 3. Data Ingestion and Extraction 4. Data Transformation and ETL Processes 5. Data Quality and Validation 6. Data Modeling and Storage 7. Data Orchestration and Workflow Management 8. Performance Tuning and Optimization 9. Scalability and Deployment Considerations 10. Data Security and Governance Last Words Index
  data science and engineering workspace: Azure Data Scientist Associate Certification Guide Andreas Botsikas, Michael Hlobil, 2021-12-03 Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease Key FeaturesCreate end-to-end machine learning training pipelines, with or without codeTrack experiment progress using the cloud-based MLflow-compatible process of Azure ML servicesOperationalize your machine learning models by creating batch and real-time endpointsBook Description The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam. What you will learnCreate a working environment for data science workloads on AzureRun data experiments using Azure Machine Learning servicesCreate training and inference pipelines using the designer or codeDiscover the best model for your dataset using Automated MLUse hyperparameter tuning to optimize trained modelsDeploy, use, and monitor models in productionInterpret the predictions of a trained modelWho this book is for This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily.
  data science and engineering workspace: Microsoft Azure Essentials Azure Machine Learning Jeff Barnes, 2015-04-25 Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.
  data science and engineering workspace: Work Space, Equipment and Tool Design A. Mital, W. Karwowski, 1991-03-05 As the ergonomic aspect of many problems facing the industry today attracts more attention from the management, providing scientific knowledge and the know-how to solve such problems is becoming increasingly more important. The impetus for this book originated from the pressing need to make the state-of-the-art ergonomic information on workspace, equipment and tool design available to practising ergonomists, safety specialists, engineering designers, and business and technical managers. The book reinforces the notion that ergonomic data should be explicitly integrated in the design of a system, and should become an indispensable part of the overall design process in production engineering, on an equal basis with such activities as mechanical component design, quality assurance, maintenance, inspection, etc. The focus is on selected ergonomic data for workspace, equipment and tool design, with special emphasis on the practical aspects of applying the available information to specific problem areas.
  data science and engineering workspace: Mastering Large Language Models with Python Raj Arun R, 2024-04-12 A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index
  data science and engineering workspace: LLM Prompt Engineering for Developers Aymen El Amri, 2024-05-23 Explore the dynamic field of LLM prompt engineering with this book. Starting with fundamental NLP principles & progressing to sophisticated prompt engineering methods, this book serves as the perfect comprehensive guide. Key Features In-depth coverage of prompt engineering from basics to advanced techniques. Insights into cutting-edge methods like AutoCoT and transfer learning. Comprehensive resource sections including prompt databases and tools. Book DescriptionLLM Prompt Engineering For Developers begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering. As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology. This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.What you will learn Understand the principles of NLP and their application in LLMs. Set up and configure environments for developing with LLMs. Implement few-shot and zero-shot learning techniques. Enhance LLM outputs through AutoCoT and self-consistency methods. Apply transfer learning to adapt LLMs to new domains. Develop practical skills in testing & scoring prompt effectiveness. Who this book is for The target audience for LLM Prompt Engineering For Developers includes software developers, AI enthusiasts, technical team leads, advanced computer science students, and AI researchers with a basic understanding of artificial intelligence. Ideal for those looking to deepen their expertise in large language models and prompt engineering, this book serves as a practical guide for integrating advanced AI-driven projects and research into various workflows, assuming some foundational programming knowledge and familiarity with AI concepts.
  data science and engineering workspace: Machine Learning and Big Data Analytics Rajiv Misra, Rana Omer, Muttukrishnan Rajarajan, Bharadwaj Veeravalli, Nishtha Kesswani, Priyanka Mishra, 2023-06-06 This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2022) is intended to be used as a reference book for researchers and professionals to share their research and reports of new technologies and applications in Machine Learning and Big Data Analytics like biometric Recognition Systems, medical diagnosis, industries, telecommunications, AI Petri Nets Model-Based Diagnosis, gaming, stock trading, Intelligent Aerospace Systems, robot control, law, remote sensing and scientific discovery agents and multiagent systems; and natural language and Web intelligence. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the advanced Scientific Technologies, provide a correlation of multidisciplinary areas and become a point of great interest for Data Scientists, systems architects, developers, new researchers and graduate level students. This volume provides cutting-edge research from around the globe on this field. Current status, trends, future directions, opportunities, etc. are discussed, making it friendly for beginners and young researchers.
  data science and engineering workspace: Business Intelligence with Databricks SQL Vihag Gupta, 2022-09-16 Master critical skills needed to deploy and use Databricks SQL and elevate your BI from the warehouse to the lakehouse with confidence Key FeaturesLearn about business intelligence on the lakehouse with features and functions of Databricks SQLMake the most of Databricks SQL by getting to grips with the enablers of its data warehousing capabilitiesA unique approach to teaching concepts and techniques with follow-along scenarios on real datasetsBook Description In this new era of data platform system design, data lakes and data warehouses are giving way to the lakehouse – a new type of data platform system that aims to unify all data analytics into a single platform. Databricks, with its Databricks SQL product suite, is the hottest lakehouse platform out there, harnessing the power of Apache Spark™, Delta Lake, and other innovations to enable data warehousing capabilities on the lakehouse with data lake economics. This book is a comprehensive hands-on guide that helps you explore all the advanced features, use cases, and technology components of Databricks SQL. You'll start with the lakehouse architecture fundamentals and understand how Databricks SQL fits into it. The book then shows you how to use the platform, from exploring data, executing queries, building reports, and using dashboards through to learning the administrative aspects of the lakehouse – data security, governance, and management of the computational power of the lakehouse. You'll also delve into the core technology enablers of Databricks SQL – Delta Lake and Photon. Finally, you'll get hands-on with advanced SQL commands for ingesting data and maintaining the lakehouse. By the end of this book, you'll have mastered Databricks SQL and be able to deploy and deliver fast, scalable business intelligence on the lakehouse. What you will learnUnderstand how Databricks SQL fits into the Databricks Lakehouse PlatformPerform everyday analytics with Databricks SQL Workbench and business intelligence toolsOrganize and catalog your data assetsProgram the data security model to protect and govern your dataTune SQL warehouses (computing clusters) for optimal query experienceTune the Delta Lake storage format for maximum query performanceDeliver extreme performance with the Photon query execution engineImplement advanced data ingestion patterns with Databricks SQLWho this book is for This book is for business intelligence practitioners, data warehouse administrators, and data engineers who are new to Databrick SQL and want to learn how to deliver high-quality insights unhindered by the scale of data or infrastructure. This book is also for anyone looking to study the advanced technologies that power Databricks SQL. Basic knowledge of data warehouses, SQL-based analytics, and ETL processes is recommended to effectively learn the concepts introduced in this book and appreciate the innovation behind the platform.
  data science and engineering workspace: Cloud Data Science: Harnessing Azure Machine Learning with Python Peter Jones, 2024-10-15 Unlock the full potential of your data with Cloud Data Science: Harnessing Azure Machine Learning with Python. This comprehensive guide equips you with the knowledge and skills to leverage the power of Azure Machine Learning and the versatility of Python to innovate and streamline your machine learning workflows. From setting up your Azure Machine Learning workspace to deploying sophisticated models, this book covers essential techniques and advanced methodologies in a clear, practical format. Dive into core topics such as data management, automated machine learning workflows, model optimization, and real-time monitoring to ensure your projects are scalable, efficient, and effective. Whether you're a data scientist, machine learning engineer, or a professional seeking to enhance your understanding of cloud-based machine learning, this book offers invaluable insights and hands-on examples to help you transform vast amounts of data into actionable insights. Explore real-world case studies across various industries, learn to overcome common challenges, and discover best practices for implementing machine learning projects successfully. Cloud Data Science: Harnessing Azure Machine Learning with Python is your gateway to mastering data science in the cloud and advancing your professional capabilities in the future of technology.
  data science and engineering workspace: Trends in Applied Knowledge-Based Systems and Data Science Hamido Fujita, Moonis Ali, Ali Selamat, Jun Sasaki, Masaki Kurematsu, 2016-07-13 This book constitutes the refereed conference proceedings of the 29th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, held in Morioka, Japan, in August 2-4, 2016. The 80 revised full papers presented were carefully reviewed and selected from 168 submissions. They are organized in topical sections: data science; knowledge base systems; natural language processing and sentiment analysis; semantic Web and social networks; computer vision; medical diagnosis system and bio-informatics; applied neural networks; innovations in intelligent systems and applications; decision support systems; adaptive control; soft computing and multi-agent systems; evolutionary algorithms and heuristic search; system integration for real-life applications.
  data science and engineering workspace: Learn Microsoft Fabric Arshad Ali, Bradley Schacht, 2024-02-29 Harness the power of Microsoft Fabric to develop data analytics solutions for various use cases guided by step-by-step instructions Key Features Explore Microsoft Fabric and its features through real-world examples Build data analytics solutions for lakehouses, data warehouses, real-time analytics, and data science Monitor, manage, and administer your Fabric platform and analytics system to ensure flexibility, performance, security, and control Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover the capabilities of Microsoft Fabric, the premier unified solution designed for the AI era, seamlessly combining data integration, OneLake, transformation, visualization, universal security, and a unified business model. This book provides an overview of Microsoft Fabric, its components, and the wider analytics landscape. In this book, you'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You’ll learn how to build end-to-end lakehouse and data warehouse solutions using the medallion architecture, unlock the real-time analytics, and implement machine learning and AI models. As you progress, you’ll build expertise in monitoring workloads and administering Fabric across tenants, capacities, and workspaces. The book also guides you step by step through enhancing security and governance practices in Microsoft Fabric and implementing CI/CD workflows with Azure DevOps or GitHub. Finally, you’ll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey. By the end of this book, you’ll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric.What you will learn Get acquainted with the different services available in Microsoft Fabric Build end-to-end data analytics solution to scale and manage high performance Integrate data from different types of data sources Apply transformation with Spark, Notebook, and T-SQL Understand and implement real-time stream processing and data science capabilities Perform end-to-end processes for building data analytics solutions in the AI era Drive insights by leveraging Power BI for reporting and visualization Improve productivity with AI assistance and Copilot integration Who this book is for This book is for data professionals, including data analysts, data engineers, data scientists, data warehouse developers, ETL developers, business analysts, AI/ML professionals, software developers, and Chief Data Officers who want to build a future-ready data analytics solution for long-term success in the AI era. For PySpark and SQL students entering the data analytics field, this book offers a broad foundation for developing the skills to build end-to-end analytics systems for various use cases. Basic knowledge of SQL and Spark is assumed.
  data science and engineering workspace: Simplify Big Data Analytics with Amazon EMR Sakti Mishra, 2022-03-25 Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key FeaturesBuild data pipelines that require distributed processing capabilities on a large volume of dataDiscover the security features of EMR such as data protection and granular permission managementExplore best practices and optimization techniques for building data analytics solutions in Amazon EMRBook Description Amazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learnExplore Amazon EMR features, architecture, Hadoop interfaces, and EMR StudioConfigure, deploy, and orchestrate Hadoop or Spark jobs in productionImplement the security, data governance, and monitoring capabilities of EMRBuild applications for batch and real-time streaming data analytics solutionsPerform interactive development with a persistent EMR cluster and NotebookOrchestrate an EMR Spark job using AWS Step Functions and Apache AirflowWho this book is for This book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
  data science and engineering workspace: Hands-On Data Science with R Vitor Bianchi Lanzetta, Nataraj Dasgupta, Ricardo Anjoleto Farias, 2018-11-30 A hands-on guide for professionals to perform various data science tasks in R Key FeaturesExplore the popular R packages for data scienceUse R for efficient data mining, text analytics and feature engineeringBecome a thorough data science professional with the help of hands-on examples and use-cases in RBook Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learnUnderstand the R programming language and its ecosystem of packages for data scienceObtain and clean your data before processingMaster essential exploratory techniques for summarizing dataExamine various machine learning prediction, modelsExplore the H2O analytics platform in R for deep learningApply data mining techniques to available datasetsWork with interactive visualization packages in RIntegrate R with Spark and Hadoop for large-scale data analyticsWho this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course
  data science and engineering workspace: Mastering Azure Synapse Analytics , 2023-04-15 A practical guide that will help you transform your data into actionable insights with Azure Synapse Analytics KEY FEATURES ● Explore the different features in the Azure Synapse Analytics workspace. ● Learn how to integrate Power BI and Data Governance capabilities with Azure Synapse Analytics. ● Accelerate your analytics journey with the no-code/low-code capabilities of Azure Synapse. DESCRIPTION Cloud analytics is a crucial aspect of any digital transformation initiative, and the capabilities of the Azure Synapse analytics platform can simplify and streamline this process. By mastering Azure Synapse Analytics, analytics developers across organizations can boost their productivity by utilizing low-code, no-code, and traditional code-based analytics frameworks. This book starts with a comprehensive introduction to Azure Synapse Analytics and its limitless cloud-scale analytics capabilities. You will then learn how to explore and work with data warehousing features in Azure Synapse. Moving on, the book will guide you on how to effectively use Synapse Spark for data engineering and data science. It will help you learn how to gain insights from your data through Observational analytics using Synapse Data Explorer. You will also discover the seamless data integration capabilities of Synapse Pipeline, and delve into the benefits of Synapse Analytics' low-code and no-code pipeline development features. Lastly the book will show you how to create network topology and implement industry-specific architecture patterns in Azure Synapse Analytics. By the end of the book, you will be able to process and analyze vast amounts of data in real-time to gain insights quickly and make informed decisions. WHAT YOU WILL LEARN ● Leverage Synapse Spark for machine learning tasks. ● Use Synapse Data Explorer for telemetry analysis. ● Take advantage of Synapse's common data model-based database templates. ● Query data using T-SQL, KQL, and Spark SQL within Synapse. ● Integrate Microsoft Purview with Synapse for enhanced data governance. WHO THIS BOOK IS FOR This book is designed for Cloud data engineers with prior experience in Azure cloud computing, as well as Chief Data Officers (CDOs) and Data professionals, who want to use this unified platform for data ingestion, data warehousing, and big data analytics. TABLE OF CONTENTS 1. Cloud Analytics Concept 2. Introduction to Azure Synapse Analytics 3. Modern Data Warehouse with the Synapse SQL Pool 4. Query as a Service- Synapse Serverless SQL 5. Synapse Spark Pool Capability 6. Synapse Spark and Data Science 7. Learning Synapse Data Explorer 8. Synapse Data Integration 9. Synapse Link for HTAP 10. Azure Synapse -Unified Analytics Service 11. Synapse Workspace Ecosystem Integration 12. Azure Synapse Network Topology 13. Industry Cloud Analytics
  data science and engineering workspace: Proceedings of Data Analytics and Management Deepak Gupta, Zdzislaw Polkowski, Ashish Khanna, Siddhartha Bhattacharyya, Oscar Castillo, 2022-01-04 This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2021), held at Jan Wyzykowski University, Poland, during June 2021. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students.
  data science and engineering workspace: Data Science Projects with Python Stephen Klosterman, 2021-07-29 Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost Key FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook Description If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects. You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data. What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is for Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.
  data science and engineering workspace: Data Analytics & Visualization All-in-One For Dummies Jack A. Hyman, Luca Massaron, Paul McFedries, John Paul Mueller, Lillian Pierson, Jonathan Reichental, Joseph Schmuller, Alan R. Simon, Allen G. Taylor, 2024-03-05 Install data analytics into your brain with this comprehensive introduction Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sources Organize and analyze data Use data to tell a story with Tableau Expand your know-how with Python and R New and novice data analysts will love this All-in-One reference on how to make sense of data. Get ready to watch as your career in data takes off.
  data science and engineering workspace: Learn Python by Building Data Science Applications Philipp Kats, David Katz, 2019-08-30 Understand the constructs of the Python programming language and use them to build data science projects Key FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.
  data science and engineering workspace: Perspectives on Data Science for Software Engineering Tim Menzies, Laurie Williams, Thomas Zimmermann, 2016-07-14 Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community's leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. - Presents the wisdom of community experts, derived from a summit on software analytics - Provides contributed chapters that share discrete ideas and technique from the trenches - Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data - Presented in clear chapters designed to be applicable across many domains
  data science and engineering workspace: DATABRICKS SERVICE GUIDE Diego Rodrigues, 2024-10-16 Discover the power of data analysis and machine learning with the DATABRICKS SERVICES GUIDE: From Fundamentals to Practical Applications. This book is an essential reference for data engineers, data scientists, and developers seeking to master the Databricks platform, one of the most advanced solutions for big data and artificial intelligence. Written by Diego Rodrigues, an internationally recognized author with vast experience in technology, this guide offers a comprehensive view of the main services of Databricks. From initial setup to advanced solutions implementation, each chapter is designed to provide clear and detailed instructions, enabling you to immediately apply the knowledge acquired in your projects. The DATABRICKS SERVICES GUIDE covers fundamental topics such as Databricks Workspace, Delta Lake, Data Engineering, Machine Learning, and much more. This book is ideal for both beginners who seek a solid foundation and experienced professionals who want to deepen their skills and explore the advanced capabilities of Databricks. This guide has been designed to be a practical and accessible tool, facilitating the understanding of concepts and the application of best practices in production environments. With practical examples and a structured approach, you will be ready to face technological challenges and implement scalable and secure solutions with Databricks. Tags: Databricks big data machine learning engineering Delta Lake processing analysis Apache Spark notebooks clusters integration pipelines automation cloud storage security data compliance GDPR lgpd engineering transformation SQL real-time API data governance data orchestration data integration Power BI Tableau CI/CD cluster management performance monitoring logs data optimization WAF Databricks File System DBFS cloud computing data science Python Scala R artificial intelligence machine learning workflow scalability efficiency encryption automation DevOps S3 Lambda Glue Kafka Kubernetes Hadoop continuous integration continuous delivery security compliance AWS Microsoft Azure Google IBM Alibaba Diego Rodrigues
  data science and engineering workspace: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
  data science and engineering workspace: Exam Ref DP-600 Implementing Analytics Solutions Using Microsoft Fabric Daniil Maslyuk, Johnny Winter, Štěpán Rešl, 2024-08-31 Prepare for Microsoft Exam DP-600 and demonstrate your real-world mastery of Microsoft Fabric analytics solution development. Designed for working Fabric analytics engineers, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified Solutions Associate level. Focus on the expertise measured by these objectives: Plan, implement, and manage a solution for data analytics Prepare and serve data Implement and manage semantic models Explore and analyze data This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you want to validate your ability to design and build diverse Microsoft Fabric analytics solutions About the Exam Exam DP-600 focuses on knowledge needed to implement and plan a data analytics environment; manage an analytics development lifecycle; create objects in a lakehouse or warehouse; copy data, transform data, and optimize performance; implement, design, and build semantic models; optimize enterprise-scale semantic models; perform exploratory analytics; query data by using SQL. About Microsoft Certification Passing this exam fulfills your requirements for Microsoft Certified: Fabric Analytics Engineer Associate credential, demonstrating your readiness to design, build, test, and maintain Microsoft Fabric data analytics solutions, and partner with other professionals and clients to implement them. See full details at: microsoft.com/learn
  data science and engineering workspace: eWork and eBusiness in Architecture, Engineering and Construction Z. Turk, R. Scherer, 2002-01-01 This is a comprehensive review of research related to construction informatics, with a particular focus on the related 5th framework EU projects on product and process technology and the implementation of the new economy technologies and business models in the construction industry.
  data science and engineering workspace: Azure Data Engineer Associate Certification Guide Giacinto Palmieri, Surendra Mettapalli, Newton Alex, 2024-05-23 Achieve Azure Data Engineer Associate certification success with this DP-203 exam guide Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, and exam tips, and the eBook PDF Key Features Prepare for the DP-203 exam with expert insights, real-world examples, and practice resources Gain up-to-date skills to thrive in the dynamic world of cloud data engineering Build secure and sustainable data solutions using Azure services Book DescriptionOne of the top global cloud providers, Azure offers extensive data hosting and processing services, driving widespread cloud adoption and creating a high demand for skilled data engineers. The Azure Data Engineer Associate (DP-203) certification is a vital credential, demonstrating your proficiency as an Azure data engineer to prospective employers. This comprehensive exam guide is designed for both beginners and seasoned professionals, aligned with the latest DP-203 certification exam, to help you pass the exam on your first try. The book provides a foundational understanding of IaaS, PaaS, and SaaS, starting with core concepts like virtual machines (VMs), VNETS, and App Services and progressing to advanced topics such as data storage, processing, and security. What sets this exam guide apart is its hands-on approach, seamlessly integrating theory with practice through real-world examples, practical exercises, and insights into Azure's evolving ecosystem. Additionally, you'll unlock lifetime access to supplementary practice material on an online platform, including mock exams, interactive flashcards, and exam tips, ensuring a comprehensive exam prep experience. By the end of this book, you’ll not only be ready to excel in the DP-203 exam, but also be equipped to tackle complex challenges as an Azure data engineer.What you will learn Design and implement data lake solutions with batch and stream pipelines Secure data with masking, encryption, RBAC, and ACLs Perform standard extract, transform, and load (ETL) and analytics operations Implement different table geometries in Azure Synapse Analytics Write Spark code, design ADF pipelines, and handle batch and stream data Use Azure Databricks or Synapse Spark for data processing using Notebooks Leverage Synapse Analytics and Purview for comprehensive data exploration Confidently manage VMs, VNETS, App Services, and more Who this book is for This book is for data engineers who want to take the Azure Data Engineer Associate (DP-203) exam and delve deep into the Azure cloud stack. Engineers and product managers new to Azure or preparing for interviews with companies working on Azure technologies will find invaluable hands-on experience with Azure data technologies through this book. A basic understanding of cloud technologies, ETL, and databases will assist with understanding the concepts covered.
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)

Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …

Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …

Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …

Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …

Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …

Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …

Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …

Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …

Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …

Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)

Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …

Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …

Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …

Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …

Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …

Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …

Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …

Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …

Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …