Download or read book Data Engineering and Data Science for Babies written by Data Babee and published by . This book was released on 2021-09-24 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Engineering and Data Science for Babies: Activity Workbook Part 1 From data collection to machine learning....for children of all ages! This workbook contains hands-on activities to introduce all age groups (from young and old) to concepts from both data engineering and data science in a fun accessible manner. Data engineering is crucial to the data science process, however it is often skipped over and assumed to already be in place. This book aims to start teaching data science from scratch - with no background knowledge required. You or your child may not fully grasp some of the concepts in this book the first time - and that's OK! This book is intended to be revisited many times. The goal is to gain familiarity with the vocabulary, as well as the data engineering and data science process. Concepts that were not clear the first time around will become easier to pick up in future visits. There are even hidden mnemonics and puns throughout this book that you will hopefully appreciate and find humor in when coming back after learning more! Part 1 of the Activity Workbook contains these topics: Introduction.....................................page 5 Data can help us solve many problems. Let's follow the story of a baby bee doing just that. Data Collection..............................page 6 The first step is getting data, but what exactly is "data" and where can we find it? Data Storage...................................page 16 Now that we have data, where do we put it? Are there different ways to store it? Part 2 will be released at a later date, and will contain these topics: Data Pipeline...................................page TBD Alright, we have data and a place to store it - how do we get data to that place? Data Science....................................page TBD Let's have fun and make use of the data we stored - maybe it can help us make more honey!

Download or read book Computer Engineering for Babies written by Chase Roberts and published by . This book was released on 2021-10-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to computer engineering for babies. Learn basic logic gates with hands on examples of buttons and an output LED.

Download or read book Data Science For Dummies written by Lillian Pierson and published by John Wiley & Sons. This book was released on 2017-02-21 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

Download or read book Perspectives on Data Science for Software Engineering written by Tim Menzies and published by Morgan Kaufmann. This book was released on 2016-07-14 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data Science with Java written by Michael R. Brzustowicz, PhD and published by "O'Reilly Media, Inc.". This book was released on 2017-06-06 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms

Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Download or read book Data Science from Scratch written by Joel Grus and published by "O'Reilly Media, Inc.". This book was released on 2015-04-14 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data Smart written by John W. Foreman and published by John Wiley & Sons. This book was released on 2013-10-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.

Download or read book Build a Career in Data Science written by Emily Robinson and published by Manning Publications. This book was released on 2020-03-24 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder

Download or read book Robotics for Babies written by Chris Ferrie and published by Sourcebooks, Inc.. This book was released on 2019-03-01 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Help your future genius become the smartest baby in the room by introducing them to robotics with the next installment of the Baby University board book series! Enjoy these simple explanations of complex ideas for your future genius. The perfect robot baby toy or baby engineering book for parents looking to kick start their baby's learning! Robotics for Babies is a colorful, simple introduction to the technology behind robots. This engineering board book is full of scientific and mathematical information from experts Dr. Sarah Kaiser and Chris Ferrie. Robotics for Babies is the perfect book to teach complex robotics concepts in a simple, engaging way. It's never too early to become a scientist! Set the children in your life on a lifelong path to learning with the next incredible installment of the Baby University board book series. Other Baby University titles include: Quantum Physics for Babies Rocket Science for Babies and many more!

Download or read book 97 Things Every Data Engineer Should Know written by Tobias Macey and published by "O'Reilly Media, Inc.". This book was released on 2021-06-11 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail

Download or read book Mastering Kafka Streams and ksqlDB written by Mitch Seymour and published by O'Reilly Media. This book was released on 2021-02-04 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time. Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You'll learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing. Learn the basics of Kafka and the pub/sub communication pattern Build stateless and stateful stream processing applications using Kafka Streams and ksqlDB Perform advanced stateful operations, including windowed joins and aggregations Understand how stateful processing works under the hood Learn about ksqlDB's data integration features, powered by Kafka Connect Work with different types of collections in ksqlDB and perform push and pull queries Deploy your Kafka Streams and ksqlDB applications to production

Download or read book Data Engineering with Google Cloud Platform written by Adi Wijaya and published by Packt Publishing Ltd. This book was released on 2022-03-31 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.

Download or read book Fundamentals of Data Engineering written by Joe Reis and published by "O'Reilly Media, Inc.". This book was released on 2022-06-22 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle

Download or read book What Is Data Science written by Mike Loukides and published by "O'Reilly Media, Inc.". This book was released on 2011-04-10 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.

Download or read book Data Science written by Certybox Education and published by Certybox Education. This book was released on 2023-02-16 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms. In this book you will learn all the basic concepts to start with data science in real life. Making base clear will help you to become Data Scientist in future. So if you are looking for the starting point in the field of Data Science, this book is perfect!

Download or read book 97 Things Every Data Engineer Should Know written by Tobias Macey and published by "O'Reilly Media, Inc.". This book was released on 2021-06-11 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail