Guide To Teaching Data Science

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Guide to Teaching Data Science

Orit Hazzan,Koby Mike
Guide to Teaching Data Science Book PDF

Author : Orit Hazzan,Koby Mike
Publisher : Springer Nature
Page : 330 pages
File Size : 41,8 Mb
Release : 2023-03-20
Category : Computers
ISBN : 9783031247583

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Guide to Teaching Data Science by Orit Hazzan,Koby Mike Pdf

Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.

Guide to Teaching Computer Science

Orit Hazzan,Noa Ragonis,Tami Lapidot
Guide to Teaching Computer Science Book PDF

Author : Orit Hazzan,Noa Ragonis,Tami Lapidot
Publisher : Springer Nature
Page : 416 pages
File Size : 41,8 Mb
Release : 2020-08-05
Category : Education
ISBN : 9783030393601

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Guide to Teaching Computer Science by Orit Hazzan,Noa Ragonis,Tami Lapidot Pdf

This concise yet thorough textbook presents an active-learning model for the teaching of computer science. Offering both a conceptual framework and detailed implementation guidelines, the work is designed to support a Methods of Teaching Computer Science (MTCS) course, but may be applied to the teaching of any area of computer science at any level, from elementary school to university. This text is not limited to any specific curriculum or programming language, but instead suggests various options for lesson and syllabus organization. Fully updated and revised, the third edition features more than 40 new activities, bringing the total to more than 150, together with new chapters on computational thinking, data science, and soft concepts and soft skills. This edition also introduces new conceptual frameworks for teaching such as the MERge model, and new formats for the professional development of computer science educators. Topics and features: includes an extensive set of activities, to further support the pedagogical principles outlined in each chapter; discusses educational approaches to computational thinking, how to address soft concepts and skills in a MTCS course, and the pedagogy of data science (NEW); focuses on teaching methods, lab-based teaching, and research in computer science education, as well as on problem-solving strategies; examines how to recognize and address learners’ misconceptions, and the different types of questions teachers can use to vary their teaching methods; provides coverage of assessment, teaching planning, and designing a MTCS course; reviews high school teacher preparation programs, and how prospective teachers can gain experience in teaching computer science. This easy-to-follow textbook and teaching guide will prove invaluable to computer science educators within all frameworks, including university instructors and high school teachers, as well as to instructors of computer science teacher preparation programs.

Data Science for Beginners

Alex Campbell
Data Science for Beginners Book PDF

Author : Alex Campbell
Publisher : Unknown
Page : 86 pages
File Size : 44,8 Mb
Release : 2021-01-12
Category : Electronic
ISBN : 9798593883094

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Data Science for Beginners by Alex Campbell Pdf

Do you wonder what the fascination is around data these days? How do we obtain insights from this data? Do you know what a data scientist does? What is artificial intelligence and machine learning? Are these the same as data science? What does it take to become a data scientist? If you have ever wondered about these questions, you have come to the right place!There are many resources and courses online that you can use to learn more about data science, but with so much information available, it can become overwhelming. One of the best ways to learn about data science is to understand different machine learning concepts, statistics, and artificial intelligence to help you design models to perform an analysis.This book has all the information you need to learn what data science is, and what the prerequisites are to become a data scientist. If you're a beginner or if you already have experience in data science, this book will have something for you.In this book, you will: Learn what data science is about.Discover the difference between data science and business intelligence.Explore the tools required for data science.Find out the technical and non-technical skills every data scientist must have.Figure out how to create a visualization of the data set with clear and easy examples.Get advice on developing a Predictive Model Using R.Uncover detailed applications of data science.And much more!The book has been structured with easy-to-understand sections to help you learn everything you need to know about data science. In this book you will learn about the prerequisites of data science and the skills you need to become a data scientist. So, what are you waiting for? Grab your copy of this comprehensive guide now

Guide to Intelligent Data Science

Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn,Rosaria Silipo
Guide to Intelligent Data Science Book PDF

Author : Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn,Rosaria Silipo
Publisher : Springer Nature
Page : 427 pages
File Size : 50,9 Mb
Release : 2020-08-06
Category : Computers
ISBN : 9783030455743

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Guide to Intelligent Data Science by Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn,Rosaria Silipo Pdf

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.

The K-12 Educator’s Data Guidebook

Ryan A. Estrellado
The K 12 Educator s Data Guidebook Book PDF

Author : Ryan A. Estrellado
Publisher : Routledge
Page : 137 pages
File Size : 47,9 Mb
Release : 2022-03-21
Category : Education
ISBN : 9781000552959

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The K-12 Educator’s Data Guidebook by Ryan A. Estrellado Pdf

The K-12 Educator’s Data Guidebook is a comprehensive field guide for school professionals learning to use data. “Non-data people,” rejoice! Requiring no prior proficiency in data tools and programming, this book validates the implicit challenges of learning to use data to empower educators and features original real-world examples from in-service educators to illustrate common problem-solving. Each chapter uses stories, humor, and a human approach to set the tone for a safe and fun learning experience. Through this highly practical foundation, everyday educators can better engage school initiatives, professional development, and instructional challenges that require competent data use for improving school systems.

A Beginner’s Guide to Learning Analytics

Srinivasa K G,Muralidhar Kurni
A Beginner s Guide to Learning Analytics Book PDF

Author : Srinivasa K G,Muralidhar Kurni
Publisher : Springer Nature
Page : 326 pages
File Size : 48,9 Mb
Release : 2021-04-19
Category : Education
ISBN : 9783030702588

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A Beginner’s Guide to Learning Analytics by Srinivasa K G,Muralidhar Kurni Pdf

This book A Beginner’s Guide to Learning Analytics is designed to meet modern educational trends’ needs. It is addressed to readers who have no prior knowledge of learning analytics and functions as an introductory text to learning analytics for those who want to do more with evaluation/assessment in their organizations. The book is useful to all who need to evaluate their learning and teaching strategies. It aims to bring greater efficiency and deeper engagement to individual students, learning communities, and educators. Covered here are the key concepts linked to learning analytics for researchers and practitioners interested in learning analytics. This book helps those who want to apply analytics to learning and development programs and helps educational institutions to identify learners who require support and provide a more personalized learning experience. Like chapters show diverse uses of learning analytics to enhance student and faculty performance. It presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educational domains. This book provides educators and researchers with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This book will be a valuable addition to researchers’ bookshelves.

Envisioning the Data Science Discipline

National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Envisioning the Data Science Discipline Book PDF

Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Publisher : National Academies Press
Page : 69 pages
File Size : 54,9 Mb
Release : 2018-03-05
Category : Education
ISBN : 9780309465052

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Envisioning the Data Science Discipline by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective Pdf

The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.

The Challenges of the Digital Transformation in Education

Michael E. Auer,Thrasyvoulos Tsiatsos
The Challenges of the Digital Transformation in Education Book PDF

Author : Michael E. Auer,Thrasyvoulos Tsiatsos
Publisher : Springer
Page : 982 pages
File Size : 53,7 Mb
Release : 2019-03-15
Category : Technology & Engineering
ISBN : 9783030119324

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The Challenges of the Digital Transformation in Education by Michael E. Auer,Thrasyvoulos Tsiatsos Pdf

This book offers the latest research and new perspectives on Interactive Collaborative Learning and Engineering Pedagogy. We are currently witnessing a significant transformation in education, and in order to face today’s real-world challenges, higher education has to find innovative ways to quickly respond to these new needs. Addressing these aspects was the chief aim of the 21st International Conference on Interactive Collaborative Learning (ICL2018), which was held on Kos Island, Greece from September 25 to 28, 2018. Since being founded in 1998, the conference has been devoted to new approaches in learning, with a special focus on collaborative learning. Today the ICL conferences offer a forum for exchanging information on relevant trends and research results, as well as sharing practical experiences in learning and engineering pedagogy. This book includes papers in the fields of: * Collaborative Learning * Computer Aided Language Learning (CALL) * Educational Virtual Environments * Engineering Pedagogy Education * Game based Learning * K-12 and Pre-College Programs * Mobile Learning Environments: Applications It will benefit a broad readership, including policymakers, educators, researchers in pedagogy and learning theory, school teachers, the learning industry, further education lecturers, etc.

A Beginner's Guide To DATA SCIENCE

Enamul Haque
A Beginner s Guide To DATA SCIENCE Book PDF

Author : Enamul Haque
Publisher : Unknown
Page : 0 pages
File Size : 44,7 Mb
Release : 2023-01-06
Category : Electronic
ISBN : 1447826558

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A Beginner's Guide To DATA SCIENCE by Enamul Haque Pdf

This book is designed for aspiring data scientists who want to start their careers in data science, even if they don't have coding skills. It provides a comprehensive introduction to the foundations of data science and its applications, using simple language that is easy for beginners to understand. No technical expertise is required to master the material in this book. It is an ideal resource for anyone looking to learn about data science in an accessible and straightforward way. Key features include: Introduction to data science History of data science Data science life-cycle Data science tools and technologies Data science methodology Data science models Developing data science business strategy Managing data science projects Becoming a data scientist, data engineer etc. Big data Data Mining Artificial intelligence Machine learning Deep learning Neural networks Mathematical analysis Statistical modelling Understanding the fundamentals of data science programming languages Database structures and principles Robotic Process Automation Data science acronyms You need to know And a lot more.

Teaching Data Analytics

Susan A Vowels,Katherine Leaming Goldberg
Teaching Data Analytics Book PDF

Author : Susan A Vowels,Katherine Leaming Goldberg
Publisher : CRC Press
Page : 202 pages
File Size : 44,8 Mb
Release : 2019-06-17
Category : Computers
ISBN : 9781351721455

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Teaching Data Analytics by Susan A Vowels,Katherine Leaming Goldberg Pdf

The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry’s need for skilled data analysts to higher education’s need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students.

Evolution of STEM-Driven Computer Science Education

Vytautas Štuikys,Renata Burbaitė
Evolution of STEM Driven Computer Science Education Book PDF

Author : Vytautas Štuikys,Renata Burbaitė
Publisher : Springer Nature
Page : 368 pages
File Size : 40,5 Mb
Release : 2024-01-01
Category : Technology & Engineering
ISBN : 9783031482359

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Evolution of STEM-Driven Computer Science Education by Vytautas Štuikys,Renata Burbaitė Pdf

The book discusses the evolution of STEM-driven Computer Science (CS) Education based on three categories of Big Concepts, Smart Education (Pedagogy), Technology (tools and adequate processes) and Content that relates to IoT, Data Science and AI. For developing, designing, testing, delivering and assessing learning outcomes for K-12 students (9-12 classes), the multi-dimensional modelling methodology is at the centre. The methodology covers conceptual and feature-based modelling, prototyping, and virtual and physical modelling at the implementation and usage level. Chapters contain case studies to assist understanding and learning. The book contains multiple methodological and scientific innovations including models, frameworks and approaches to drive STEM-driven CS education evolution. Educational strategists, educators, and researchers will find valuable material in this book to help them improve STEM-driven CS education strategies, curriculum development, and new ideas for research.

Data Science in Education Using R

Ryan A. Estrellado,Emily A. Freer,Jesse Mostipak,Joshua M. Rosenberg,Isabella C. Velásquez
Data Science in Education Using R Book PDF

Author : Ryan A. Estrellado,Emily A. Freer,Jesse Mostipak,Joshua M. Rosenberg,Isabella C. Velásquez
Publisher : Routledge
Page : 284 pages
File Size : 49,5 Mb
Release : 2020-10-26
Category : Education
ISBN : 9781000200720

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Data Science in Education Using R by Ryan A. Estrellado,Emily A. Freer,Jesse Mostipak,Joshua M. Rosenberg,Isabella C. Velásquez Pdf

Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.

A Beginners Guide To DATA SCIENCE

Enamul Haque
A Beginners Guide To DATA SCIENCE Book PDF

Author : Enamul Haque
Publisher : Unknown
Page : 288 pages
File Size : 40,8 Mb
Release : 2021-03-31
Category : Electronic
ISBN : 9798731261074

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A Beginners Guide To DATA SCIENCE by Enamul Haque Pdf

Calling all the Aspiring Data Scientists! This book is your "one-stop-shop" to kick start your data science career without knowing how to code! In fact, data science doesn't have to be complicated! With this book, you will grow an understanding of the foundations of data science and its applications. To master this book, you don't need technical abilities. This book is recommended for beginners and anybody who want to understand data science conveniently. You don't need a big textbook to master data science today. A straightforward language has been used to ensure ease of understanding, especially for beginners. Key features include: Introduction to data scienceHistory of data scienceData science life-cycleData science tools and technologiesData science methodologyData science modelsDeveloping data science business strategyManaging data science projectsBecoming a data scientist, data engineers etc.Doing data science without codingBig dataData MiningArtificial intelligenceMachine learningDeep learningNeural networksMathematical analysisStatistical modellingUnderstanding the fundamentals of Python and RDatabase structures and principlesRobotic Process AutomationData science acronyms you need to knowOnline free data science learning resources And a lot more

International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) 13th International Conference on EUropean Transnational Education (ICEUTE 2022)

Pablo García Bringas,Hilde Pérez García,Francisco Javier Martínez de Pisón,José Ramón Villar Flecha,Alicia Troncoso Lora,Enrique A. de la Cal,Álvaro Herrero,Francisco Martínez Álvarez,Giuseppe Psaila,Héctor Quintián,Emilio Corchado
International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems CISIS 2022 13th International Conference on EUropean Transnational Education ICEUTE 2022 Book PDF

Author : Pablo García Bringas,Hilde Pérez García,Francisco Javier Martínez de Pisón,José Ramón Villar Flecha,Alicia Troncoso Lora,Enrique A. de la Cal,Álvaro Herrero,Francisco Martínez Álvarez,Giuseppe Psaila,Héctor Quintián,Emilio Corchado
Publisher : Springer Nature
Page : 279 pages
File Size : 46,9 Mb
Release : 2022-11-04
Category : Technology & Engineering
ISBN : 9783031184093

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International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) 13th International Conference on EUropean Transnational Education (ICEUTE 2022) by Pablo García Bringas,Hilde Pérez García,Francisco Javier Martínez de Pisón,José Ramón Villar Flecha,Alicia Troncoso Lora,Enrique A. de la Cal,Álvaro Herrero,Francisco Martínez Álvarez,Giuseppe Psaila,Héctor Quintián,Emilio Corchado Pdf

This book of Lecture Notes in Networks and Systems contains accepted papers presented at the 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) and the 13th International Conference on EUropean Transnational Education (ICEUTE 2022). These conferences were held in the beautiful city of Salamanca, Spain, in September 2022. The aim of the CISIS 2022 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of computational intelligence, information security, and data mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a thorough peer review process, the CISIS 2022 International Program Committee selected 20 papers, which are published in this conference proceedings. In this edition, three special sessions were organized: Cybersecurity in Future Connected Societies, Cybersecurity and Trusted Supply Chains of ICT, and Intelligent Solutions for Cybersecurity Systems. The aim of ICEUTE 2022 is to offer a meeting point for people working on transnational education within Europe. It provides a stimulating and fruitful forum for presenting and discussing the latest works and advances on transnational education within European countries. In the case of ICEUTE 2022, the International Program Committee selected 5 papers, which are also published in this conference proceedings. The selection of papers was extremely rigorous to maintain the high quality of the conferences. We want to thank the members of the Program Committees for their hard work during the reviewing process. This is a crucial process for creating a high-standard conference; the CISIS and ICEUTE would not exist without their help.

SQL for Data Scientists

Renee M. P. Teate
SQL for Data Scientists Book PDF

Author : Renee M. P. Teate
Publisher : John Wiley & Sons
Page : 400 pages
File Size : 46,9 Mb
Release : 2021-08-17
Category : Computers
ISBN : 9781119669395

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SQL for Data Scientists by Renee M. P. Teate Pdf

Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!