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| data science project based learning: Teaching Children Science Joseph S. Krajcik, Charlene Czerniak, Carl Berger, 1999 This brand-new elementary science methods text uses an innovative applied approach and is authored by three leaders in the field. The text takes a constructivist approach and practices this approach by engaging students in reflective thought and investigations.Project-based science engages young learners in exploring authentic, important, and meaningful questions of real concern to students. Through a dynamic process of investigation and collaboration and using the same processes and technologies that real scientists use, students work in teams to formulate questions, make predictions, design investigations, collect and analyze data, make products and share ideas. Students learn fundamental science concepts and principles that they apply to their daily lives. Project-based science helps all students regardless of culture, race, or gender engage in science learning.The book is packed with numerous examples so that the reader can easily understand points that are made throughout the book. Each chapter has activity boxes with experiments that exemplify the project-based approach. The book provides useful tips, charts, diagrams, and tables that illustrate how to get children doing investigations. The text's dynamic teaching methods match all of today's major science education reports including The National Science Education Standards, Project 2061: Science for All Americans, and Benchmarks for Science Literacy. |
| data science project based learning: Keep It Real With PBL, Elementary Jennifer Pieratt, 2019-09-25 Plan enriching Project-Based Learning experiences with ease! The book′s companion website features an updated guide to help teachers integrate technology into PBL experiences for online and blended learning instruction. Is project-planning a project in and of itself? Does project-based learning (PBL) feel more like a pipe dream than a reality in your classroom? Dr. Jennifer Pieratt, a consultant and former teacher herself, knows just where you′re coming from. Developed from the author′s experience in the trenches of project-based learning over the past decade, this book will lead you through the planning process for an authentic PBL experience in a clear and efficient way. Project-based learning has been found to develop workforce readiness, innovation, and student achievement. In this book, the keys to implementing PBL effectively are explored in a simple, easy-to-use format. In addition to thought-provoking questions for journaling, readers will find a visually accessible style featuring • #realtalk soundbites that honor the challenges to implementing PBL • Tips and resources to support the project-planning process • Planning forms to guide you through planning your projects • Key terminology and acronyms in PBL • Exercises to help you reflect and process throughout your project plans If mastering a PBL framework is on your list, prepare to cross it off with the help of this book! Foreword INDIES Book of the Year Awards Winner |
| data science project based learning: STEM Project-Based Learning Robert M. Capraro, Mary Margaret Capraro, James R. Morgan, 2013-04-20 This second edition of Project-Based Learning (PBL) presents an original approach to Science, Technology, Engineering and Mathematics (STEM) centric PBL. We define PBL as an “ill-defined task with a well-defined outcome,” which is consistent with our engineering design philosophy and the accountability highlighted in a standards-based environment. This model emphasizes a backward design that is initiated by well-defined outcomes, tied to local, state, or national standard that provide teachers with a framework guiding students’ design, solving, or completion of ill-defined tasks. This book was designed for middle and secondary teachers who want to improve engagement and provide contextualized learning for their students. However, the nature and scope of the content covered in the 14 chapters are appropriate for preservice teachers as well as for advanced graduate method courses. New to this edition is revised and expanded coverage of STEM PBL, including implementing STEM PBL with English Language Learners and the use of technology in PBL. The book also includes many new teacher-friendly forms, such as advanced organizers, team contracts for STEM PBL, and rubrics for assessing PBL in a larger format. |
| data science project based learning: Thinking Through Project-Based Learning Jane Krauss, Suzie Boss, 2013-03-20 Everything you need to know to lead effective and engaging project-based learning! Are you eager to try out project-based learning, but don't know where to start? How do you ensure that classroom projects help students develop critical thinking skills and meet rigorous standards? Find the answers in this step-by-step guide, written by authors who are both experienced teachers and project-based learning experts. Thinking Through Projects shows you how to create a more interactive classroom environment where students engage, learn, and achieve. Teachers will find: A reader-friendly overview of project-based learning that includes current findings on brain development and connections with Common Core standards, Numerous how-to's and sample projects for every K-12 grade level, Strategies for integrating project learning into all main subject areas, across disciplines, and with current technology and social media and Ways to involve the community through student field research, special guests, and ideas for showcasing student work. Whether you are new to project-based learning or ready to strengthen your existing classroom projects, you'll find a full suite of strategies and tools in this essential book. |
| data science project based learning: 2021 16th International Conference on Computer Science and Education (ICCSE) IEEE Staff, 2021-08-17 The scope of the conference will cover computer science, data science, educational technology, industrial design, etc |
| data science project based learning: Data Science Bookcamp Leonard Apeltsin, 2021-12-07 Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution |
| data science project based learning: Guide to Teaching Data Science Orit Hazzan, Koby Mike, 2023-03-20 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. |
| data science project based learning: Implementing Project-Based Learning Suzie Boss, 2015-04-21 Deepen learning experiences in every classroom. Project-based learning (PBL) has the potential to fully engage students of the digital age, changing student-teacher dynamics and giving students greater influence and agency in their learning. Discover user-friendly strategies for implementing PBL to equip students with essential 21st century skills, strengthen their problem-solving abilities, and prepare them for college and careers. |
| data science project based learning: Street Data Shane Safir, Jamila Dugan, 2021-02-12 Radically reimagine our ways of being, learning, and doing Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on fixing and filling academic gaps, we must envision and rebuild the system from the student up—with classrooms, schools and systems built around students’ brilliance, cultural wealth, and intellectual potential. Street data reminds us that what is measurable is not the same as what is valuable and that data can be humanizing, liberatory and healing. By breaking down street data fundamentals: what it is, how to gather it, and how it can complement other forms of data to guide a school or district’s equity journey, Safir and Dugan offer an actionable framework for school transformation. Written for educators and policymakers, this book · Offers fresh ideas and innovative tools to apply immediately · Provides an asset-based model to help educators look for what’s right in our students and communities instead of seeking what’s wrong · Explores a different application of data, from its capacity to help us diagnose root causes of inequity, to its potential to transform learning, and its power to reshape adult culture Now is the time to take an antiracist stance, interrogate our assumptions about knowledge, measurement, and what really matters when it comes to educating young people. |
| data science project based learning: Teaching Science in Elementary and Middle School Joseph S. Krajcik, Charlene M. Czerniak, 2014-01-23 Teaching Science in Elementary and Middle School offers in-depth information about the fundamental features of project-based science and strategies for implementing the approach. In project-based science classrooms students investigate, use technology, develop artifacts, collaborate, and make products to show what they have learned. Paralleling what scientists do, project-based science represents the essence of inquiry and the nature of science. Because project-based science is a method aligned with what is known about how to help all children learn science, it not only helps students learn science more thoroughly and deeply, it also helps them experience the joy of doing science. Project-based science embodies the principles in A Framework for K-12 Science Education and the Next Generation Science Standards. Blending principles of learning and motivation with practical teaching ideas, this text shows how project-based learning is related to ideas in the Framework and provides concrete strategies for meeting its goals. Features include long-term, interdisciplinary, student-centered lessons; scenarios; learning activities, and Connecting to Framework for K–12 Science Education textboxes. More concise than previous editions, the Fourth Edition offers a wealth of supplementary material on a new Companion Website, including many videos showing a teacher and class in a project environment. |
| data science project based learning: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science project based learning: The Cambridge Handbook of the Learning Sciences R. Keith Sawyer, 2014-11-17 The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences, first published in 2006, is the definitive introduction to this innovative approach to teaching, learning, and educational technology. In this dramatically revised second edition, leading scholars incorporate the latest research to provide practical advice on a wide range of issues. The authors address the best ways to write textbooks, design educational software, prepare effective teachers, organize classrooms, and use the Internet to enhance student learning. They illustrate the importance of creating productive learning environments both inside and outside school, including after school clubs, libraries, and museums. Accessible and engaging, the Handbook has proven to be an essential resource for graduate students, researchers, teachers, administrators, consultants, software designers, and policy makers on a global scale. |
| data science project based learning: 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 project based learning: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. |
| data science project based learning: Data Science for Undergraduates 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, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science project based learning: Project Based Learning Made Simple April Smith, 2018-05-08 100 ready-to-use projects to challenge and inspire your third-, fourth- and fifth-graders! Project Based Learning Made Simple is the fun and engaging way to teach twenty-first-century competencies including problem solving, critical thinking, collaboration, communication and creativity. This straightforward book makes it easier than ever to bring this innovative technique into your classroom with 100 ready-to-use projects in a range of topics, including: Science and STEM • Save the Bees! • Class Aquarium • Mars Colony Math Literacy • Personal Budgeting • Bake Sale • Family Cookbook Language Arts • Candy Bar Marketing • Modernize a Fairy Tale • Movie Adaptation Social Studies • Build a Statue • Establish a Colony • Documenting Immigration |
| data science project based learning: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science project based learning: Situating Data Science Michelle Hoda Wilkerson, Joseph L. Polman, 2022-04-19 The emerging field of Data Science has had a large impact on science and society. This book explores how one distinguishing feature of Data Science – its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded – suggests serious implications for learning and education. Drawing from theories of learning and identity development in the learning sciences, this volume investigates the impacts of these complex relationships on how learners think about, use, and share data, including their understandings of data in light of history, race, geography, and politics. More than just using ‘real world examples’ to motivate students to work with data, this book demonstrates how learners’ relationships to data shape how they approach those data with agency, as part of their social and cultural lives. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, socially aware, and transformative Data Science Education. The chapters in this book were originally published as a special issue of the Journal of the Learning Sciences. |
| data science project based learning: Core Practices for Project-Based Learning Pam Grossman, Christopher G. Pupik Dean, Zachary Herrmann, Sarah Schneider Kavanagh, 2021-06-22 Core Practices for Project-Based Learning offers a framework and essential set of strategies for successfully implementing project-based learning (PBL) in the classroom. Centering on teaching practice, this work moves beyond project planning to focus on the complex instructional demands of the student-centered PBL approach. Pam Grossman and her colleagues draw on their research with teachers, educational leaders, and curriculum designers to identify the instructional goals, practices, and mindsets that enable educators to effectively facilitate deep learning in PBL environments. The authors first define the four primary teaching goals of the PBL model: supporting subject-area learning, engaging students in authentic work, encouraging student collaboration and agency, and building an iterative culture where students are always prototyping, reflecting, and trying again. They then equip educators with ten key practices that serve these goals. These practices include methods to elicit higher-order thinking, engage students in disciplinary and interdisciplinary practice, and mentor student decision making. The authors guide educators from a clear starting place through a series of concrete, manageable steps that apply whether they are initiating PBL or working to improve the quality of existing PBL implementation. Extended case studies illustrate the use of the core practices in real-world situations. Core Practices for Project-Based Learning is an invaluable resource to help educators realize their instructional vision and create meaningful student experiences. |
| data science project based learning: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-18 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| data science project based learning: Sustainable Project-Based Learning Brad Sever, 2022-03-22 This essential guide shares a five-step process for designing, implementing, and assessing sustainable project-based learning (SPBL) units while ensuring students gain surface-, deep-, and transfer-level knowledge. Brad Sever draws from his daily work as a practitioner to deliver practical strategies for creating meaningful learning experiences that join academic growth with social-emotional skill development—all supported by the power of professional learning communities. Use this book to help increase authentic learning for your students: Learn the benefits of project-based learning (PBL) and social-emotional learning (SEL) to student growth and academic achievement. Access a five-step process for planning, integrating, and sustaining a comprehensive SPBL framework in your curriculum. Receive reproducible tools and templates you can use independently or collaboratively to maximize student engagement and learning. Reflect with end-of-chapter questions to enhance your understanding of the content. Understand how to promote transfer learning skills through connections to real-world experiences. Contents: Introduction Part I: The What and Why of Sustainable Project-Based Learning Chapter 1: Defining Sustainable Project-Based Learning Chapter 2: Reviewing the Seven PBL Design Elements Chapter 3: Maintaining a Sustainable Relationship With Project-Based Learning Chapter 4: Integrating Social and Emotional Learning Into SPBL Units Part II: The How of Sustainable Project-Based Learning Chapter 5: Step 1 Planning the SBPL Unit Chapter 6: Step 2 Developing SPBL Assessments Chapter 7: Step 3 Establishing a Clear Goal for Student Learning Chapter 8: Step 4 Conducting Teacher Action Research Chapter 9: Step 5 Reflecting, Refining, and Celebrating Chapter 10: Transferring Learning Through Three Simultaneous Experiences Appendix A: Glossary of Key Terms Appendix B: Protocol Library Appendix C: Example SPBL Unit Plans References and Resources Index |
| data science project based learning: Improving Equity in Data Science Colby Tofel-Grehl, Emmanuel Schanzer, 2024-06-03 Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, “how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?” whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms. |
| data science project based learning: Setting the Standard for Project Based Learning John Larmer, John R. Mergendoller, Suzie Boss, 2015-05-26 This book take readers through the step-by-step process of how to create, implement, and assess project based learning (PBL) using a classroom-tested framework. Also included are chapters for school leaders on implementing PBL system wide and the use of PBL in informal settings. |
| data science project based learning: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science project based learning: Creativity in Intelligent Technologies and Data Science Alla Kravets, Maxim Shcherbakov, Marina Kultsova, Olga Shabalina, 2015-09-11 This book constitutes the refereed proceedings of the First Conference on Creativity in Intelligent Technologies and Data Science, CIT&DS 2015, held in Volgograd, Russia, in September 2015. The 66 revised full papers and two short papers presented were carefully reviewed and selected from 208 submissions. The papers are organized in topical sections on computational creativity for science and design; knowledge discovery in patent and open sources for creative tasks; software computer-aided design and agent-based systems; conceptual, cognitive and qualitative modeling with application in intelligent decision making; design creativity in CAD/CAM/CAE/PDM; intelligent decision support for continual improvement process; data science in energy management, transportation and urban development; data science in social networks analysis; natural language and image processing and analysis; game-based learning technologies in engineering education and educational games design; personalized learning in Web-based intelligent educational systems; e-inclusion: development of smart mobile applications for people with disabilities. |
| data science project based learning: Enhancing Education With Intelligent Systems and Data-Driven Instruction Bhatia, Madhulika, Mushtaq, Muhammad Tahir, 2024-03-04 In the dynamic environment of education, the traditional methods employed by educators are struggling to keep pace with the evolving needs of students in the 21st century. The challenge lies in fostering an environment that not only engages students but also equips them with the skills essential for the modern world. Teachers find themselves navigating a complex terrain where outdated pedagogical approaches fall short of unlocking the full potential of diverse learning styles and unique talents within their classrooms. Enhancing Education With Intelligent Systems and Data-Driven Instruction is a groundbreaking book that goes beyond the constraints of conventional teaching methods, offering a comprehensive guide that inspires and equips educators with innovative tools and approaches. From integrating innovative technology to cultivating collaborative learning environments, the book provides a roadmap for educators to reimagine their teaching practices. By embracing student-centered approaches, fostering diversity, and utilizing digital tools effectively, this book empowers teachers to transform their classrooms into dynamic hubs of inspiration, motivation, and empowerment. |
| data science project based learning: Rigorous PBL by Design Michael McDowell, 2017-03-01 By designing projects that move students from surface to deep and transfer learning through PBL, they will become confident and competent learners. Discover how to make three shifts essential to improving PBL’s overall effect: Clarity: Students should be clear on what they are expected to learn, where they are in the process, and what next steps they need to take to get there. Challenge: Help students move from surface to deep and transfer learning. Culture: Empower them to use that knowledge to make a difference in theirs and the lives of others. |
| data science project based learning: Creativity in Intelligent Technologies and Data Science Alla G. Kravets, Peter P. Groumpos, Maxim Shcherbakov, Marina Kultsova, 2019-08-29 This two-volume set constitutes the proceedings of the Third Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2019, held in Volgograd, Russia, in September 2019. The 67 full papers, 1 short paper and 3 keynote papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections in the two volumes. Part I: cyber-physical systems and Big Data-driven world. Part II: artificial intelligence and deep learning technologies for creative tasks; intelligent technologies in social engineering. |
| data science project based learning: Project Based Learning Starter Kit John Larmer, David Ross, Buck Institute for Education, John R. Mergendoller, 2010-06 |
| data science project based learning: Frameworks for Integrated Project-Based Instruction in STEM Disciplines Anthony J. Petrosino, Candace Walkington, Denise Ekberg, 2024-01-01 Frameworks for Integrated Project-Based Instruction in STEM Disciplines presents an original approach to Science, Technology, Engineering, and Mathematics (STEM) centric project based instruction. We approach project based instruction from an engineering design philosophy and the accountability highlighted in a standards-based environment. We emphasize a backward design that is initiated by well-defined outcomes tied to local, state, or national standards that provide teachers with a framework guiding students' design, solving, or completion of ill-defined tasks. In project-based STEM classrooms students investigate, utilize technological tools, construct artifacts, participate in debates, collaborate, and make products to demonstrate what they have learned. Features include deep coverage of four topics in PBI: scaffolding, student-driven inquiry, driving questions, and development of lessons based on national and state standards. This focus will ensure a deep understanding by the reader of project-based instruction, which will allow the reader to create strong and meaningful lesson experiences for their students. An emphasis on student-driven inquiry will be discussed, including the importance of giving students the cognitive tools, such as statistical analysis tools, they need to research and inquire about the lesson topic. A breakdown of what a successful driving question includes will be explained, and examples given. The book will include strategies for starting the lesson process with ending goals in mind by creating driving questions and breaking down state and national standards. This book is strongly rooted in research in the learning sciences about project-based instruction, but will also be designed to be practically useful to teachers and teacher educators and researchers by bridging research and practice. |
| data science project based learning: Cross Reality and Data Science in Engineering Michael E. Auer, Dominik May, 2020-08-20 Today, online technologies are at the core of most fields of engineering and society as a whole . This book discusses the fundamentals, applications and lessons learned in the field of online and remote engineering, virtual instrumentation, and other related technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Since the first Remote Engineering and Virtual Instrumentation (REV) conference in 2004, the event has focused on the use of the Internet for engineering tasks, as well as the related opportunities and challenges. In a globally connected world, interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. In this context, the REV conferences discuss fundamentals, applications and experiences in the field of Online and Remote Engineering as well as Virtual Instrumentation. Furthermore, the conferences focus on guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and open resources. This book presents the proceedings of REV2020 on “Cross Reality and Data Science in Engineering” which was held as the 17th in series of annual events. It was organized in cooperation with the Engineering Education Transformations Institute and the Georgia Informatics Institutes for Research and Education and was held at the College of Engineering at the University of Georgia in Athens (GA), USA, from February 26 to 28, 2020. |
| data science project based learning: Statistics for Empowerment and Social Engagement Jim Ridgway, 2023-03-10 “This book is a remarkable achievement” Gerd Gigerenzer This book offers practical approaches to working in a new field of knowledge - Civic Statistics - which sets out to engage with, and overcome well documented and long-standing problems in teaching quantitative skills. The book includes 23 peer-reviewed chapters, written in coordination by an international group of experts from ten countries. The book aims to support and enhance the work of teachers and lecturers working both at the high school and tertiary (university) levels. It is designed to promote and improve the critical understanding of quantitative evidence relevant to burning social issues – such as epidemics, climate change, poverty, migration, natural disasters, inequality, employment, and racism. Effective citizen engagement with social issues requires active participation and a broad understanding of data and statistics about societal issues. However, many statistics curricula are not designed to teach relevant skills nor to improve learners' statistical literacy. Evidence about social issues is provided to the public via print and digital media, official statistics offices, and other information channels, and a great deal of data is accessible both as aggregated summaries and as individual records. Chapters illustrate the approaches needed to teach and promote the knowledge, skills, dispositions, and enabling processes associated with critical understanding of Civic Statistics presented in many forms. These include: statistical analysis of authentic multivariate data; use of dynamic data visualisations; deconstructing texts about the social and economic well-being of societies and communities. Chapters discuss: the development of curricula and educational resources; use of emerging technologies and visualizations; preparation of teachers and teaching approaches; sources for relevant datasets and rich texts about Civic Statistics; ideas regarding future research, assessment, collaborations between different stakeholders; and other systemic issues. |
| data science project based learning: Paradigm Shifts in 21st Century Teaching and Learning Orakc?, ?enol, 2020-04-24 One of the most important transformations in the world today is the adaptation to education and teaching methods that must be made to enhance the learning experience for Millennial and Generation Z students. The system in which the student is passive and the teacher is active is no longer the most effective form of education. Additionally, with the increased availability to information, knowledge transfer is no longer done solely by the teacher. Educators need to become moderators in order to promote effective teaching practices. Paradigm Shifts in 21st Century Teaching and Learning is an essential scholarly publication that examines new approaches to learning and their application in the teaching-learning process. Featuring a wide range of topics such as game-based learning, curriculum design, and sustainability, this book is ideal for teachers, curriculum developers, instructional designers, researchers, education professionals, administrators, academicians, educational policymakers, and students. |
| data science project based learning: Behavioral Competencies of Digital Professionals Sara Bonesso, Elena Bruni, Fabrizio Gerli, 2019-12-18 Shedding new light on the human side of big data through the lenses of emotional and social intelligence competencies, this book advances the understanding of the requirements of the different professions that deal with big data. It also illustrates the empirical evidence collected through the application of the competency-based methodology to a sample of data scientists and data analysts, the two most in-demand big data jobs in the labor market. The book provides recommendations for the higher education system to offer better designed curricula for entry-level big data professions. It also offers managerial insights in describing how organizations and specifically HR practitioners can benefit from the competency-based approach to overcome the skill shortage that characterizes the demand for big data professional roles and to increase the effectiveness of the selection and recruiting processes. |
| data science project based learning: Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies Murugan, Thangavel, E., Nirmala, 2023-09-21 Disruptive innovations are now propelling Industry 4.0 (I4.0) and presenting new opportunities for value generation in all major industry segments. I4.0 technologies' innovations in cybersecurity and data science provide smart apps and services with accurate real-time monitoring and control. Through enhanced access to real-time information, it also aims to increase overall effectiveness, lower costs, and increase the efficiency of people, processes, and technology. The Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies discusses the technological foundations of cybersecurity and data science within the scope of the I4.0 landscape and details the existing cybersecurity and data science innovations with I4.0 applications, as well as state-of-the-art solutions with regard to both academic research and practical implementations. Covering key topics such as data science, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, computer scientists, scholars, researchers, academicians, practitioners, instructors, and students. |
| data science project based learning: 97 Things About Ethics Everyone in Data Science Should Know Bill Franks, 2020-08-06 Most of the high-profile cases of real or perceived unethical activity in data science arenâ??t matters of bad intent. Rather, they occur because the ethics simply arenâ??t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Conceptâ??Tim Wilson How to Approach Ethical Transparencyâ??Rado Kotorov Unbiased ≠ Fairâ??Doug Hague Rules and Rationalityâ??Christof Wolf Brenner The Truth About AI Biasâ??Cassie Kozyrkov Cautionary Ethics Talesâ??Sherrill Hayes Fairness in the Age of Algorithmsâ??Anna Jacobson The Ethical Data Storytellerâ??Brent Dykes Introducing Ethicizeâ?¢, the Fully AI-Driven Cloud-Based Ethics Solution!â??Brian Oâ??Neill Be Careful with Decisions of the Heartâ??Hugh Watson Understanding Passive Versus Proactive Ethicsâ??Bill Schmarzo |
| data science project based learning: Machine Intelligence and Data Science Applications Vaclav Skala, T. P. Singh, Tanupriya Choudhury, Ravi Tomar, Md. Abul Bashar, 2022-08-01 This book is a compilation of peer reviewed papers presented at International Conference on Machine Intelligence and Data Science Applications (MIDAS 2021), held in Comilla University, Cumilla, Bangladesh during 26 – 27 December 2021. The book covers applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, speech and gesture analysis, etc. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber physical system and smart agriculture, etc. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates. |
| data science project based learning: Ambitious Science Teaching Mark Windschitl, Jessica Thompson, Melissa Braaten, 2020-08-05 2018 Outstanding Academic Title, Choice Ambitious Science Teaching outlines a powerful framework for science teaching to ensure that instruction is rigorous and equitable for students from all backgrounds. The practices presented in the book are being used in schools and districts that seek to improve science teaching at scale, and a wide range of science subjects and grade levels are represented. The book is organized around four sets of core teaching practices: planning for engagement with big ideas; eliciting student thinking; supporting changes in students’ thinking; and drawing together evidence-based explanations. Discussion of each practice includes tools and routines that teachers can use to support students’ participation, transcripts of actual student-teacher dialogue and descriptions of teachers’ thinking as it unfolds, and examples of student work. The book also provides explicit guidance for “opportunity to learn” strategies that can help scaffold the participation of diverse students. Since the success of these practices depends so heavily on discourse among students, Ambitious Science Teaching includes chapters on productive classroom talk. Science-specific skills such as modeling and scientific argument are also covered. Drawing on the emerging research on core teaching practices and their extensive work with preservice and in-service teachers, Ambitious Science Teaching presents a coherent and aligned set of resources for educators striving to meet the considerable challenges that have been set for them. |
| data science project based learning: Innovative Practices in Teaching Information Sciences and Technology John M. Carroll, |
| data science project based learning: Handbook on Teaching and Learning in Operations Management Yufeng Zhang, 2024-05-02 This essential Handbook outlines the latest research on operations management teaching, and identifies new developments in the overall trends of (de)globalisation, sustainability and digitalisation. It highlights contemporary developments in teaching practice, providing theoretical insights into potential future pedagogical directions. |
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 …
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, …
