Data For Learning

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Street Data

Author : Shane Safir
Publisher : Corwin
ISBN 13 : 1071812661
Total Pages : 281 pages
Book Rating : 4.0/5 (718 download)

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Book Synopsis Street Data by : Shane Safir

Download or read book Street Data written by Shane Safir and published by Corwin. This book was released on 2021-02-12 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning from Data

Author : Vladimir Cherkassky
Publisher : John Wiley & Sons
ISBN 13 : 9780470140512
Total Pages : 560 pages
Book Rating : 4.1/5 (45 download)

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Book Synopsis Learning from Data by : Vladimir Cherkassky

Download or read book Learning from Data written by Vladimir Cherkassky and published by John Wiley & Sons. This book was released on 2007-09-10 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Learning from Data

Author : Yaser S. Abu-Mostafa
Publisher :
ISBN 13 : 9781600490064
Total Pages : 201 pages
Book Rating : 4.4/5 (9 download)

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Book Synopsis Learning from Data by : Yaser S. Abu-Mostafa

Download or read book Learning from Data written by Yaser S. Abu-Mostafa and published by . This book was released on 2012-01-01 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning and Data Science Blueprints for Finance

Author : Hariom Tatsat
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492073008
Total Pages : 432 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Using Data to Improve Student Learning in School Districts

Author : Victoria Bernhardt
Publisher : Routledge
ISBN 13 : 1317922840
Total Pages : 499 pages
Book Rating : 4.3/5 (179 download)

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Book Synopsis Using Data to Improve Student Learning in School Districts by : Victoria Bernhardt

Download or read book Using Data to Improve Student Learning in School Districts written by Victoria Bernhardt and published by Routledge. This book was released on 2013-10-11 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps you make sense of the data your school district collects, including state student achievement results as well as other qualitative and quantitative data. Easy-to-use templates, tools, and examples are available on the accompanying downloadable resources.

The Data Coach's Guide to Improving Learning for All Students

Author : Nancy Love
Publisher : Corwin Press
ISBN 13 : 1412950015
Total Pages : 409 pages
Book Rating : 4.4/5 (129 download)

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Book Synopsis The Data Coach's Guide to Improving Learning for All Students by : Nancy Love

Download or read book The Data Coach's Guide to Improving Learning for All Students written by Nancy Love and published by Corwin Press. This book was released on 2008-02-27 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use data as an effective tool for school change and improvement! This resource helps data team facilitators move schools away from unproductive data practices and toward examining data for systematic and continuous improvement in instruction and learning. The book, which includes a CD-ROM with slides and reproducibles, illustrates how the authors' model has proven successful in: Narrowing achievement gaps in all content areas and grade levels Achieving strong, continuous gains in local and state assessments in mathematics, science, and reading Initiating powerful conversations about race/ethnicity, class, educational status, gender, and language differences Developing a vision for a high-performing, data-informed school culture

The Art of Statistics

Author : David Spiegelhalter
Publisher : Penguin UK
ISBN 13 : 0241258758
Total Pages : 269 pages
Book Rating : 4.2/5 (412 download)

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Book Synopsis The Art of Statistics by : David Spiegelhalter

Download or read book The Art of Statistics written by David Spiegelhalter and published by Penguin UK. This book was released on 2019-03-28 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'A statistical national treasure' Jeremy Vine, BBC Radio 2 'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular Science Do busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science. Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. 'Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world' Nature

Big Data in Education

Author : Ben Williamson
Publisher : SAGE
ISBN 13 : 1526416344
Total Pages : 257 pages
Book Rating : 4.5/5 (264 download)

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Book Synopsis Big Data in Education by : Ben Williamson

Download or read book Big Data in Education written by Ben Williamson and published by SAGE. This book was released on 2017-07-24 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This cutting-edge overview explores big data and the related topic of computer code, examining the implications for education and schooling for today and the near future.

Machine Learning for Data Streams

Author : Albert Bifet
Publisher : MIT Press
ISBN 13 : 026254783X
Total Pages : 289 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Machine Learning for Data Streams by : Albert Bifet

Download or read book Machine Learning for Data Streams written by Albert Bifet and published by MIT Press. This book was released on 2023-05-09 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

R for Data Science

Author : Hadley Wickham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Big Data and Learning Analytics in Higher Education

Author : Ben Kei Daniel
Publisher : Springer
ISBN 13 : 9783319791517
Total Pages : 272 pages
Book Rating : 4.7/5 (915 download)

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Book Synopsis Big Data and Learning Analytics in Higher Education by : Ben Kei Daniel

Download or read book Big Data and Learning Analytics in Higher Education written by Ben Kei Daniel and published by Springer. This book was released on 2018-04-21 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns.

Utility-Based Learning from Data

Author : Craig Friedman
Publisher : CRC Press
ISBN 13 : 1420011286
Total Pages : 418 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Utility-Based Learning from Data by : Craig Friedman

Download or read book Utility-Based Learning from Data written by Craig Friedman and published by CRC Press. This book was released on 2016-04-19 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used t

Transforming Teaching and Learning Through Data-Driven Decision Making

Author : Ellen B. Mandinach
Publisher : Corwin Press
ISBN 13 : 1452279667
Total Pages : 281 pages
Book Rating : 4.4/5 (522 download)

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Book Synopsis Transforming Teaching and Learning Through Data-Driven Decision Making by : Ellen B. Mandinach

Download or read book Transforming Teaching and Learning Through Data-Driven Decision Making written by Ellen B. Mandinach and published by Corwin Press. This book was released on 2012-04-10 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connect data and instruction to improve practice Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text’s integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: Assessment Statistics Instructional and differentiated psychology Classroom management

Deep Learning with Structured Data

Author : Mark Ryan
Publisher : Simon and Schuster
ISBN 13 : 163835717X
Total Pages : 262 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning with Structured Data by : Mark Ryan

Download or read book Deep Learning with Structured Data written by Mark Ryan and published by Simon and Schuster. This book was released on 2020-12-08 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps

An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 3031387473
Total Pages : 617 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Using Data to Improve Learning for All

Author : Nancy Love
Publisher : Corwin Press
ISBN 13 : 1412960851
Total Pages : 193 pages
Book Rating : 4.4/5 (129 download)

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Book Synopsis Using Data to Improve Learning for All by : Nancy Love

Download or read book Using Data to Improve Learning for All written by Nancy Love and published by Corwin Press. This book was released on 2009 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative inquiry + effective use of data = significant leaps in learning and achievement! This resource combines a powerful collaborative inquiry process, reflective dialogue, and rigorous use of data to improve outcomes for all students. The editor and contributors provide detailed examples of schools that have demonstrated dramatic gains by building collaborative cultures, nurturing ongoing inquiry, and using data systematically. The book shows school leaders how to: Implement collaborative inquiry to meet accountability mandates Build and support a high-performing data culture Establish a school climate characterized by collective responsibility for student learning and a respect for students’ cultures

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262361108
Total Pages : 853 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher

Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.