Learning to See Data

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Release : 2020-12-15
Genre : Business & Economics
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Book Rating : 454/5 ( reviews)

Learning to See Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning to See Data write by Ben Jones. This book was released on 2020-12-15. Learning to See Data available in PDF, EPUB and Kindle. This book is associated with the 'Data Literacy Level 1' on-demand online course: https://dataliteracy.com/courses/data-literacy-level-1 For most of us, it's rare to go a full day without coming across data in the form of a chart, map or dashboard. Graphical displays of data are all around us, from performance indicators at work to election trackers on the news to traffic maps on the road. But few of us have received training or instruction in how to actually read and interpret them. How many times have we been misled simply because we aren't aware of the pitfalls to avoid when interpreting data visualizations. Learning to See Data will teach you the different ways that data can be encoded in graphical form, and it will give you a deeper understanding of the way our human visual system interprets these encodings. You will also learn about the most common chart types, and the situations in which they are most appropriate. From basic bar charts to overused pie charts to helpful maps and many more, a wide array of chart types are covered in detail, and conventions, pitfalls, strengths and weaknesses of each of them are revealed. This book will help you develop fluency in the interpretation of charts, an ability that we all need to hone and perfect if we are to make meaningful contributions in the professional, public and personal arenas of life. The principles covered in it also serve as a critical background for anyone looking to create charts that others will be able to understand. "This book is clear and evocative, thorough and thoughtful, and remarkably readable: a marvelous launchpad into the world of data." –Tamara Munzner, Professor, University of British Columbia Computer Science "Everyone of us needs good data literacy skills to survive in the modern world. Without them, it's hard to succeed at work, or survive the onslaught of information (and misinformation) across all our media. Ben's book provides the necessary building blocks for a strong foundation. From that foundation, Ben's approach will inspire you to own the process of developing your skills further." –Andy Cotgreave, Technical Evangelism Director, Tableau

Learning to See

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Author :
Release : 2003
Genre : Business & Economics
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Book Rating : 308/5 ( reviews)

Learning to See - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning to See write by Mike Rother. This book was released on 2003. Learning to See available in PDF, EPUB and Kindle. Lean production is the gold standard in production systems, but has proven famously difficult to implement in North America. Mass production relies on large inventories, uses "push" processes and struggles with long lead times. Moving towards a system that eliminates muda ("waste") caused by overproduction, while challenging, proves necessary for improved efficiency. Often overlooked, value stream mapping is the essential planning stage for any Lean transformation. In Mike Rother and John Shook's essential guide, you follow the value stream mapping undertaken for Acme Stamping, for its current and future state. Fully illustrated and well-organized, Learning to See is a must-see for the value stream manager.

Street Data

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Release : 2021-02-12
Genre : Education
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Book Rating : 661/5 ( reviews)

Street Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Street Data write by Shane Safir. This book was released on 2021-02-12. Street Data available in PDF, EPUB and Kindle. 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.

Using Data to Improve Student Learning in School Districts

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Release : 2013-10-11
Genre : Education
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Book Rating : 859/5 ( reviews)

Using Data to Improve Student Learning in School Districts - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Using Data to Improve Student Learning in School Districts write by Victoria Bernhardt. This book was released on 2013-10-11. Using Data to Improve Student Learning in School Districts available in PDF, EPUB and Kindle. 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.

Machine Learning and Data Science Blueprints for Finance

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Release : 2020-10-01
Genre : Computers
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Book Rating : 008/5 ( reviews)

Machine Learning and Data Science Blueprints for Finance - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning and Data Science Blueprints for Finance write by Hariom Tatsat. This book was released on 2020-10-01. Machine Learning and Data Science Blueprints for Finance available in PDF, EPUB and Kindle. 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