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Visual Intelligence

AK Peters Visualization Series

The book explores real-world datasets, uncovering hidden patterns and gaining insights along the way. The book is filled with illustrations using practical examples, Python codes, and different types of exercises designed to reinforce the concepts and processes discussed.

Descriptive Analysis, Visualization, and Dashboard Design (with codes in Python)

Exploratory Data Analysis

Learn to build a web app that can monitor millions of posts across all social media platforms to chart public opinion in real time. Social media has become our modern-day agora, the place where we get our news and engage in political discourse. With disinformation, hate speech, foreign interference, authoritarianism, market monopolization, and political polarization all on the rise, monitoring social media in the public interest has never been so difficult nor so urgent. Social Media Observatory is an in-depth beginner’s guide to coding full-stack web applications in Python to track and analyze social media activity at scale, giving you unparalleled insights into the cultural and political conversations of our time, then empowering you to reach your own conclusions. You’ll start by coding a web app to monitor Russian disinformation channels discussing Ukraine on Telegram, then learn more adversarial methods to scrape the Wayback Machine, Instagram, and TikTok. You’ll detect Twitter bots following Saudi influencers, extract text-embedded URLs to reveal a white supremacist blogosphere, parse archived tweets to reveal the discourse leading up to the January 6 insurrection, and learn to circumvent Meta's and ByteDance's defenses to scrape Instagram and TikTok, collecting evidence of war crimes in the Middle East. Finally, you’ll write an API and deploy your observatory to a cloud server to collaborate with other investigators. By the end of the book, you’ll have a working website that allows collaborators to log in, request data collection, and perform analysis of collected data, via a point-and-click interface.&nbsp; Along the way, you’ll learn to: <ul><li>Use Python and RabbitMQ to retrieve Telegram data from a remote API</li><li>Store data with PostgreSQL and query it with SQLAlchemy</li><li>Analyze the data you’ve retrieved with Pandas, NetworkX, Gephi, and other tools</li><li>Use state-of-the-art AI tools to transcribe audio and classify text</li><li>Deploy a website with an interactive dashboard using FastAPI, Dash, Cytoscape, Uvicorn, Gunicorn, Nginx, AWS, and Let’s Encrypt</li><li>Containerize your apps with Docker and provision infrastructure with Pulumi</li></ul> Whether you’re a citizen scientist, a security researcher, a journalist, an activist, or a scholar, Social Media Observatory is an essential blueprint for building your window into online worlds.

Social Media Observatory

Drawing inspiration from fast and slow thinking, design thinking and our everchanging world, this book contrasts "fast" approaches-such as "quick-and-dirty ethnography, prototyping, and AI-driven automation-with "slow" methodologies that emphasize ethnographic studies, longitudinal research, and participatory design.

Methods for Timely HCI and Interaction Design Research

Fast and Slow

From the global scale of financial markets and communication networks, to the smallest moments of personal observation and social connection, data is created, shared, and analyzed at a speed and volume once unimaginable. Whether we realize it or not, today we are surrounded by invisible ecosystems of data.

Artists, Scientists, Thinkers, and Dreamers on How We Live Our Lives in Numbers

Speak Data

This book discusses the concept of the digital twin, which has the potential to change how systems are managed and created. It also discusses the metaverse as a new technology with literary roots, cross-platform avatars, and artificial intelligence-related cybersecurity risks.

From Virtual to Reality

Digital Twins for Smart Cities and Urban Planning

The DnA of Statistics

A practical guide to reproducible and high impact mass spectrometry data analysis R Programming for Mass Spectrometry teaches a rigorous and detailed approach to analyzing mass spectrometry data using the R programming language. It emphasizes reproducible research practices and transparent data workflows and is designed for analytical chemists, biostatisticians, and data scientists working with mass spectrometry. Readers will find specific algorithms and reproducible examples that address common challenges in mass spectrometry alongside example code and outputs. Each chapter provides practical guidance on statistical summaries, spectral search, chromatographic data processing, and machine learning for mass spectrometry. Key topics include: <ul><li>Comprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data</li><li>Processing chromatographic peaks, peak detection, and quality control in mass spectrometry data</li><li>Applying machine learning techniques, using Tidymodels for supervised and unsupervised learning, as well as for feature engineering and selection, providing modern approaches to data-driven insights</li><li>Methods for producing reproducible, publication-ready reports and web pages using RMarkdown</li></ul> R Programming for Mass Spectrometry is an indispensable guide for researchers, instructors, and students. It provides modern tools and methodologies for comprehensive data analysis. With a companion website that includes code and example datasets, it serves as both a practical guide and a valuable resource for promoting reproducible research in mass spectrometry.

Effective and Reproducible Data Analysis

R Programming for Mass Spectrometry

This book addresses the topic of HR analytics focusing especially on demystifying analytics for HR managers, from both statistical and computing points of view.

Theory, Tools and Techniques

People Analytics

Business analytics is about leveraging data analysis and analytical modeling methods to achieve business objectives. Suitable for senior and graduate students in business or data science, this innovative text presents methods in an intuitive fashion and backs up business applications with an approachable level of mathematical rigor.

Methods and Cases for Data-Driven Decisions

Business Analytics

Business analytics is all about leveraging data analysis and analytical modeling methods to achieve business objectives. This is the book for upper division and graduate business students with interest in data science, for data science students with interest in business, and for everyone with interest in both. A comprehensive collection of over 50 methods and cases is presented in an intuitive style, generously illustrated, and backed up by an approachable level of mathematical rigor appropriate to a range of proficiency levels. A robust set of online resources, including software tools, coding examples, datasets, primers, exercise banks, and more for both students and instructors, makes the book the ideal learning resource for aspiring data-savvy business practitioners.

'Kiko Llaneras is one of the most consistently interesting data journalists to have emerged in the last decade' John Burn-Murdoch, Chief data reporter, Financial Times It's almost impossible to feel confident we've made the right choice when we are constantly faced with conflicting data. From choosing the right school for our children to understanding major social and political events, making informed decisions can feel overwhelming. In a world overflowing with data, it is more important than ever to decipher what the numbers tell us. Drawing on years of work interpreting and explaining data about almost anything, renowned data journalist Kiko Llaneras offers practical tools and shortcuts to help you understand the numbers and make better decisions using a quantitative lens. Through a series of compelling examples, he reveals some of the surprising insights that a data-driven perspective can offer such as: <ul><li>What caused the Chernobyl disaster</li><li>How Barack Obama made decisions and slept so peacefully during his presidency</li><li>Why so many footballers are born in January</li></ul> An essential guide for decoding the complexities of our modern world, Think Clearly will transform the way you view and use data in every aspect of your life.

Eight Simple Rules to Succeed in the Data Age

Think Clearly

In today's data-driven world, earning the Tableau Certified Data Analyst credential signals your ability to connect, analyze, and communicate insights using one of the industry's leading visualization platforms. This study guide offers practical and comprehensive preparation for the certification exam, with walk-throughs, best practices, vocabulary, and example questions to help you build both confidence and competence in Tableau. Written by Christopher Gardner, business intelligence analyst and lead Tableau developer at the University of Michigan, this guide supports first-time test-takers and seasoned users alike. You'll begin with foundational skills in Tableau Prep Builder and Tableau Desktop--connecting, combining, and preparing data--before progressing to building effective visualizations, performing calculations, and applying advanced tools like level-of-detail expressions, parameters, forecasts, and predictive analytics. <ul> <li>Read, manipulate, and prepare data for analysis</li> <li>Navigate Tableau's tools to build impactful visualizations</li> <li>Write calculations and functions to enhance your dashboards</li> <li>Share your work responsibly with secure publishing options</li> </ul>

Tableau Certified Data Analyst Study Guide

Visual Intelligence

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