“…., as well as of the equipment inthe currently booming field of telecommunications, such as antennas, radars, etc.The present book is uniquely written to enable the reader-- be it a student, a scientist, or a practitioner-- to successfully perform important simulation techniques and to design efficient computer software for electromagnetic device analysis. …”

“…Gaertner is a principal research Scientist in SRI International's Center for Education Policy. …”

“…In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. …”

“…Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases…”

Tabla de Contenidos: “…Specifications Good, Standards Bad; Open Source Tools; ASCII; Regular Expressions; Format Commands; Converting Nonprintable Files to Plain-Text; Dublin Core; Glossary; References; Chapter 3: Indexing Text; 3.1. How Data Scientists Use Indexes…”

Tabla de Contenidos: “…Mackaness -- Gaining knowledge from georeferenced social media data with visual analytics / Gennady Andrienko and Natalia Andrienko -- Head/tail breaks for visualization of city structure and dynamics / Bin Jiang -- Querying VGI by semantic enrichment / Rob Lemmens, Gilles Falquet, Stefano De Sabbata, Bin Jiang, and Benedicte Bucher -- Extracting location information from crowd-sourced social network data / Pinar Karagoz, Hali Oguztuzun, Ruket Cakici, Ozer Ozdikis, Kezban Dilek Onal, and Meryem Sagcan -- Spatial and temporal sentiment analysis of Twitter data / Zhiwen Song and Jianhong (Cecilia) Xia -- Social networks VGI : Twitter sentiment analysis of social hotspots / Dario Stojanovski, Ivan Chorbev, Ivica Dimitrovski, and Gjorgji Madjarov -- Research on social media feeds : a GIScience perspective / Enrico Steiger, Rene Westerholt, and Alexander Zipf -- Changing role of citizens in national environmental monitoring / Juhani Kettunen, Jari Silander, Matti Lindholm, Maiju Lehtiniemi, Outi Setälä, and Seppo Kaitala et al -- On the contribution of volunteered geographic information to land monitoring efforts / Jamal Jokar Arsanjani and Cidália C Fonte -- Discussing the potential of crowdsourced geographic information for urban areas monitoring using the Panoramio initiative / Flavio Lupia and Jacinto Estima -- AtrapaelTigre.com : enlisting citizen-scientists in the war on tiger mosquitoes / Aitana Oltra, John R.B. …”

Tabla de Contenidos: “…; Big Data Hardware; Data Scientists and Data Admins: Big Data Managers; Chief Data Officer: Big Data Owner; Your Big Data Vision; Chapter 4: Big Data at Work…”

“…If you are a data engineer, an application developer, or a data scientist who would like to leverage the power of Apache Spark to get better insights from big data, then this is the book for you…”

“…Qian Xuesen) and his co-authors, which published between 1938-1956 when he was studying and working in the United States as a graduate student, scientist and professor, when aeronautic exploration stepped up from low speed to high speed regimes and astronautic technology entered its infant stage…”

“…This book is for you if you are a data scientist or working on any technical or scientific computation projects. …”

“…This is your companion to understanding and implementing a solid data analysis solution using Java Who This Book Is For If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. …”

Tabla de Contenidos: “…Feldman and Jongmin Choi 14 MNE Innovation Networks and the Role of Cities 294 Simona Iammarino and Philip McCann 15 The Rise of the Global Creative Class 317 Richard Florida and Charlotta Mellander 16 Global Science Collaboration 347 Stefan Hennemann and Ingo Liefner 17 International Mobility of Scientists 368 Kieron Flanagan 18 The Role of Global Connectedness in the Development of Indigenous Science in Middle?]…”

Tabla de Contenidos: “…Introduction -- 1 Walmart: How Big Data Is Used To Drive Supermarket Performance -- 2 CERN: Unravelling the Secrets of the Universe with Big Data -- 3 Netflix: How Netflix Used Big Data to Give Us the Programmes We Want -- 4 Rolls-Royce: How Big Data Is Used To Drive Success In Manufacturing -- 5 Shell: How Big Oil Uses Big Data -- 6 Apixio: How Big Data Is Transforming Healthcare -- 7 Lotus F1 Team: How Big Data Is Essential To The Success Of Motorsport Teams -- 8 Pendleton & Son Butchers: Big Data for Small Business -- 9 US Olympic Women's Cycling Team: How Big Data Analytics Is Used To Optimize Athletes' Performance -- 10 ZSL: Big Data In The Zoo And To Protect Animals -- 11 Facebook: How Facebook Use Big Data to Understand Customers -- 12 John Deere: How Big Data Can Be Applied On Farms -- 13 Royal Bank of Scotland: Using Big Data to Make Customer Service More Personal -- 14 LinkedIn: How Big Data Is Used To Fuel Social Media Success -- 15 Microsoft: Bringing Big Data To The Masses -- 16 Acxiom: Fuelling Marketing With Big Data -- 17 US Immigration and Customs: How Big Data Is Used To Keep Passengers Safe and Prevent Terrorism -- 18 Nest: Bringing the Internet of Things into The Home -- 19 GE: How Big Data Is Fuelling the Industrial Internet -- 20 Etsy: How Big Data Is Used In A Crafty Way -- 21 Narrative Science: How Big Data Is Used To Tell Stories -- 22 BBC: How Big Data Is Used In The Media -- 23 Milton Keynes: How Big Data Is Used To Create Smarter Cities -- 24 Palantir: How Big Data Is Used To Help The CIA And To Detect Bombs In Afghanistan -- 25 Airbnb: How Big Data Is Used To Disrupt the Hospitality Industry -- 26 Sprint: Profiling Audiences Using Mobile Network Data -- 27 Dickey's Barbecue Pit: How Big Data Is Used to Gain Performance Insights Into One Of America's Most Successful Restaurant Chains -- 28 Caesars: Big Data At The Casino -- 29 Fitbit: Big Data In The Personal Fitness Arena -- 30 Ralph Lauren: Big Data In The Fashion Industry -- 31 Zynga: Big Data In The Gaming Industry -- 32 Autodesk: How Big Data Is Transforming The Software Industry -- 33 Walt Disney Parks and Resorts: How Big Data Is Transforming Our Family Holidays -- 34 Experian: Using Big Data To Make Lending Decisions And To Crack Down On Identity Fraud -- 35 Transport for London: How Big Data Is Used To Improve And Manage Public Transport In London -- 36 The US Government: Using Big Data To Run A Country -- 37 IBM Watson: Teaching Computers To Understand And Learn -- 38 Google: How Big Data Is At The Heart Of Google's Business Model -- 39 Terra Seismic: Using Big Data To Predict Earthquakes -- 40 Apple: How Big Data Is At The Centre Of Their Business -- 41 Twitter: How Twitter And IBM Deliver Customer Insights From Big Data -- 42 Uber: How Big Data Is At The Centre Of Uber's Transportation Business -- 43 Electronic Arts: Big Data In Video Gaming -- 44 Kaggle: Crowdsourcing Your Data Scientist -- 45 Amazon: How Predictive Analytics Are Used To Get A 360-Degree View Of Consumers -- Final Thoughts -- About the Author -- Acknowledgements -- Index…”

Tabla de Contenidos: “…Chapter 6: Scripting with Shell and Bash Commands in Linux -- Introducing operating systems -- Navigating system directories -- Introducing basic command-line prompts -- Understanding directory types -- Filing and directory manipulation -- Scripting with Bash -- Introducing control statements -- Creating functions -- Processing data and pipelines -- Using pipes -- Using cron -- Summary -- Chapter 7: Using Git for Version Control -- Introducing repositories (repos) -- Creating a repo -- Cloning an existing remote repository -- Creating a local repository from scratch -- Linking local and remote repositories -- Detailing the Git workflow for data scientists -- Using Git tags for data science -- Understanding Git tags -- Using tagging as a data scientist -- Understanding common operations -- Summary -- Part 3: Exploring Artificial Intelligence -- Chapter 8: Mining Data with Probability and Statistics -- Describing data with descriptive statistics -- Measuring central tendency -- Measuring variability -- Introducing populations and samples -- Defining populations and samples -- Representing samples -- Reducing the sampling error -- Understanding the Central Limit Thereom (CLT) -- The CLT -- Demonstrating the assumption of normality -- Shaping data with sampling distributions -- Probability distributions -- Uniform distribution -- Normal and student's t-distributions -- The binomial distribution -- The Poisson distribution -- Exponential distribution -- Geometric distribution -- The Weibull distribution -- Testing hypotheses -- Understanding one-sample t-tests -- Understanding two-sample t-tests -- Understanding paired sample t-tests -- Understanding ANOVA and MANOVA -- Chi-squared test -- A/B tests -- Understanding Type I and Type II errors -- Type I error (false positive) -- Type II error (false negative) -- Striking a balance -- Summary -- References…”

“…Después de Una historia natural del pensamiento humano, el antropólogo evolutivo Michael Tomasello nos brinda otra contribución seminal.»-New ScientistUna historia natural de la moralidad humana ofrece la explicación más detallada de la evolución de la psicología moral humana. …”