Big data
D3.js Quick Start Guide. Create amazing, interactive visualizations in the browser with JavaScript
Matthew Huntington
D3.js is a JavaScript library that allows you to create graphs and data visualizations in the browser with HTML, SVG, and CSS. This book will take you from the basics of D3.js, so that you can create your own interactive visualizations, to creating the most common graphs that you will encounter as a developer, scientist, statistician, or data scientist.The book begins with an overview of SVG, the basis for creating two-dimensional graphics in the browser. Once the reader has a firm understanding of SVG, we will tackle the basics of how to use D3.js to connect data to our SVG elements. We will start with a scatter plot that maps run data to circles on a graph, and expand our scatter plot to make it interactive. You will see how you can easily allow the users of your graph to create, edit, and delete run data by simply dragging and clicking the graph. Next, we will explore creating a bar graph, using external data from a mock API.After that, we will explore animations and motion with a bar graph, and use various physics-based forces to create a force-directed graph. Finally, we will look at how to use GeoJSON data to create a map.
Dancing with Qubits. How quantum computing works and how it can change the world
Robert S. Sutor
Quantum computing is making us change the way we think about computers. Quantum bits, a.k.a. qubits, can make it possible to solve problems that would otherwise be intractable with current computing technology. Dancing with Qubits is a quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. From there it moves on to a fuller description of classical computing and the mathematical underpinnings necessary to understand such concepts as superposition, entanglement, and interference. Next up is circuits and algorithms, both basic and more sophisticated. It then nicely moves on to provide a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments will affect you.Really understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is introduced and explained thoroughly, in clear English with helpful examples.
Dane grafowe w praktyce. Jak technologie grafowe ułatwiają rozwiązywanie złożonych problemów
Denise Gosnell, Matthias Broecheler
Komputer do pracy potrzebuje liczb i danych. Człowiek chętniej wysnuwa wnioski i wyodrębnia kontekst na podstawie relacji. Te dwa sposoby myślenia są tak odmienne, że komputery do niedawna z trudem wykonywały zadania związane z operowaniem na relacjach. Obecnie może się to zmienić dzięki grafom. Technologie grafowe łączą ludzkie postrzeganie świata i liniową pamięć komputerów. Ich wdrożenie na szerszą skalę będzie stanowić przełom i pozwoli osiągnąć nieznany dziś poziom. Ale najpierw trzeba nauczyć się stosować myślenie grafowe w rozwiązywaniu problemów technicznych. Dzięki tej książce opanujesz podstawy myślenia grafowego. Zapoznasz się z elementarnymi koncepcjami grafowymi: teorią grafów, schematami baz danych, systemami rozproszonymi, a także analizą danych. Dowiesz się również, jak wyglądają typowe wzorce wykorzystania danych grafowych w aplikacjach produkcyjnych. Poznasz sposób, w jaki można te wzorce stosować w praktyce. Pokazano tu, jak używać technik programowania funkcyjnego oraz systemów rozproszonych do tworzenia zapytań i analizowania danych grafowych. Opisano też podstawowe podejścia do proceduralnego przechodzenia przez dane grafowe i ich wykorzystanie za pomocą narzędzi grafowych. W książce: nowy paradygmat rozwiązywania problemów: dane grafowe wzorce wykorzystania danych grafowych przykładowa architektura aplikacji w technologiach relacyjnych i grafowych technologie grafowe a przewidywanie preferencji i zaufania użytkowników filtrowanie kolaboratywne i jego zastosowanie Grafy: przełomowa koncepcja w analizie danych!
David Rojas
Excel 2013 is one of the easiest to use data analysis tools you will ever come across. Its simplicity and powerful features has made it the go to tool for all your data needs. Complex operations with Excel, such as creating charts and graphs, visualization, and analyzing data make it a great tool for managers, data scientists, financial data analysts, and those who work closely with data. Learning data analysis and will help you bring your data skills to the next level.This book starts by walking you through creating your own data and bringing data into Excel from various sources. You’ll learn the basics of SQL syntax and how to connect it to a Microsoft SQL Server Database using Excel’s data connection tools. You will discover how to spot bad data and strategies to clean that data to make it useful to you. Next, you'll learn to create custom columns, identify key metrics, and make decisions based on business rules. You’ll create macros using VBA and use Excel 2013’s shiny new macros. Finally, at the end of the book, you'll be provided with useful shortcuts and tips, enabling you to do efficient data analysis and business modeling with Excel 2013.
Data Analysis for Business Decisions. A Laboratory Manual
Mercury Learning and Information, Andres Fortino
This manual is for business analysts to enhance their statistical analysis skills, with case studies focusing mainly on Excel. It covers basic descriptive techniques, linear regression, forecasting, t-Test, chi-square, A/B testing, text data analysis, and Big Data management. Companion files include solution spreadsheets, sample files, and data sets.The course starts with data shaping and cleaning, installing the Analysis ToolPak, and descriptive statistics. It progresses through histograms, scatter plots, Pareto analysis, correlation, linear and multivariate regression, and forecasting. Advanced topics include inferential statistics, contingency analysis, and A/B testing. The final chapters cover text analytics, big data sets, and data visualization.These techniques are crucial for informed business decisions. This book guides users from basic to advanced analysis, blending theory with practical skills. Companion files enhance learning, making this manual essential for mastering statistical analysis in business.
Cuantum Technologies LLC
Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently.Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling.To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.
Data Analysis with IBM SPSS Statistics. Implementing data modeling, descriptive statistics and ANOVA
James C. Mott, Ken Stehlik-Barry, James Sugrue,...
SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data. The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed. By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.
Prasad Kothari
STATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming.This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you’ll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered.As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You’ll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA.