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| Title & Speakers | Event |
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Data visualization (Part II)
2024-02-22 · 17:00
Join us for a 3-part workshop series meant to introduce you to R and RStudio. We will walk through the basics of how to handle data in R and how you can create data visualizations! Please note: There is one meetup link per workshop, please register for each workshop separately. 1. Introduction to R and RStudio Date: January 25th, 2024 at 12:00-1:00pm In this session, Reiko will go through R vocabulary; creating clear, organized scripts; using GitHub repositories for sharing scripts; creating a project; importing data; and troubleshooting. 2. Data visualization (Part I) Date: February 1st, 2024 at 12:00-1:00pm In this session, Reiko will introduce R Markdown and the package ggplot2. You will see how to create the following data visualizations:
3. Data visualization (Part II) Date: February 22nd, 2024 at 12:00-1:00pm In this session, Reiko will continue the topic of data visualizations. Using bar plots, you will see how to change colors and overall look and feel of a plot as well as exploring more plot types and how to save our masterpieces! |
Data visualization (Part II)
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Data visualization (Part I)
2024-02-01 · 17:00
Join us for a 3-part workshop series meant to introduce you to R and RStudio. We will walk through the basics of how to handle data in R and how you can create data visualizations! Please note: There is one meetup link per workshop, please register for each workshop separately. 1. Introduction to R and RStudio Date: January 25th, 2024 at 12:00-1:00pm In this session, Reiko will go through R vocabulary; creating clear, organized scripts; using GitHub repositories for sharing scripts; creating a project; importing data; and troubleshooting. 2. Data visualization (Part I) Date: February 1st, 2024 at 12:00-1:00pm In this session, Reiko will introduce R Markdown and the package ggplot2. You will see how to create the following data visualizations:
3. Data visualization (Part II) Date: February 22nd, 2024 at 12:00-1:00pm In this session, Reiko will continue the topic of data visualizations. Using bar plots, you will see how to change colors and overall look and feel of a plot as well as exploring more plot types and how to save our masterpieces! |
Data visualization (Part I)
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Introduction to R and RStudio
2024-01-25 · 17:00
Join us for a 3-part workshop series meant to introduce you to R and RStudio. We will walk through the basics of how to handle data in R and how you can create data visualizations! Please note: There is one meetup link per workshop, please register for each workshop separately.
In this session, Reiko will go through R vocabulary; creating clear, organized scripts; using GitHub repositories for sharing scripts; creating a project; importing data; and troubleshooting.
In this session, Reiko will introduce R Markdown and the package ggplot2. You will see how to create the following data visualizations:
In this session, Reiko will continue the topic of data visualizations. Using bar plots, you will see how to change colors and overall look and feel of a plot as well as exploring more plot types and how to save our masterpieces! |
Introduction to R and RStudio
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Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientists Aimed at undergraduate/graduate-level engineering and natural science students, this timely, fully updated edition of a popular book on statistics and probability shows how real-world problems can be solved using statistical concepts. It removes Excel exhibits and replaces them with R software throughout, and updates both MINITAB and JMP software instructions and content. A new chapter discussing data mining—including big data, classification, machine learning, and visualization—is featured. Another new chapter covers cluster analysis methodologies in hierarchical, nonhierarchical, and model based clustering. The book also offers a chapter on Response Surfaces that previously appeared on the book’s companion website. Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP, Second Edition is broken into two parts. Part I covers topics such as: describing data graphically and numerically, elements of probability, discrete and continuous random variables and their probability distributions, distribution functions of random variables, sampling distributions, estimation of population parameters and hypothesis testing. Part II covers: elements of reliability theory, data mining, cluster analysis, analysis of categorical data, nonparametric tests, simple and multiple linear regression analysis, analysis of variance, factorial designs, response surfaces, and statistical quality control (SQC) including phase I and phase II control charts. The appendices contain statistical tables and charts and answers to selected problems. Features two new chapters—one on Data Mining and another on Cluster Analysis Now contains R exhibits including code, graphical display, and some results MINITAB and JMP have been updated to their latest versions Emphasizes the p-value approach and includes related practical interpretations Offers a more applied statistical focus, and features modified examples to better exhibit statistical concepts Supplemented with an Instructor's-only solutions manual on a book’s companion website Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP is an excellent text for graduate level data science students, and engineers and scientists. It is also an ideal introduction to applied statistics and probability for undergraduate students in engineering and the natural sciences. |
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Ed McCarthy
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Graduate from Excel to MATLAB® to keep up with the evolution of finance data Foundations of Computational Finance with MATLAB® is an introductory text for both finance professionals looking to branch out from the spreadsheet, and for programmers who wish to learn more about finance. As financial data grows in volume and complexity, its very nature has changed to the extent that traditional financial calculators and spreadsheet programs are simply no longer enough. Today’s analysts need more powerful data solutions with more customization and visualization capabilities, and MATLAB provides all of this and more in an easy-to-learn skillset. This book walks you through the basics, and then shows you how to stretch your new skills to create customized solutions. Part I demonstrates MATLAB’s capabilities as they apply to traditional finance concepts, and PART II shows you how to create interactive and reusable code, link with external data sources, communicate graphically, and more. Master MATLAB’s basic operations including matrices, arrays, and flexible data structures Learn how to build your own customized solutions when the built-ins just won’t do Learn how to handle financial data and industry-specific variables including risk and uncertainty Adopt more accurate modeling practices for portfolios, options, time series, and more MATLAB is an integrated development environment that includes everything you need in one well-designed user interface. Available Toolboxes provide tested algorithms that save you hours of code, and the skills you learn using MATLAB make it easier to learn additional languages if you choose to do so. Financial firms are catching up to universities in MATLAB usage, so this is skill set that will follow you throughout your career. When you’re ready to step into the new age of finance, Foundations of Computational Finance with MATLAB provides the expert instruction you need to get started quickly. |
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Data Visualization, Volume II
2017-03-07
Dr. Amar Sahay
– author
This book discusses data and information visualization techniques-the decision-making tools with applications in health care, finance, manufacturing engineering, process improvement, product design, and others. These tools are an excellent means of viewing the current state of the process and improving them. The initial chapters discuss data analysis, the current trends in visualization, the concepts of systems and processes from which data are collected. The second part is devoted to quality tools-a set of graphical and information visualization tools in data analysis, decision-making, and Lean Six-Sigma quality. The eight basic tools of quality discussed are the Process Maps, Check Sheets, Histograms, Scatter Diagrams, Run Charts, Control Charts, Cause-and-Effect Diagrams, and Pareto Charts. The new quality tools presented are the Affinity, Tree, and Matrix Diagrams, Interrelationship Digraph, Prioritizing Matrices, Process Decision Program Chart, and Activity Network Diagram along with Quality Function Deployment (QFD) and Multivari Charts. |
O'Reilly Data Visualization Books
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