Tabla de Contenidos: “…-- Identifying effective visualizations -- Scatter plots -- Line graphs -- Bar charts -- Histograms -- Box plots -- When graphs and statistics lie -- Correlation versus causation -- Simpson's paradox -- If correlation doesn't imply causation, then what does? …”

Tabla de Contenidos: “…-- Line charts -- Scatter plots -- Box plots -- Advanced visualization technique -- Prefuse -- IVTK Graph toolkit -- Other libraries -- Summary -- Chapter 4: Basics of Machine Learning -- What is machine learning? …”

Tabla de Contenidos: “…One‐Tailed t‐Tests -- 5.2.2 Rank‐Based Two‐Sample Tests -- 5.3 One‐Sample Tests -- 5.4 Power Analyses and Sample Size Determination -- Chapter 6 Communicating Quantitative Information Using Visuals -- 6.1 The Fundamentals of Scientific Plotting -- 6.2 Scatter Plots -- 6.3 Line Plots -- 6.4 Box Plots and Bar Plots -- 6.5 Multipanel Plots and Plotting Regions -- 6.6 Adding Text, Formulae, and Colour -- 6.7 Interaction Plots -- 6.8 Images, Colour Contour Plots, and 3D Plots -- 6.8.1 Adding Images to Plots -- 6.8.2 Colour Contour Plots -- References -- Chapter 7 Working with Categorical Data -- 7.1 Tabling and Visualising Categorical Data -- 7.2 Contingency Tables -- 7.3 The Chi‐squared Test -- 7.4 Decision Trees -- 7.5 Optimising Decision Trees -- References -- Chapter 8 Working with Continuous Data -- 8.1 Covariance -- 8.2 Correlation Coefficient -- 8.3 Transformations -- 8.4 Plotting Correlations -- 8.5 Correlation Tests -- References -- Chapter 9 Linear Regression -- 9.1 Basics and Simple Linear Regression -- 9.1.1 Making Sense of the summary Output for Regression Models Fitted with lm -- 9.1.2 Model Diagnostics -- 9.1.3 Model Predictions and Visualisation -- 9.1.4 What to Write in a Report or Paper? …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Laying the Foundation for Reproducible Data Analysis -- Introduction -- Setting up Anaconda -- Installing the Data Science Toolbox -- Creating a virtual environment with virtualenv and virtualenvwrapper -- Sandboxing Python applications with Docker images -- Keeping track of package versions and history in IPython Notebook -- Configuring IPython -- Learning to log for robust error checking -- Unit testing your code -- Configuring pandas -- Configuring matplotlib -- Seeding random number generators and NumPy print options -- Standardizing reports, code style, and data access -- Chapter 2: Creating Attractive Data Visualizations -- Introduction -- Graphing Anscombe's quartet -- Choosing seaborn color palettes -- Choosing matplotlib color maps -- Interacting with IPython Notebook widgets -- Viewing a matrix of scatterplots -- Visualizing with d3.js via mpld3 -- Creating heatmaps -- Combining box plots and kernel density plots with violin plots -- Visualizing network graphs with hive plots -- Displaying geographical maps -- Using ggplot2-like plots -- Highlighting data points with influence plots -- Chapter 3: Statistical Data Analysis and Probability -- Introduction -- Fitting data to the exponential distribution -- Fitting aggregated data to the gamma distribution -- Fitting aggregated counts to the Poisson distribution -- Determining bias -- Estimating kernel density -- Determining confidence intervals for mean, variance, and standard deviation -- Sampling with probability weights -- Exploring extreme values -- Correlating variables with Pearson's correlation -- Correlating variables with the Spearman rank correlation -- Correlating a binary and a continuous variable with the point biserial correlation…”

Tabla de Contenidos: “…. -- Plotting a bar chart -- Getting ready -- How to do it... -- Plotting box plots or whisker diagrams -- Getting ready -- How to do it... -- Plotting scatter plots -- Getting ready -- How to do it... -- Plotting donut plots -- Getting ready -- How to do it... -- Plotting area graphs -- Getting ready -- How to do it... -- Index…”

Tabla de Contenidos: “…Arithmetic operations -- Logical operations -- Data loading and working with different file formats -- Transforming a DataFrame into another file format -- Summary -- Chapter 4: Data Analysis, Wrangling, and Transformation -- Technical requirements -- Transforming data -- Replacing missing values -- Removing duplicates -- Data transformation with the map function -- Data transformation with the apply function -- Filtering and querying -- Random sampling -- Encoding DataFrames and Series -- Combining datasets -- DataFrame merge -- Data concatenation -- Series data accessors -- Calculating statistics -- Calculating statistics by axis -- Summary -- Chapter 5: Data Visualization with Plotly.js -- Technical requirements -- A brief primer on Plotly.js -- Using Plotly.js via a script tag -- Fundamentals of Plotly.js -- Data format -- Configuration options for plots -- Plotly layout -- Creating basic charts with Plotly.js -- Creating statistical charts with Plotly.js -- Creating histogram plots with Plotly.js -- Creating box plots with Plotly.js -- Creating violin plots with Plotly.js -- Summary -- Chapter 6: Data Visualization with Danfo.js -- Technical requirements -- Setting up Danfo.js for plotting -- Adding Danfo.js to your code -- Downloading a dataset for plotting -- Creating line charts with Danfo.js -- Creating scatter plots with Danfo.js -- Creating box and violin plots with Danfo.js -- Making box and violin plots for a Series -- Box and violin plots for multiple columns -- Box and violin plots with specific x and y values -- Creating histograms with Danfo.js -- Creating a histogram from a Series -- Creating a histogram from multiple columns -- Creating bar charts with Danfo.js -- Creating a bar chart from a Series -- Creating a bar chart from multiple columns -- Summary -- Chapter 7: Data Aggregation and Group Operations -- Technical requirements…”

Tabla de Contenidos: “…Using user-defined functions -- Breaking down the user-defined function syntax -- Doing "stuff" with user-defined functions -- Getting familiar with lambda functions -- Creating good functions -- Handling files in Python -- Opening files with pandas -- Wrangling data with pandas -- Handling missing data -- Selecting data -- Sorting data -- Merging data -- Aggregation with groupby() -- Summary -- References -- Chapter 4: Visualizing Data and Data Storytelling -- Understanding data visualization -- Bar charts -- Line charts -- Scatter plots -- Histograms -- Density plots -- Quantile-quantile plots (Q-Q plots) -- Box plots -- Pie charts -- Surveying tools of the trade -- Power BI -- Tableau -- Shiny -- ggplot2 (R) -- Matplotlib (Python) -- Seaborn (Python) -- Developing dashboards, reports, and KPIs -- Developing charts and graphs -- Bar chart - Matplotlib -- Bar chart - Seaborn -- Scatter plot - Matplotlib -- Scatter plot - Seaborn -- Histogram plot - Matplotlib -- Histogram plot - Seaborn -- Applying scenario-based storytelling -- Summary -- Chapter 5: Querying Databases with SQL -- Introducing relational databases -- Mastering SQL basics -- The SELECT statement -- The WHERE clause -- The ORDER BY clause -- Aggregating data with GROUP BY and HAVING -- The GROUP BY statement -- The HAVING clause -- Creating fields with CASE WHEN -- Analyzing subqueries and CTEs -- Subqueries in the SELECT clause -- Subqueries in the FROM clause -- Subqueries in the WHERE clause -- Subqueries in the HAVING clause -- Distinguishing common table expressions (CTEs) from subqueries -- Merging tables with joins -- Inner joins -- Left and right join -- Full outer join -- Multi-table joins -- Calculating window functions -- OVER, ORDER BY, PARTITION, and SET -- LAG and LEAD -- ROW_NUMBER -- RANK and DENSE_RANK -- Using date functions -- Approaching complex queries -- Summary…”

Tabla de Contenidos: “…. -- There is more... -- Generating box plots for two variables -- Getting ready -- How to do it... -- How it works... -- Creating histograms on two variables -- Getting ready -- How to do it... -- How it works... -- Analyzing two variables using a correlation analysis -- Getting ready -- How to do it... -- How it works... -- Chapter 6: Performing Multivariate Analysis in Python -- Technical requirements -- Implementing Cluster Analysis on multiple variables using Kmeans -- Getting ready -- How to do it... -- How it works... -- There is more... -- See also... -- Choosing the optimal number of clusters in Kmeans -- Getting ready -- How to do it... -- How it works... -- There is more... -- See also... -- Profiling Kmeans clusters -- Getting ready -- How to do it... -- How it works... -- There's more... -- Implementing principal component analysis on multiple variables -- Getting ready -- How to do it... -- How it works... -- There is more... -- See also... -- Choosing the number of principal components -- Getting ready -- How to do it... -- How it works... -- Analyzing principal components -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also... -- Implementing factor analysis on multiple variables -- Getting ready…”

Tabla de Contenidos: “…. -- Generating histograms and box plots -- Getting ready -- How to do it... -- How it works... -- Creating three-dimensional plots -- Getting ready -- How to do it... -- How it works... -- Generating interactive displays in the Jupyter Notebook -- Getting ready -- How to do it... -- How it works... -- Object-oriented graph creation using Artist objects -- Getting ready -- How to do it... -- How it works... -- Creating a map with cartopy -- Getting ready -- How to do it... -- How it works... -- Chapter 4: Data Wrangling with pandas -- Creating Series objects -- Getting ready -- How to do it... -- How it works... -- Creating DataFrame objects -- Getting ready -- How to do it... -- How it works... -- Inserting and deleting columns to a DataFrame -- Getting ready -- How to do it... -- How it works... -- Inserting and deleting rows to a DataFrame -- Getting ready -- How to do it... -- How it works... -- Selecting items by row indexes and column labels -- Getting ready -- How to do it... -- How it works... -- Selecting items by integer location -- Getting ready -- How to do it... -- How it works... -- Selecting items using mixed indexing -- Getting ready -- How to do it... -- How it works... -- Accessing, selecting, and modifying data -- Getting ready -- How to do it... -- How it works... -- Selecting rows using Boolean selection -- How to do it... -- Reading and storing data in different formats -- Getting ready -- How to do it... -- Working with CSV, text/tabular, and format data -- How it works... -- Reading a CSV file into a DataFrame -- Specifying the index column when reading a CSV file -- Reading and writing data in Excel format -- Reading and writing JSON files -- Reading HTML data from the web -- Accessing CSV data on the web…”

“…Line charts, ellipses, box plots, and maps are just a few of the graphs available in Graph Builder. …”

“…Line charts, ellipses, box plots, and maps are just a few of the graphs available in Graph Builder. …”

“…Line charts, ellipses, box plots, and maps are just a few of the graphs available in Graph Builder. …”

Tabla de Contenidos: “…-- 3.3.7 Population Variance -- 3.3.8 Degrees of Freedom -- 3.4 What to Measure-Distance (Nearness) -- 3.5 Test Statistic -- 3.5.1 Test Statistic for this Study -- 3.6 Examining and Displaying the Data -- 3.6.1 Frequency Tables -- 3.6.2 Histograms -- 3.6.3 Bar Chart -- 3.6.4 Box Plots -- 3.6.5 Tails and Skew -- 3.6.6 Errors and Outliers Are Not the Same Thing! …”

“…The first biostatistical book to focus on software, Biostatistics Using JMP discusses such topics as data visualization, data wrangling, data cleaning, histograms, box plots, Pareto plots, scatter plots, hypothesis tests, confidence intervals, analysis of variance, regression, curve fitting, clustering, classification, discriminant analysis, neural networks, decision trees, logistic regression, survival analysis, control charts, and metaanalysis. …”

“…Use R’s default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and other examples Provide annotations to help viewers interpret data Control the overall appearance of graphics Render data groups alongside each other for easy comparison Use colors in plots Create network graphs, heat maps, and 3D scatter plots Structure data for graphing…”

“…You'll see how to use the plots available in Optimus such as histogram, frequency charts, and scatter and box plots, and understand how Optimus lets you connect to libraries such as Plotly and Altair. …”

“…With this application, you can use the interactive drag-and-drop feature to create many graphs, including histograms, box plots, scatter plot matrices, classification panels, and more. …”

“…What you will learn Use basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control Import data to R from various formats such as CSV, Excel, and SQL Clean data by handling missing values and standardizing fields Perform univariate and bivariate analysis using ggplot2 Create statistical summary and advanced plots such as histograms, scatter plots, box plots, and interaction plots Apply data management techniques, such as factoring, pivoting, aggregating, merging, and dealing with missing values, on example datasets Who this book is for R Programming Fundamentals is for you if you are an analyst who wants to grow in the field of data science and explore the latest tools…”

“…This guide will help you Explore and understand the data visualization technology stack Master the thought process and steps involved in importing data Extract, transform, and load data in usable, reliable form Handle spotty data, or data that doesn’t line up with what your chart expects Use D3 to build pie and bar charts, scatter and box plots, and more Work effectively with time-series data Tweak Ruby and SQL to optimize performance with large datasets Use raw SQL in Rails: window functions, subqueries, and common table expressions Build chord diagrams and time-series aggregates Use separate databases or schema for reporting databases Integrate geographical data via geospatial SQL queries Construct maps with Leaflet and Rails Query geospatial data the “Rails way” and the “raw SQL way”…”