The role of data visualization in communicating the complex insights hidden inside data is vital. This is becoming more and more important since the audience for data visualizations is also expanding along with the size of data. Data visualizations are now consumed by people from all sorts of professional backgrounds. For the same reason, the ease of consumption is now a hot topic. While data scientists and analysts have an eye for digging out the key insights from even complex visualizations, a top business stakeholder or an average person might not be able to do the same.
And this is what makes effective data visualization the need of the hour. Communicating the data effectively is an art. However, many data scientists lag behind when it comes to the design and aesthetic aspects of visualizing data.
Here are some of the key design principles for creating beautiful and effective data visualizations for everyone. The following principles are from Anderson Data is simply a collection of many individual elements i. In data viz, our goal is usually to group these elements together in a meaningful way to highlight patterns and anomalies.
Described this way, it makes sense that the following principles by Gestalt are a good set of guidelines to assemble different elements into groups FusionCharts How can we achieve this? We must consider several aspects: efficiency, complexity, structure, density and beauty. We also should consider the audience whether they will be confused about the design. Data-ink is the non-erasable core of a graphic, the non-redundant ink arranged in response to variation in the numbers represented.
Erase non-data-ink and redundant data-ink. Source: Tufte Source: Plotly Always revise and edit Source: Tufte This rule states that a visulaization should contain as much data as possible while also using as little pixels as possible.Cloud Computing Fundamentals
Through a comprehensive editing and testing process, any visualization can continually be improved upon. The main stakeholder of any visualization is the audience and their ability to understand what the visualization is trying to get across. Although if an audience member is not able to understand the visualization, there is nothing lost, but for those that do understand it, something is gained. It is a great feat for an audience member to be able to understand a statistical graphic because it is the most frequently made mistake in underestimating an audience.Pretend you are teaching a differential equations class, and you have reached the classic Vibrating Strings Problem.
To make the class come alive, you want to show them strings that vibrate. Unfortunately, strings - at the sizes available to you - vibrate far too quickly for your phone to pick up well, and someone is already using the long string that the physics department lends out for this purpose. Don't worry, I'm not going to make you solve a differential equation at least, not until SymPy - and then you'll have some help.
This is a great problem to wow your students because - with a stationary start - it has a linear solution. The initial velocity of the string is uniformly 0. Don't worry about automatically testing this function - matplotlib behaves strangely and is very hard to test reliably. Graded Exercise: Upload your. In addition, upload a.
The function must be continuous and satisfy the initial conditions, and should not be identically 0, but otherwise is up to you! You can dramatically increase your efficiency by computing f x only once at each value, storing the result in numpy arrays, and slicing. A very useful result for this is numpy.
This kind of picture is all well and good for a textbook, but on the web - and in student imagination - animation is king. You can the solution I am partial to simply generate a bunch of image files and stitch them together. This is fast, clean, and gives you the most versatility - but requires the use of an external program such as ImageMagick.
With ImageMagick convert, you can turn a bunch of png s into a looped gif with:. Alternately, you can animate within MatPlotLib. There are many tutorials for this, but the basic concept is to create an animate i function that takes a frame number and changes the figure to match. Note that this depends on an esoteric stack of installed codecs and libraries; you may have to install the codec pack ffmpegpython library Pillowand one or more other things. This is one reason I favor the folder-of-images approach with imagemagick.
Department of Mathematics. College of Science. People Graduate Students Clinton Bradford. What you are going to do is show them the solutions using Python.Due to heightened concerns regarding the outbreak of COVID, we are adding more instructor-led online training courses as an alternative to classroom courses.
Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:. See detailed course outline. Get an overview of course themes.
Objective: Enter MATLAB commands, with an emphasis on creating variables, accessing and manipulating data in variables, and creating basic visualizations. Objective: Perform mathematical and statistical calculations with vectors. Organize scripts into logical sections for development, maintenance, and publishing. Objective: Use matrices as mathematical objects or as collections of vector data. Objective: Organize table data for analysis.
Objective: Perform typical data analysis tasks in MATLAB, including importing data from files, preprocessing data, fitting a model to data, and creating a customized visualization of the model. Objective: Create flexible code that can interact with the user, make decisions, and adapt to different situations.
Objective: Increase automation by encapsulating modular tasks as user-defined functions. When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason. The pricing applies for purchase and use in Russian Federation, For pricing in other regions Contact Sales.
The product price does not include sales, use, excise, value-added, or other taxes. Any applicable taxes, duties, levies, assessments and governmental charges payable in connection with this purchase will be assessed on the order. Refer to Training Policies for more information. You are eligible for discounted academic pricing when you use MATLAB and Simulink for teaching, academic research, or for meeting course requirements at a degree granting institution. You are not eligible for academic pricing when you use MATLAB and Simulink at a commercial or government lab, or for other commercial or industrial purposes.
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance.
Other MathWorks country sites are not optimized for visits from your location. Toggle Main Navigation. Toggle navigation. Contact Training. Course Schedule Prerequisites. Undergraduate-level mathematics and experience with basic computer operations. This course is also offered in an online, self-paced format. Details Launch. Topics include: Working with the MATLAB user interface Entering commands and creating variables Analyzing vectors and matrices Visualizing vector and matrix data Working with data files Working with data types Automating commands with scripts Writing programs with branching and loops Writing functions.
Reading data from files Saving and loading variables Plotting data Customizing plots Exporting graphics for use in other applications Variables and Commands Objective: Enter MATLAB commands, with an emphasis on creating variables, accessing and manipulating data in variables, and creating basic visualizations.Documentation Help Center. Parallel computing can help you to solve big computing problems in different ways. If you have big data, you can scale up using distributed arrays or datastore.
You can also execute a task without waiting for it to complete, using parfevalso that you can carry on with other tasks. You can use different types of hardware to solve your parallel computing problems, including desktop computers, GPUs, clusters, and clouds.
Choose a Parallel Computing Solution. Take advantage of parallel computing resources without requiring any extra coding. Interactively Run a Loop in Parallel Using parfor. Convert a slow for -loop into a faster parfor -loop. Plot During Parameter Sweep with parfor.
This example shows how to perform a parameter sweep in parallel and plot progress during parallel computations. Scale Up from Desktop to Cluster. Run Batch Parallel Jobs. Process Big Data in the Cloud.
This example shows how to access a large data set in the cloud and process it in a cloud cluster using MATLAB capabilities for big data. Evaluate Functions in the Background Using parfeval. What Is Parallel Computing? Run Code on Parallel Pools. Learn about starting and stopping parallel pools, pool size, and cluster selection. With Parallel Computing Toolbox, you can run your parallel code in different parallel environments, such as thread-based or process-based environments.
Quick Success with parfor. Benchmarks the parfor construct by repeatedly playing the card game of blackjack, also known as We simulate a number of players that are independently playing thousands of hands at a time, and display payoff statistics.Please leave this field empty. Technology is changing rapidly with new inventions each passing day. Cloud Computing is one such technology trending these days. Thus it is an internet based technology.
Cloud Computing is an internet based service that provides on demand access to shared computer resources and data.
In other words, cloud computing means delivery of hosted services and resources over the internet. These resources include storage, server, network and applications. These resources can be quickly equipped and can be freed easily. Virtualization is the driving force behind cloud computing.
Virtualization means the creation of virtual resources rather than actual resources like hardware, operating system, storage, network etc. These virtual resources act as a replica of the actual resources. The common example of virtualization is the partition of the hard drive to create separate hard drives. In virtualizationthere are two types of machines host machine and the guest machine. Host machine is the real physical machine on which virtualization takes place while the guest machine is the virtual machine.
Following are the types of virtualization:. Infrastructure as a Service provides virtual computing resources such as storage, server, virtual machines and network over the internet. A third party provider provides this infrastructure to the consumer.
Platform as a Service allows the consumer to deploy applications on the cloud infrastructure. A consumer is not responsible for the underlying cloud infrastructure. Software as a Service enables the user to use the deployed applications running on the cloud infrastructure.
The consumer is not responsible for managing the underlying infrastructure and the applications. Private Cloud is provided for a single organization with multiple consumers. It is operated by the organization itself or by a third party. It may be operated within the premises or off the premises. Public Cloud is for public use or a large organization and is owned by a third party provider selling cloud services. Hybrid Cloud is a combination of two or more distinct clouds that retain their unique entity.
It is used for sensitive data and strategic applications. It is a type of private cloud for users with specific demands. It has several stakeholders. It is managed by the organization or by a third party provider.
Cloud Computing Services are rapidly changing with new advancements each day. It is important for each internet user to know the use of this technology. Cloud Computing subject should be mandatory in the curriculum of computer science and information technology engineering streams. It is also beneficial in higher studies of computer science and information technology. Tech thesis in Cloud Computing is also a good choice for scholars. Therefore it should be reached to wider internet users.
Cloud Computing security refers to set of policies and measures deployed to protect cloud infrastructure and the underlying data and applications. It is a part of information security. Cloud Computing Security is needed as there are certain security concerns regarding security and privacy of the users and their data.
Cloud Computing gives users the ability to store and process data at third-party data centers.Metabolomics provides a wealth of information about the biochemical status of cells, tissues, and other biological systems. However, for many researchers, processing the large quantities of data generated in typical metabolomics experiments poses a formidable challenge.
Robust computational tools are required for all data processing steps, from handling raw data to high level statistical analysis and interpretation. This chapter describes several established methods for processing and analyzing metabolomics data within the R statistical programming environment. The focus is on processing LCMS data but the methods can be applied virtually to any analytical platform. We provide a step-by-step workflow to demonstrate how to integrate, analyze, and visualize LCMS-based metabolomics data using computational tools available in R.
These concepts and methods will allow specialists and nonspecialists alike to develop and evaluate their own data more critically. Metabolomics - Fundamentals and Applications.
Metabolomics is a rapidly growing discipline focusing on the global study of small molecule metabolites in biological systems. Through the characterization of metabolite dynamics, interactions, and responses to genetic or environmental perturbations, metabolomics can provide a comprehensive picture of both baseline physiology and global biochemical responses to genetic, abiotic, and biotic factors [ 1 ].
As the diversity in abundance and chemical properties of metabolites varies greatly in organisms, a range of analytical techniques must be utilized to survey the entire metabolome.
A number of methods have been developed for the extraction, detection, identification, and quantification of the metabolome [ 2 ]. Mass spectrometry coupled with gas chromatography GC-MS or liquid chromatography LC-MS are the most common analytical platforms, although capillary electrophoresis mass spectrometry CE-MS and nuclear magnetic resonance NMR are also widely used in metabolomics research [ 3 — 6 ].
Since metabolomics experiments typically produce large amounts of data, sophisticated bioinformatic tools are needed for efficient and high-throughput data processing to remove systematic bias and to explore biologically significant findings.
Both multivariate statistical analysis and data visualization play a critical role in extracting relevant information and interpreting the results of metabolomics experiments. The data generated in a metabolomics experiment generally can be represented as a matrix of intensity values containing N observations samples of K variables peaks, bins, etc. Additional information, such as experimental group, genotype, time point, gender, etc.
For multivariate analysis, very few mathematical constraints are placed on the values contained in the data matrix. Therefore, a common set of statistical tools can be used to analyze metabolomics data of almost any type.
However, as discussed below, multiple preprocessing steps are often necessary to yield interpretable results [ 78 ]. The focus of this chapter is on describing methods for processing and visualizing metabolomics data obtained by liquid chromatography mass spectrometry LCMS.
LCMS is the most widely used method in metabolomics research due to its dynamic range, coverage, ease of sample preparation, and high information content [ 3 — 5 ]. We present a standard workflow for handling LCMS data, from raw data processing to downstream statistical analysis using open source tools available within the R software environment.
R is a software environment for statistical computing, data analysis, and graphics, which has become an essential tool in all areas of bioinformatics research.
A major advantage of R over commercial software is that it is open source and free to all users. In addition to being a popular language for performing high level statistics, R has a wide array of graphical tools that make it an ideal environment for exploratory data analysis and generating publication quality figures. All work is done using the command line-based text functions with user-defined scripts. Although R can be challenging for new users, it is quite flexible once the basic commands, functions, and data structures have been learned.
A detailed description of every function with examples can be obtained by typing help followed by the name of the function, i.
In addition, there are ample online resources to help users learn the basics of R as well as solve a wide range of common data analysis problems [ 9 — 11 ].
R has a powerful set of functions for creating graphics, from fairly simple graphs using base graphics commands to highly sophisticated graphs using the one of several advanced graphics packages [ 12 ]. The focus of this chapter is on using the ggplot2 package, a high-level platform for creating graphics that is especially powerful for working with high-dimensional data [ 13 ]. The basic idea in ggplot2 is to build graphs by adding successive layers that include visual representations as well as statistical summaries of the data [ 14 ].MATLAB is an integrated technical computing environment from the MathWorks that combines array-based numeric computation, advanced graphics and visualization, and a high-level programming language.
Separately licensed toolboxes provide additional domain-specific functionality. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:. This workhop uses selected topics from the online self-paced course from the MathWorks in the link below.
Participants should have a Mathworks account in order to access the links in this document. Matlab Academy:Fundamentals of Matlab. Video: Fundamentals of Matlab. Quick Reference Guide used for presentation. Exercise: Order of Operations. Exercise: Change Output Display. Video: Import Tool.
Exercise: Using the Variable Editor. Exercise: Saving Modifications. Exercise: Open and Use Function Documentation. Exercise: Plotting Gasoline Prices. Exercise: Plot Options. Exercise: Axis Labels and Title. Exercise: More Plot Annotation Options. Exercise: Modify Axis Properties.
Exercise: Modifying and Running Scripts. Exercise: International Gasoline Prices. Exercise: Creating Vectors. Exercise: Creating Matrices. Exercise: Use Colon Operator and Linspace. Exercise: Concatenating Arrays. Exercise: Creating and Concatenating Arrays. Exercise: Creating Arrays.
Exercise: Creating Arrays of Random Numbers. Exercise: Reshaping a Matrix. Exercise: Overall Average Electricity Revenue. Exercise: Accessing and Modifying Vector Values. Exercise: Index Using Variables and Keywords. Exercise: Index with Vectors. Exercise: Modify Multiple Elements. Exercise: Row,Column Indexing.
Exercise: Extract a Single Element. Exercise: Matrix Indexing with Vectors.