Today, Jesse Marshall returns to discuss visualization in Matlab.
Did you ever think you could make plots like these in MATLAB? Neither did I!
Smartphones, tablets, wearables, smart-toasters and 8 billion other ‘devices’ have swamped us in data. This explosion of big, and sometimes bad data has led to seemingly endless charts that all compete for our rapidly shrinking attention. Scientists and statisticians are now tasked with explaining complex analyses and subtle concepts rapidly, in a compact, meaningful and compelling way. This means maximizing the perceptual differences of the points you want to make in the graphics so you can minimize the time a reader has to spend digesting your analysis.
Ariane 5 first launched rocket famously exploded because of a numerical error – Source : Wikipedia
I would like today to talk about one very important concept that is often overlooked when you learn to use a computer for data analysis : Rounding errors. In Matlab as in other languages, numbers are represented as a series of 0 and 1 in a way that depends on its type (double or int16, for instance). Each number type is inherently designed to provide a certain precision. Because of this, making mathematical operations on these numbers behave differently than what you expect from simple mathematical formulas. In this post, I hope to allow you to identify these behaviors and avoid all associated problems.
What is Matlab All About? – Source: Image
If you’re reading this, you must be interested in learning what Matlab is, Lucky for you, Matlabtips is a fantastic resource to aid in learning the Matlab language and the multitude of functionality that comes with it. But first, we must answer these simple but important questions :
What is Matlab? Why would you use it? Where is it commonly used?
No need to buy a book to learn Matlab – Source : Image
While we here at Matlabtips appreciate you visiting our blog, we would like to take one post and introduce a few other great online resources for beginners and advanced users alike to learn more about Matlab. We hope that you can use other sites for help when you are stuck or to just pick up some new Matlab skill.
Today, we are lucky to have Jesse Marshall as a guest blogger on data analysis. Jesse is a PhD student at Stanford, working on analyzing the collective behavior of hundreds of neurons as they process information in the living brain.
Humans have trouble accepting randomness. We search for patterns, trends and correlations in all aspects of life, and we do it for good reason. Identifying patterns lets us learn from our mistakes and predict the future. Clustering algorithms are exploratory data analysis tools used to categorize a set of observations into a few discrete classes, or, clusters, that share common features. Cluster algorithms only need four ingredients: a set of objects,a list of quantitative or qualitative descriptors of these objects, and a metric that you can use to compare these objects, based on their features. Then you try to divide the data into a specified number of clusters and see what happens.
Try and Catch! – Source: Image
Typically when using Matlab, if the program encounters some bug, you will hear the system bell and see the dreaded red text show up in the command window. So what do you do if you absolutely need your programs to run without error? Having a program crash is unacceptable. You must implement error handling within your code.
Every variable has a scope or is it the opposite? – Source: Image
For this post, we are glad to welcome Nicholas as a contributor to Matlabtips.com. If you are interested in writing a guest post, please don’t hesitate to contact us.
In this post, we talk about variables. In particular, we will explain where variables “sit” in memory depending on where you declared it (within a script or a function). This is a very important post if you are learning the language.