Data Science, Machine Learning, AI & Analytics
Plotly's dash framework allows the user to build high quality interactive visualizations. This article provides a high level overview of the process.
Analyse to Visualise, for everything you need data, a correct #data #Outsource Data Analytics to WEchartered #wechartered #dataanalysis
How to design powerful dashboards to investigate trends or give an overview of the performance. Which type of charts to use to visualise data.
An essential part of data analysis is communication. We need to arrange information in a comfortable and digestible way to communicate, highlight and visualise critical areas.
Digital Balanced scorecard software (BSC) help organisations measure, analyse and visualise business performance using financial and non-financial data
Visualise Data in Power Pivot for Excel or SSAS with DAX. 70-779, 70-768 exams. Business Intelligence PowerPivot tool.
Pernilla Ohrstedt's exhibition design will visualise 3D data captured by driverless cars, which she says will create a "perfect" digital model of the world.
Design consultancy Manual worked with the brand to visualise large amounts of fitness data, creating an engaging narrative for athletes in a challenging year for the fitness community.
The challenge was to visualise data on the Earth's non-renewable resources. Stuff like tin, aluminium, gold coal and oil - the stuff that we can't replace. Our design was selected from around 200 entries to be included in a shortlist of 10.The brief was…
At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers…
This tweet by mikefc alerted me to a mind-blowingly simple but amazing trick using the ggplot2 package: to visualise data for different groups in a facetted plot with all of the data plotted in the background. Here’s an example that we’ll learn to make in this post so you know what I’m talking about: Credit where credit’s due Before continuing, I’d be remiss for not mentioning that the origin of this ingenious suggestion is Hadley Wickham. The tip comes in his latest ggplot book, for which hardcopies are available online at places like Amazon, and the code and text behind it are freely available on Hadley’s Github at this repository. Some motivating examples Let’s start with some examples that explain just why I’m so excited about this trick. Consider wanting to plot the results shown in the example above. That is, for the iris data set (that comes with R), we want to plot a histogram of the Sepal.Width variable, but separately for each flower Species. Let’s start by creating the histogram of all the data: library(ggplot2) ggplot(iris, aes(x = Sepal.Width)) + geom_histogram() So how can we present this separately for each of the flower Species? Well, here are two possibilities… Coloured stacking via fill: ggplot(iris, aes(x = Sepal.Width, fill = Species)) + geom_histogram() Or separating the results into panels with facet_wrap(): ggplot(iris, aes(x = Sepal.Width)) + geom_histogram() + facet_wrap(~ Species) There’s certainly nothing wrong with these options, but there’s always room for improvement. General idea for adding background data As the first example showed, a nice improvement (and the focus of the post) is to add the complete data set in the background of each facet panel. This way, it’s easy to interpret the data for any group relative to all other groups in the data set. The following pseudo code describes a generic approach for implementing this trick: ggplot(data = full_data_frame, aes(...)) + geom_*(data = data_frame_without_grouping_var, colour/fill = "neutral_color") + geom_*() + facet_wrap(~ grouping_var) This pseudo code will become clearer as we try some examples. For the moment, the important thing to recognise is that the first geom layer we’re plotting uses data without the grouping variable, and will appear in some neutral colour. On top of this, we add the same sort of geom, but with the group data highlighted (e.g., with different colours). Finally, we facet the plot based on our grouping variable. Let’s try it out with some examples. Histogram example We’ll start by creating something similar to the example shown at the beginning. d
“Cyberspace. A consensual hallucination experienced daily by billions of legitimate operators, in every nation, by children being taught mathematical concepts. … A graphic representation of data abstracted from banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding.” Neuromancer My try to visualise the cybernet, and it "Gods". Still inspired by cyberpunk novel, "Sprawl Trilogy"... :)
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Visualise the media
A few weeks ago, fellow sketchnoter Katharina Bluhm asked me, if she could interview me for her series ‘Women who visualise’ (well, it’s actually called’ Frauen, die visualisieren ’ because it is in German and you can find many interesting interviews over on her blog). It took me a while to carve
Tip 1: Know your Keywords Tip 2: Build a story for concepts Tip 3: Check your understanding Tip 4: Use trace table to visualise data structures and programming statements TIp 5: Solutions are from your mind, not your computer Tip 6: Practice, practice, practice Tip 7: Frequent revision using small chunks of time Tip 8: Exam style questions Tip 9: Make good use of marking scheme
When you think about something and ***FEEL*** them a lot…Nothing can stop them from coming to you!💫🪐✨This is law of attraction #selflove #selfcare #mentalhealthmatters #dream #cutecomics #positivity
What would happen if you applied data visualisation techniques of 2012 to data from the 1980s? Danny Dorling has found out
