Getting Started | Using Displayr
| 31 January 2017 | by Tim Bock
Introduction to Displayr 1: Overview

What is Displayr? It’s two apps in one.  First, it’s a complete data science platform, which does everything from simple tables through to advanced machine learning.  It’s also a comprehensive reporting platform that creates online dashboards and exports to PowerPoint and Excel. As a cloud-based app, you can access it, and your work in it, from anywhere and in any browser.



Displayr workflow

Displayr covers the following four stages of the data analysis workflow with no need to use additional applications.

  1. Import the data
  2. Basic data exploration
  3. Advanced analysis/data science.
  4. Finalize reporting.

What is so unusual about this?  Because it’s integrated into one single app, Displayr can support many different workflows between these procedural stages. This also allows for much greater fluidity and freedom within the workflow. That said, the first step is always the same – after all, without data you wouldn’t have anything to analyze. Then you and your colleagues would conduct your data exploration and add in more advanced analytical methods. In many cases the next step would involve switching software to finalize your report.

Now imagine that you realized, just as you were finalizing your presentation, that something wasn’t quite right in your data.  You’d have to fix the issue, repeat your basic and advanced analysis (or get your colleague who conducted the analysis to do it for you), and finally re-create your report accordingly.  In Displayr, once the data issue has been resolved, all the analyses using that data would update automatically.  From this follows that you can set up your report using only partial data, and when importing a final data file the whole report would update.

There are many additional benefits to performing data science in Displayr worth mentioning: check out the section titled The secret sauces in Introducing Displayr: the data science and reporting app for everyone.  The rest of this post, and its five companions in the Introduction to Displayr series, give you more details on the mechanics of using Displayr, showing you how to get started.



A quick tour of the interface

The screenshot below shows you Displayr. In the middle, you can see a page of a document created in Displayr. On the right-hand-side of the page you can see a decision tree.  In the center-left a table and a chart (or more specifically, a ranking plot). The callouts, in this case blue, boxes with arrows are also created in Displayr.  They point to different parts of the program.  Above the document a ribbon menu system provides access to all functions. If you want a closer look at this example, you can play with it here.

Sankey plot in Displayr



This is the first of a series of blogs introducing the basics of Displayr. The next blog in this series is Introduction to Displayr 2: Getting your data into Displayr.

Author: Tim Bock

Tim Bock is the founder of Displayr. Tim is a data scientist, who has consulted, published academic papers, and won awards, for problems/techniques as diverse as neural networks, mixture models, data fusion, market segmentation, IPO pricing, small sample research, and data visualization. He has conducted data science projects for numerous companies, including Pfizer, Coca Cola, ACNielsen, KFC, Weight Watchers, Unilever, and Nestle. He is also the founder of Q, a data science product designed for survey research, which is used by all the world’s seven largest market research consultancies. He studied econometrics, maths, and marketing, and has a University Medal and PhD from the University of New South Wales (Australia’s leading research university), where he was an adjunct member of staff for 15 years.

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