[BlindMath] Is it possible for screen reader users to do data science?

Sun Jun 25 21:58:05 UTC 2017

Hello Dickson et al.,

I agree with Vincent. I would note that the vast majority of data science is not actually a visual exercise. Experts put the proportion of work being done to collect and clean data at anywhere from half to 90%. Yes, some of the cleaning is done by investigating unusual observations in graphs, but there are so many non-visual ways of gaining such information.

I would observe that statisticians were doing work well before graphs were being created and that there are plenty of somewhat forgotten ideas that might prove useful for a blind person to substitute for creating a graph. For example, I have two variables and want to know if they are linearly related. A sighted person jumps straight for a scatter plot and eyeballs the results to help them decide if the relationship can be explained by a straight line. While they are dithering, I've fitted the straight line model and I'm checking the residuals because that's what is going to happen anyway if the sighted person decides the relationship is linear. If the relationship wasn't linear, I've found that out by investigating the residuals; OK, I'm doing that with another scatter plot, but at least my scatter plot is going to exaggerate any nonlinearity that existed in the original relationship. I've also see the R squared of the model to decide if there is any relationship to want to question he linearity of as well. Then, if there is something to get excited about, I make the graph for the poor sighted audiences I must work with.

The problem I have is that the use of the all too readily available software that creates awesome data visualisations gets used in introductory teaching to the detriment of the old-fashioned stuff that generally gets pushed into the higher level courses. I was once asked if there was room to create an accessible intro stats course for blind people. I didn't think so at the time, but I've toyed with the issue on and off for the last few years. The trend I've observed is to emphasise what you can see from the current display of data, as against what a graph can show in a theoretical sense. The same ideas get taught, but sort of in the wrong order for a blind person. 

Compare the two quotes:
"Let's look at this data, and hey, what's that funny point over there? Well it's called an outlier and this (insert name of graph here) can identify the existence of outliers for you."
Versus:
"A (insert name of graph here) can highlight the existence of outliers. Let's look at some examples, one with an outlier and  one without."

The first is disabling to anyone not able to see the graph in question, while the second is less disabling. Unfortunately, the second (older) style is considerably less stimulating for a sighted audience. The former goes well with someone actually creating the graphs in a live demonstration while the second suits the pre-prepared (I call it the cooking show approach) style of lecturing. Actually most live demonstration style lectures require some preparation too, but it's obvious that the actions are being completed on the spot. Most smart lecturers now do the former and have the latter (pre-prepared back up plan) waiting just in case.

Your choice of nonvisual technique will depend on the scenario in question, and on your personal ability to comprehend what that nonvisual technique has to offer.

I realise that some people swear by sonification as a substitute for visual inspection of data.  I've also heard data sonification described as an alternative to visual inspection of data with its use having an additive impact to visual inspection. For others, sonification just makes a lot of noise. Rather unfortunately, I find myself in this last category. I do like that it can be done and I'll help anyone trying to create a useful tool for sonifying data (in R only though) but that's because I'm interested in getting a wide range of options for blind end users as against just working towards tools that I personally will find useful.

I hope that you (Dickson) are considering coming to Orlando for the NFB Convention where we will almost certainly have discussions on the problems you face.

Cheers,
Jonathan

-----Original Message-----
From: BlindMath [mailto:blindmath-bounces at nfbnet.org] On Behalf Of Martin, Vincent F via BlindMath
Sent: Monday, 26 June 2017 5:15 a.m.
To: Blind Math list for those interested in mathematics
Cc: Martin, Vincent F
Subject: Re: [BlindMath] Is it possible for screen reader users to do data science?

It can be done using a number of methodologies.  I have done it with and without sighted assistance.  Using a combination of tactile graphs and sighted assistance is how I did forecasting work.  I also am using sonification of data to interpret graphical output as well.  Work is being done for sonifying graphs using "R", SAS has intergrated what we called the "sonification sandbox" in Dr. Bruce Walker's sonification lab at Georgia Tech and it is readily available right now.  It was unveiled at CSUN this Spring and I use it with SAS right now.  I am also using sonified graphs with the graphical output of SPSS in my current research as well.  I am up to dissertation proposal on what type of experiment I want to do, but I am sure it is similar to what most data analysts also use to make inferences with their visual graphs as well.

-----Original Message-----
From: BlindMath [mailto:blindmath-bounces at nfbnet.org] On Behalf Of Dickson Tan via BlindMath
Sent: Sunday, June 25, 2017 12:40 PM
To: blindmath at nfbnet.org
Cc: Dickson Tan <dickson.tan.2013 at gmail.com>
Subject: [BlindMath] Is it possible for screen reader users to do data science?

Hi,

Given that visualization is used often in data science to see patterns, is it possible for a screen reader user to do data science? I've heard of some tools for doing basic bar/line graphing, but as far as I know, they are immature, and can't handle other types of graphs. 

If not, how far would I get without being able to access visualizations?

Best regards,

Dickson

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