Data science case studies

We will spend much of this book looking at real-life examples of using data science and machine learning. The combination of math, computer programming, and domain knowledge is what makes data science so powerful but it can often be too abstract without concrete coding examples.

Oftentimes, it is difficult for a single person to master all three of these areas. That’s why it’s very common for companies to hire teams of data scientists instead of a single person. Let’s look at a few powerful examples of data science in action and its outcomes.

Case study – automating government paper pushing

Social security claims are known to be a major hassle for both the agent reading them and the person who wrote the claims. Some claims take over two years to get resolved in their entirety, and that’s absurd! Let’s look at the following figure, which shows what goes into a claim:

Figure 1.4 – Sample social security form

Not bad. It’s mostly just text, though. Fill this in, then that, then this, and so on. You can see how it would be difficult for an agent to read these all day, form after form. There must be a better way!

Well, there is. Elder Research Inc. parsed this unstructured data and was able to automate 20% of all disability social security forms. This means that a computer could look at 20% of these written forms and give its opinion on the approval.

Apart from this, the third-party company that is hired to rate the approvals of the forms gave the machine-graded forms a higher grade than the human forms. So, not only did the computer handle 20% of the load on average, but it also did better than a human.

Modern language models such as GPT-3 and BERT have taken the world of NLP by storm by pushing the boundaries of what we thought was possible way beyond its previously considered limits. We will spend a lengthy amount of time talking about these models later in this book.

Fire all humans, am I right?

Before I get a load of angry emails and tweets claiming that data science is bringing about the end of human workers, keep in mind that the computer was only able to handle 20% of the load in our previous example. This means that it probably performed terribly on 80% of the forms! This is because the computer was probably great at simple forms. The claims that would have taken a human minutes to compute took the computer seconds. But these minutes add up, and before you know it, each human is being saved over an hour a day!

Forms that might be easy for a human to read are also likely easy for the computer. It’s when the forms are very terse, or when the writer starts deviating from the usual grammar, that the computer starts to fail. This model is great because it lets humans spend more time on those difficult claims and give them more attention without getting distracted by the sheer volume of papers.

Note that I used the word “model.” Remember that a model is a relationship between elements. In this case, the relationship is between written words and the approval status of a claim.

Case study – what’s in a job description?

Looking for a job in data science? Great! Let me help. In this case study, I have scraped (used code to read from the web) 1,000 job descriptions for companies that are actively hiring data scientists. The goal here is to look at some of the most common keywords that people use in their job descriptions, as shown in the following screenshot:

Figure 1.5 – An example of data scientist job listings

In the following Python code, the first two imports are used to grab web data from Indeed.com, and the third import is meant to simply count the number of times a word or phrase appears, as shown in the following code:

import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
# grab postings from the web
texts = []
# cycle through 100 pages of indeed job resources
for i in range(0,1000,10):
 response = requests.get('http://www.indeed.com/jobs?q=data+scientist&sta rt='+str(i)).text
 soup  = BeautifulSoup(response)
 texts += [a.text for a in soup.findAll('span', {'class':'summary'})]
print(type(texts))
print(texts[0]) # first job description

All this loop is doing is going through 100 pages of job descriptions, and for each page, it is grabbing each job description. The important variable here is texts, which is a list of over 1,000 job descriptions, as shown in the following code:

type(texts) # == list
vectorizer = CountVectorizer(ngram_range=(1,2), stop_words='english')
# Get basic counts of one and two word phrases
matrix = vectorizer.fit_transform(texts)
# fit and learn to the vocabulary in the corpus
print len(vect.get_feature_names()) # how many features there are

There are 10,857 total one- and two-word phrases in my case! Since web pages are scraped in real time and these pages may change when you run this code, you may get a different number than 10,857.

I have omitted some code here because we will cover these packages in more depth in our NLP chapters later, but it exists in the GitHub repository for this book. The results are as follows (represented by the phrase and then the number of times it occurred):

Figure 1.6 – The top one- and two-word phrases when looking at job descriptions on Indeed for the title of “Data Scientist”

There are many interesting things to note about this case study, but the biggest takeaway is that there are many keywords and phrases that make up a data science role. It isn’t just math, coding, or domain knowledge; it truly is a combination of these three ideas (whether exemplified in a single-person team or across a multi-person team) that makes data science possible and powerful.