Why feature engineering matters

Data scientists and machine learning engineers frequently gather data in order to solve a problem. Because the problem they are attempting to solve is often highly relevant and exists and occurs naturally in this messy world, the data that is meant to represent the problem can also end up being quite messy and unfiltered, and often incomplete.

This is why in the past several years, positions with titles such as Data Engineer have been popping up. These engineers have the unique job of engineering pipelines and architectures designed to handle and transform raw data into something usable by the rest of the company, particularly the data scientists and machine learning engineers. This job is not only as important as the machine learning experts’ job of creating machine learning pipelines, it is often overlooked and undervalued.

A survey conducted by data scientists in the field revealed that over 80% of their time was spent capturing, cleaning, and organizing data. The remaining less than 20% of their time was spent creating these machine learning pipelines that end up dominating the conversation. Moreover, these data scientists are spending most of their time preparing the data; more than 75% of them also reported that preparing data was the least enjoyable part of their process.

Here are the findings of the survey mentioned earlier:

Following is the graph of the what Data Scientist spend the most time doing:

As seen from the preceding graph, we breakup the Data Scientists's task in the following percentage :

• Building training sets: 3%
• Cleaning and organizing data: 60%
• Collecting data for sets: 19%
• Mining data for patterns: 9%
• Refining algorithms: 5%

A similar pie diagram for what is the least enjoyable part of data science:

From the graph a similar poll for the least enjoyable part of data science revealed: 

• Building training sets: 10 %
• Cleaning and organizing data: 57%
• Collecting data sets: 21%
• Mining for data patterns: 3%
• Refining algorithms: 4%
• Others: 5%

The uppermost chart represents the percentage of time that data scientists spend on different parts of the process. Over 80% of a data scientists' time is spent preparing data for further use. The lower chart represents the percentage of those surveyed reporting their least enjoyable part of the process of data science. Over 75% of them report that preparing data is their least enjoyable part.

Note

Source of the data: https://whatsthebigdata.com/2016/05/01/data-scientists-spend-most-of-their-time-cleaning-data/.

A stellar data scientist knows that preparing data is not only so important that it takes up most of their time, they also know that it is an arduous process and can be unenjoyable. Far too often, we take for granted clean data given to us by machine learning competitions and academic sources. More than 90% of data, the data that is interesting, and the most useful, exists in this raw format, like in the AI chat system described earlier.

Preparing data can be a vague phrase. Preparing takes into account capturing data, storing data, cleaning data, and so on. As seen in the charts shown earlier, a smaller, but still majority chunk of a data scientist's time is spent on cleaning and organizing data. It is in this process that our Data Engineers are the most useful to us. Cleaning refers to the process of transforming data into a format that can be easily interpreted by our cloud systems and databases. Organizing generally refers to a more radical transformation. Organizing tends to involve changing the entire format of the dataset into a much neater format, such as transforming raw chat logs into a tabular row/column structure.

Here is an illustration of Cleaning and Organizing:

 

The top transformation represents cleaning up a sample of server logs that include both the data and a text explanation of what is occurring on the servers. Notice that while cleaning, the & character, which is a Unicode character, was transformed into a more readable ampersand (&). The cleaning phase left the document pretty much in the same exact format as before. The bottom organizing transformation was a much more radical one. It turned the raw document into a row/column structure, in which each row represents a single action taken by the server and the columns represent attributes of the server action. In this case, the two attributes are Date and Text.

Both cleaning and organizing fall under a larger category of data science, which just so happens to be the topic of this book, feature engineering.