Why data science?
In this Data Age, it's clear that we have a surplus of data. But why should that necessitate an entirely new set of vocabulary? What was wrong with our previous forms of analysis? For one, the sheer volume of data makes it impossible for a human to parse it in a reasonable time frame. Data is collected in various forms and from different sources and often comes in a very unorganized format.
Data can be missing, incomplete, or just flat out wrong.
Oftentimes, we will have data on very different scales, and that makes it tough to compare it. Say that we are looking at data about pricing used cars. One characteristic of a car is the year it was made, and another might be the number of miles on that car. Once we clean our data (which we will spend a great deal of time looking at in this book), the relationships between the data become more obvious, and the knowledge that was once buried deep in millions of rows of data simply pops out. One of the main goals of data science is to make explicit practices and procedures to discover and apply these relationships in the data. Learn more knowledge from
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Earlier, we looked at data science in a more historical perspective, but let's take a minute to discuss its role in business today using a very simple example.
Example – xyz123 Technologies
Ben Runkle, the CEO of xyz123 Technologies, is trying to solve a huge problem. The company is consistently losing long-time customers. He does not know why they are leaving, but he must do something fast.
He is convinced that to reduce his churn, he must create new products and features, and consolidate existing technologies. To be safe, he calls in his chief data scientist, Dr. Hughan. However, she is not convinced that new products and features alone will save the company. Instead, she turns to the transcripts of recent customer service tickets. She shows Ben the most recent transcripts and finds something surprising:
- ".... Not sure how to export this; are you?"
- "Where is the button that makes a new list?"
- "Wait, do you even know where the slider is?"
- "If I can't figure this out today, it's a real problem..."
It is clear that customers were having problems with the existing UI/UX, and weren't upset because of a lack of features. Runkle and Hughan organized a mass UI/UX overhaul and their sales have never been better.
Of course, the science used in the last example was minimal, but it makes a point. We tend to call people like Runkle drivers. Today's common stick-to-your-gut CEO wants to make all decisions quickly and iterate over solutions until something works. Dr. Hughan is much more analytical. She wants to solve the problem just as much as Runkle, but she turns to user-generated data instead of her gut feeling for answers. Data science is about applying the skills of the analytical mind and using them as a driver would. data science course
Both of these mentalities have their place in today's enterprises; however, it is Hughan's way of thinking that dominates the ideas of data science—using data generated by the company as her source of information, rather than just picking up a solution and going with it.