What Is Data Science?

 

 

 

 

There’s a joke that says a data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician. (I didn’t say it was a good joke.) In fact, some data scientists are—for all practical purposes—statisticians, while others are pretty much indistinguishable from software engineers. Some are machine-learning experts, while others couldn’t machine-learn their way out of kindergarten.Some are PhDs with impressive publication records, while others havenever read an academic paper (shame on them, though). In short, pretty much no matter how you define data science, you’ll find practitioners for whom the definition is totally, absolutely wrong.

 

Nonetheless, we won’t let that stop us from trying. We’ll say that a data scientist is someone who extracts insights from messy data. Today’s world is full of people trying to turn data into insight.

 

 

Data science has many tools, techniques, and algorithms called from these fields, plus others –to handle big data.

 

The goal of data science, somewhat similar to machine learning, is to make accurate predictions and to automate and perform transactions in real-time, such as purchasing internet traffic or automatically generating content.

 

 

Data science relies less on math and coding and more on data and building new systems to process the data. Relying on the fields of data integration, distributed architecture, automated machine learning, data

 

 

 

 

visualization, data engineering, and automated data-driven decisions, data science can cover an entire spectrum of data processing, not only the algorithms or statistics related to data.