What Is Data Science?
The world is full of data. Data is everywhere and it always has been. Data helps us communicate with each other and make sense of the world around us, but the focus today is on digital data. Companies can now track consumer preferences, habits, and tendencies to tailor individual offerings based on customers’ online actions.
Ever wondered how you can browse running shoes and see the same pair you were just looking at in an advertisement minutes later? Or have you been baffled by how Google magically serves up relevant answers to your complex questions in seconds? It’s possible because of data science.
Data science is complex enough that it’s difficult to give one simple definition. As data becomes more intricate and complex, so does the data science definition. To put it simply, though, data science is the act of collecting, organizing, understanding, and using data to make strategic decisions.
Many companies who receive a lot of data have data scientists to help them provide a better experience for their customers and to make more practical decisions on their product or service offering. Companies that specialize in machine learning or artificial intelligence are built on data science.
Data Science Versus Data Analytics: What’s the Difference?
Data science is more comprehensive while data analytics is more niche. In short, data analytics is an element of data science. Data science, though, involves much more than just analytics. It deals with data from the collection stage to organization and analysis, and then through to the reporting and communication of what the data means.
What Is the History of Data Science?
Data science is a relatively new field. It’s essentially a combination of data mining and computer science. The idea of data mining came about in 1996, and it was named about five years later when William S. Cleveland formalized the term data science in an article, where he took data mining and added computer science.
The need for data science arose from companies’ necessity to understand all of the digital data and information they were getting. Data science encompasses everything from data collection and storage to analysis, reporting, and communication.
The future of data science and its uses is still largely unwritten. And as the ways to collect and analyze data become more sophisticated, the granularity of the targeted business decisions that can be made as a result will be mind-boggling. We’ve only scratched the surface when it comes to what data science can do for business.
What Does Data Science Have to do with Computer Programming?
Data scientists need to understand how to code. Why? Because they are often tasked with building data infrastructures. To put it simply, machines are needed to collect, analyze, and manipulate digital data, and those machines run on software. That software is built with code.
To be a good data scientist, you must know how to code. Data scientists are frequently skilled in many programming languages, including Python, R, Java, SQL, and Julia. They’re also usually familiar with platforms like Hadoop, Hive, or Pig.
Why Is Data Science So Important?
Do you wonder why data science is so critical? Because everything runs on data. Everything that pops up on the Google search result page wouldn’t be possible without data science. Your smart device wouldn’t have a prayer of working without data science. Machine learning and AI? Non-existent without data science. The way businesses grow and evolve in today’s competitive environment is largely driven by data science.
What Are Some of the Elements of Data Science?
When data science is mentioned, you’ll often hear terms including algorithms, big data, statistics, artificial intelligence, deep learning, and machine learning. It’s easier to understand data science when you understand the hierarchy and also the data science life cycle.
What’s the Data Science Hierarchy?
Think of data science like a pyramid. At the bottom, you collect the data. The next level deals with how you move and store that data. Next, come data exploration and transformation, followed by aggregating and labeling. Learning and optimization as a result of data are at the tip of the hierarchy.