Data science is a concept that is continuing to gain popularity in mainstream media. It can often be in discussions of AI, machine learning, data analytics, predictive analytics, or other related terms. Whether it is the recommended shows on your Netflix account, the creation of digital faces that are indistinguishable from those of real human beings, or even the candidacy of a data scientist in a recent U.S. election, data science is continually revolutionizing our world.
Data science is a combination of mathematics, programming, and the scientific process. Specialized blocks of code are developed to run large amounts of data through mathematical processes to find notable trends, answer complex questions, or develop solutions to a wide range of problems. Applications for data science may vary widely, but any business, governmental agency, or other institution can use data science to find quantitatively determined opportunities for growth and efficiency.
How Data Science Answers Tough Questions
Data science begins with a question. Regardless of whether the question is curious (e.g., “Can you tell the difference between a goldendoodle puppy and a piece of fried chicken?”) or complicated (e.g., “Can I use AI to determine if cancer exists in an image from a patient?”), the goal is to create a solution that is accurate, repeatable, and timely.
Once the question has been determined, a data scientist begins a multistep process to create the necessary solution. The first step in this process is to gather a large amount of data. For some questions, data has already been collected for others to use. However, other questions require data scientists to collect data through surveys or experiments or to “scrape” data from websites when allowed.
The collected data must be made usable before any solutions can be created. A significant portion of the world’s data is unstructured. Unstructured data, such as video and audio files, is data that is not stored in a traditional database format and requires much more manipulation to become usable. Even in structured data, duplicate and other erroneous information needs to be removed.
Cleaning data often requires specific scripts to remove unnecessary values. Common programming languages that are used in data science to write scripts include Python and R. These programming languages are usually run in a modular format through environments such as Jupyter Notebooks. This allows data scientists to work in an incremental process as well as quickly view data as cleaning occurs.
I Have Data—What’s Next?
After the data has been collected and cleaned, data scientists begin exploring it for any noticeable trends through visualization. Data visualizations such as graphs can be created directly within the data scientist’s programming environment. These visualizations give data scientists the initial leads on how to build a solution for the original question. For example, if a data scientist at an ice cream company was asked what month the most ice cream was sold, a line chart of ice cream sales over the last few years may show that July had the highest sales volume. Data scientists may even develop their data visualizations in specific software such as Tableau or Microsoft Power BI because these applications allow users to dynamically interact with data in a much more user-friendly way.
Depending on the question, the data scientist may discover the necessary …….