What are data science and data analytics? Are they two different things? Why is one important and the other less important? These are questions that programmers, IT managers, entrepreneurs, CIOs, CEOs, and other business leaders may be asking as the data sets and technologies continue to evolve.
Data science refers to the process of mining relevant data from all sources in order to uncover patterns and insights. Data mining typically refers to the study of data sets mined from databases and other sources to find relationships and correlations. These patterns and insights can then be used to support strategic business processes by improving operations, providing better customer service, or discovering new product opportunities. Data mining and data analytics become especially important as organizations evolve and grow, because as these businesses expand their data collection and resources increase to support even more business activities.
While data science does not actually use the data it identifies and mines, data science often makes use of mathematical algorithm models to create visual representations of the data sets. Examples include data visualization tools such as matrices, neural networks, and graphical algorithms. Algorithms are programming solutions that make use of mathematical equations to solve problems. They are sometimes written in a higher level of language than other types of code because they rely on data to function.
One of the most common uses of algorithms in artificial intelligence. Artificial intelligence describes the ability to extract insights from large databases without the aid of humans. Researchers have been using artificial intelligence for a number of years and companies such as Google, Facebook, and Microsoft have made significant strides toward developing technologies for such purposes. Algorithms in this context are also considered to be part of the broader field of data mining.
Another way data analytics is used today is through data modeling or machine learning. Machine learning deals with the training of computer programs to recognize patterns and relationships between data sets. One example is how a car company might use data analytics in order to make their cars safer. Similarly, data models are used for things like product recommendations and search engine optimization.
While data science has many applications across many different industries, one of its most popular uses today is predictive analytics. This refers to the process of using past data in order to predict the outcome of future events. Machine learning and predictive analysis are closely related and each is designed to take the best possible steps towards achieving a predetermined goal. In both cases, computers are used to effectively take over the role of a human by handling and organizing data sets.
Even though data science has so many applications across many different industries, it is by no means a complete science. Data mining deals exclusively with unstructured data sets in massive online libraries. The machine learning applications of data science often involve tasks such as writing a report about a person that someone has studied. It can also be applied to marketing research or to search engine optimization. Today, data mining has even reached the consumer level by way of apps that help consumers organize and store data.
As data mining is becoming more popular as an industry, data scientists need to hone their skills required to use the technology. In order to do this, they must learn to work with artificial intelligence and have the ability to train machines. Data science is not as easy as it looks. However, with proper training and dedication, anyone can become a data scientist. With the right skills and the right tools, data scientists can make a major impact on the field of technology today.
