What Is Data Science and How Do They Apply in the Real World?

Data science is the discipline of study that combines technical domain knowledge, programming skills, and common knowledge of statistics and math to extract useful insights from large amounts of unstructured data. Data science practitioners use large-scale data sets to build artificial intelligent systems to solve practical problems in domains ranging from weather prediction to corporate strategic planning. Data scientists can work in a variety of industries, including health care, finance, information technology, human resources, manufacturing, and law enforcement. In recent years, though, there has been a growing trend for data scientists to go after less-frequently asked questions in the social sciences-including anthropology, computer science, law, engineering, philosophy, psychology, and sociology.

A handful of disciplines dominate the list of available employment for data scientists. The most obvious choice for a new graduate engineer or entrepreneur in the Data Science field is to pursue a graduate degree in one of the four fields mentioned above, in order to develop analytical and modeling skills. However, some data sets just do not lend themselves to traditional scientific analysis; in fact, some have very poor properties when it comes to representing the real world.

For example, real world problems such as pollution are difficult to collect and process using traditional scientific methods. Traditional statistical methods simply don’t work well for this kind of data, especially with the kind of high levels of redundancy typical of big data sets. Researchers in this field typically utilize machine learning techniques, like supervised learning, to make their job easier. Machine learning allows them to take a vast amount of complex, highly correlated real world data sets and shape them using a specially designed machine. This process allows researchers to leverage huge amounts of data without having to actually learn how to collect, manipulate, analyze, and interpret it.

Machine learning is also particularly useful for those whose talents lie in language processing. Language processing tasks, such as speech recognition, are common capabilities within the range of skills that Data Science professionals possess. It’s very common for Data Science jobs to require proficiency in some sort of programming language, as well. The primary reason why computer science jobs are so attractive to those who are excellent at logic and reasoning is that logic and reasoning is heavily dependent on a good grasp of statistics and machine learning techniques. While this may not seem all that important to a Data Science major, it certainly makes the job much less complicated for the Data Science professional to master.

Data Science jobs also tend to appeal to those who are more artistic than others. Particularly for younger researchers, a Data Science job can allow them to put their creative geniuses to good use. Many Data Science jobs involve creating visual presentations of data sets and related data manipulation and analysis. These visualizations often demonstrate how the data set itself has been constructed and what techniques the researchers used to arrive at its conclusions. This type of creativity can be very useful for a Data Science major in graduate school or for those who are seeking careers after graduation.

As you progress through your graduate program or seek employment in the related field, you will likely want to take courses that teach you how to utilize various types of statistics and machine learning techniques to make your presentations and analyses more meaningful and interesting to your audiences. You’ll also find that there are plenty of options beyond the traditional computer sciences. You may be interested in working in healthcare, business, or even law and would benefit from a solid understanding of statistics and the Warehousing industry.

Generally speaking, Data Science jobs require a strong understanding of all of the various statistical techniques, such as linear regression, logistic regression, generalized linear and logistic models, k-folding and mathematical algorithms, and of course, Data Science fundamentals. Machine Learning involves a sophisticated form of statistical analysis that uses an artificial intelligence tool that learns through real-time experiences. Data scientists may be required to implement complex algorithms that deal with thousands or millions of data samples. In all actuality, any position as a data scientist requires knowledge of a wide variety of scientific disciplines and a strong aptitude for ridding data of unnecessary noise and biases. Graduates with a strong background in mathematics will especially excel in this field.

In addition to basic math skills and scientific methods of measurement, the ability to communicate well is essential. Those pursuing a career as data scientists may be asked to design, train, or work with artificial intelligence programs. It is possible that the position may also require proficiency in other areas such as computer programming, artificial intelligence theory, specific technologies, database systems design, and web and software development.

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