Data science is the study of the extraction of knowledge from data, yet the key word is science. It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, statistical learning, computer programming, data engineering, pattern recognition and learning, data warehousing, and performance computing with the goal of extracting meaning from data and creating data products. The subject is not restricted to only big data, although the fact that data is scaling up makes big data an important aspect of data science. Another key ingredient that boosted the practice and applicability of data science is the development of machine learning- a branch of artificial intelligence- which is used to uncover patterns from data and develop practical and usable predictive models.
A practitioner of data science is called a data scientist. Data scientists solve complex data problems by employing deep expertise in some scientific discipline. It is generally expected that data scientists be able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects is not always required. However, a data scientist is most likely to be an expert in only one or two of these disciplines and proficient in another two or three. Therefore, data science is practiced as a team, where the members of the team have a variety of expertise.
Data science degrees are offered at the bachelor’s and master’s level-each requires core courses in mathematics, computer science (programming and data structures) and statistics. At the bachelor’s level, typical courses involve:
- Linear algebra
- Elements of Probability and Mathematical Statistics
- Applied Statistics for the Social Sciences, Biological and Physical Sciences
- Introduction to Artificial Intelligence
- Data Mining
- Design and Analysis of Efficient Algorithms
There are also particular application area courses:
- Biology- evolution, genetics and the human genome
- Brain and Cognitive Sciences- perception, neurobiology, psycholinguists, sensory and motor neuroscience
- Earth and Environmental Science- geohazards, climate change and energy resources
- Physics- quantum mechanics, electricity, physics and finance
There are numerous master’s programs offered throughout America in Data Science. One such Master’s of Science (M.S.) program is offered a New York University (NYU) whose curriculum is 36 credits, half of which are required and half are electives. The majority of the courses mirror an undergraduate curriculum with the same emphasis on data science, statistics and mathematics.
NYU believes that their M.S. in Data Science is a “capstone project that makes the theoretical knowledge you gain in the program operational in realistic settings. During the project, you will go through the entire process of solving a real-world problem: from collecting and processing real-world data, to designing the best method to solve the problem, and finally, to implementing a solution. The problems and datasets you’ll engage with will come from real-world settings identical to what you might encounter in industry, academia, or government”.
The NYU degree objective should be one to consider when selecting a program; making sure the academics prepare one well enough for the workplace application of one’s knowledge.
The US Bureau of Labor Statistics (BLS) has one category for “Computer and Information Research Scientists.” The BLS reported in 2012 that the median annual salary was $102,190 with a master’s or doctoral degree. The growth/change rate in this profession is projected to be 15% through 2022.
All major industries and companies employ data scientists. Some reports refer to 6,000 companies whose workforce consists of these specialists. For example: Google, Twitter, Intel, Boeing, health care companies, oil industries, retail, marketing agencies, as well as the US government routinely hire data scientists.