This article separates computer science into six other categories, with software engineering being one of them. How is software engineering like computer science? How is software engineering different?
Each category is further described by a spider web diagram, showing the topics covered and the degree of emphasis on each topic. A comparison of computer science and software engineering shows the overlap and differences in topics and emphasis. It positions software engineering as a category of computer science. This categorization contrasts the STEM categorization of four main disciplines: science, technology, engineering, and math. Science discovers general principles and problem-solving techniques. Engineering uses those principles and techniques to develop solutions to problems. Technology uses practices and tools to deploy, operate, and maintain those solutions in practical applications. Both perspectives are helpful. At a higher level of abstraction, the STEM perspective shows that computer science and software engineering have fundamentally different processes. At a more detailed level of abstraction, the six degrees perspective shows the intersection and difference of topic coverages between computer science and software engineering and the other four disciplines.
Computing Related Fields
Data Science
Data Science is a new, and rapidly growing inter-disciplinary field of study. It focuses on using a combination of computation and mathematics to answer questions and solve problems using large amounts of data.
A data scientist will be responsible for gathering data and then using it to find trends, make forecasts, and communicate information. To do this, they will write computer programs, apply various mathematical techniques from statistics, calculus, and linear algebra, and make use of advanced tools like machine learning algorithms.
Although there are a general set of skills used by all data scientists, an individuals data scientist often focuses in a particular domain. They may specialize in working with data from biological sciences, or business and marketing, or sports management, or geology, or in any other domain where large amounts of data exist. To effectively work in one of these domains, a data scientist often needs field specific knowledge in addition to their general data science skills.
Typical careers:
-
Data Scientist or Data Analyst
Education:
Working as a data analyst or scientist generally requires a Bachelor's degree or graduate degree (Master's or PhD).
Because Data Science is a cross-disciplinary field, degrees in data science can be found in many different programs. Some Data Science degrees are offered by math departments, others as concentrations in a degree in computer science. And specialized data science programs may exist in other departments - a biology department may offer a degree in "Bio Informatics" or "Biological Data Science".
Any data science degree is probably going to feature:
-
A solid foundation in programming and algorithms.
-
Mathematics including statistics and likely calculus and linear algebra.
-
Exposure to the techniques and tools used in data science.
Data Science combines mathematics, computer science, and knowledge in a domain.