Data Science and Computer Science often go hand in hand, but what makes them different? What do they have in common? Some assume data science is more or less the same as computer science. However, both the fields, although similar in many areas are different from each other altogether. This article will look closely at the overlapping areas, their scope, the specific differences between data science and computer science & much more. Read on to know!
What Is Computer Science?
Computer science, in simple words, is the study of computers and computing concepts. It is the study of the processes that interact with data in the form of programs. It deals with the manipulation of information by making use of various types of algorithms. So, computer science deals with the study of both software and hardware and other components like networking and the internet. The hardware part of computer science deals with studying the basic design of computers and their working process of it. The software part of computer science deals with the study of programming concepts as well as languages. Computer science also deals with compilers and operating systems.
What Is Data Science?
Data science is a domain in which information & knowledge are extracted from the data using scientific methods, algorithms, and processes. It can be defined as a combination of various mathematical tools, algorithms, statistics, and machine learning (ML) techniques. These techniques are thus used to find the hidden patterns and valuable insights from the data, which helps decision-making. Data science deals with both structured as well as raw data. It is related to both big data & data mining. Data science involves studying the historical trends and thus using its conclusions to redefine present trends and predict future trends.
Major Differences Between Computer Science and Data Science
Given below is the list of points that describe the important differences between Computer Science and Data Science:
- Computer Science is the field that encompasses various subjects such as Data Structures, Computer Architecture, Programming Languages, Algorithms, etc., whereas Data Science comprises mathematics concepts such as Algebra, Calculus, Advanced Statistics, Data Engineering, etc.,
- Computer Science teaches us how processors are built and work and the memory management in the programming areas. Data Science gives us a view of how data can be used to study how it will be processed and manipulated to give it a structure and make it insightful.
- Computers Science provides us with detailed insight into the utilization of computing machinery and its applications. Data Science reveals how to derive information & Knowledge from data in many forms.
- Computer Science sub-areas include computations, probability, reasoning, discrete structures, and database design. Data Science includes Simulation, modeling, analytics, ML, etc.,
- Computer science is the umbrella branch, whereas Data Science is a branch under Computer Science.
- Computer Science is entirely about building and utilizing computers efficiently, and Data Science is about safely handling the data.
- Computer science is evolving with new concepts, and more efficient and advanced devices are coming. Data is growing fast daily, causing more complexity to handle and maintain efficiently.
- Computer Science deals with algorithms with a significant focus on software engineering and development. Data Science is the combination of data engineering, maths, and statistics domains.
- Computer science concerns itself with scientific ways of finding a solution to a problem. At the same time, data Science deals with finding ways to store, organize & process data.
- Computer Science has many research areas to pursue and excel in career, whereas Data Science research areas have been recently evolving and expanding, providing us more options.
You May Also Like to Read About: The Difference Between Data Science and Data Mining
How Should you Begin your Career in Data Science?
While both branches are related to computers and technology, aspiring data scientists will need to recognize their knack in this field and choose their specialization accordingly if they are interested in becoming data scientists. In that case, they should ensure that your data science course allows you to study linear algebra, statistics, and calculus in your four-year Bachelor’s degree.
By the end of your education, you will be able to process complex datasets using your technical skills & knowledge. Many graduates start at small companies, where they can quickly begin to apply their theoretical knowledge, putting it into practice at work. Building practical experience & learning skills is the best way to advance your data science career; for this, many students take professional courses to skill up.
How Should you Begin your Career in Computer Science?
You’ll need to complete a bachelor’s degree in computer science as an aspiring computer scientist. Doing a master’s degree will place you in a strong position in this notoriously competitive field. Many online courses also provide associate degrees in computer programming.
You can develop a solid foundation for your computer science career path early by choosing to study mathematics and computer science in school. Most IT companies will hire college graduates looking to apply their new skills before pursuing a master’s qualification, giving you a more senior position with a more attractive salary.
Work profiles of Data scientists & Computer scientists
Both profiles align in some places & differ in many. Data Scientists primarily business analysis, research, data, statistics, and Machine Learning algorithms. Computer Scientists: programming, Software Engineering, productionaliztion, DevOps, automation, IT, Networking, Database Administration, Hardware, Systems Analytics, and Web Development.
Here are a few similarities between the two roles:
- They require an understanding of the business and its products
- They require a working knowledge of the data at the company
- Overall, both domains follow a systemic approach to the scientistic process
- Both are usually proficient in one programming language
- Both are expected to be leaders in their domain
- Both are cross-functional
- Both can start in the other role and switch to the other
Here are a few differences between the two roles:
- Data Scientists often work with Product Managers & other business-facing roles more
- Computer Scientist as a role is relatively more encompassing with a greater variety
- Data Scientists focus more on ML algorithms
- Education of the two is different, usually a Computer Science degree and a Data Science degree
- Computer Scientists focus more on software design & development
- Data Scientists have a background in statistics
- Computer Scientists are more automation and object-oriented-focused
- Computer Scientists generally have a background in Computer Engineering
Conclusion!
To conclude, Computer science is an umbrella domain, whereas Data science comes under it. Computer Science allows us to use the technologies in computing the data. In contrast, Data Science lets us operate on the existing data to make it available for all functional purposes. At present, there are loads of opportunities in Computer Science career paths as well as in Data Science. This massive demand for skilled Data scientists & computer scientists doesn’t seem to go off anytime soon.
