Both Data Science and Computer Science have been having a lot of traction as of late, leading to some confusion about what the fields entail and their relation to each other. In this article, we will try our best to explore that relation on a very high level.
The first distinction that should be made is that Data Science is a relatively new field, one which has seen tremendous growth as of late, whereas Computer Science is a much older discipline. So computer science as such has been around for ages and as such it is hard to sum up what exactly it is all about; however we will try our best here - the basics are that Computer Science concerns itself with how computers work and how they're built: what they can do, how they can do it, and what problems they may encounter along the way.
Since nearly every part of modern society is now somehow connected to computers, this has become a very important field to study if one wants to understand how information technology works within our world.
What is Data Science?
Data science is a field that uses scientific methods, processes, and algorithms in order to extract knowledge or insights from raw data (e.g.: numbers, text) by employing techniques like pattern recognition, machine learning, statistics, and data mining. As far back as antiquity, there were people interested in extracting knowledge out of data it can be found in the navigation logs of sailors or in the astronomical records. The methods were however rudimentary and limited to specific problems.
When computers became commonplace, scientists started working with numerical data using statistical techniques like linear regression, probability theory, hypothesis testing, etc. Modern-day data science has its roots in the late 90s at the intersection of computer science and statistics. It's really only with the proliferation of the internet, web 2.0 and modern business needs for data-driven decision making that data science as we know it today really started to take off.
What is Computer Science?
Computer science is the study of how to effectively solve problems using computers. Computer science is a very broad field that spans several disciplines like mathematics, physics, hardware architecture, cybersecurity, software engineering, linguistics, etc. Some common themes are however found in the different areas which include algorithms for problem-solving, developing quick and efficient methods to access data (e.g. databases), computer hardware architecture, the theory of computation (i.e.: Turing machines), formal languages (e.g.: regular expressions) and more recently, artificial intelligence (AI).
Computer science has its roots in the 19th century with Charles Babbage's Analytical Engine which was first described in 1837. The field really took off during WW2 with the need for intricate calculations to develop weapons of war, leading to more powerful and faster computers being designed.
The intersection of computer science and data science is found with the advent of Big Data. In order to process this huge amount of information, many algorithms from computer science have been employed with success. An example is Google's PageRank algorithm which was developed by Larry Page and Sergey Brin in the 1990s.
Key Differences between Computer Science and Data Science
Data science typically deals with data at rest. Computer science on the other hand traditionally deals with data in motion through a network connection.
Most algorithms used in data science are largely concerned about extracting knowledge from data as well as finding patterns or correlations between variables. In computer science, however, the majority of algorithms focus more on problem-solving and computational efficiency.
Computer science has many sub-disciplines like hardware architecture, software engineering, etc. Data science has traditionally focused more on statistics and machine learning.
Computer science is generally interested in problems that can be solved within the confines of a computer. Data science on the other hand is more concerned about knowledge extraction from data but will ultimately try to solve the problem at hand by employing computer science techniques.
Data Science vs Computer Science: The Science
Computer science is mostly about the theory of computation. It deals with how algorithms work, how they are executed by computers, and ways to improve their performance. Data science also focuses on algorithms but more so on methods to extract knowledge from data or information.
Data science devotes a lot of time towards scientific thinking, reasoning, and extracting insights from data. One of the main goals is to generate knowledge that can be used to solve problems in various fields including engineering, business, economics, etc.
Data Science vs Computer Science: The Engineering Side
Computer science programs teach students how to model and design algorithms while also detailing the limitations of computers when it comes to automating calculations. Data science students are taught to extract information from data using various techniques which can then be used to solve problems.
Computer science programs are more focused on theory while data science programs are more focused on practical applications of their knowledge. Data scientists use their programming skills to extract unknown patterns and trends in the underlying data, determine what should be done with these discoveries, and then implement a solution.
Computer science students will therefore be more knowledgeable about how computers work, what they are good at, and what they are not good at. Data scientists need to have this type of knowledge as well but their thinking is more towards what data needs to be acquired from where, the best way to extract information from it, and then how to apply that knowledge towards solving a specific problem.
Another difference is what skills and tools they will be using in the future. As technology advances, new tools and techniques are constantly being introduced to make data science more efficient and accurate. Computer scientists need to keep up with trends by acquiring skills such as Hadoop which enables them to manage large amounts of data. They also need to know the limitations of these tools to know what they are good at and what they are not so good at.
Data Science vs Computer Science: Salary Differences
According to Robert Half’s Salary Guide, the average salary for a software engineer is $178,000 per year, while a data scientist with equal qualifications and skills makes $155,000. However, the compensation of any professional is determined by a number of variables, an important one being location.
Software engineers in the San Francisco Bay Area earn up to 40% more than average pay in the United States, according to Payscale, demonstrating that Silicon Valley is still a dream destination for software developers. Data scientists also see a difference of 27 percent from the national average.
Seattle comes in second for both jobs, with pay that is above the national average. With technology giants like Microsoft, Amazon, and Facebook's engineering departments based in Seattle, this is nothing new.
Related: The Best Data Science Internships
Data Science vs Computer Science: Application and Advertised Job Titles
Computer science jobs are typically more prevalent in the industry than data science jobs. Apart from large corporations like Google, Amazon, IBM, and Microsoft which have many openings for roles such as machine learning engineer, software engineer, and data analyst, there is also a significant number of computer science jobs available with companies that are not in the technology sector - particularly in the field of Web Development.
Data science is still a relatively new field compared to computer science which has been around for decades. New job titles are constantly being created in the industry for employees to handle various tasks or responsibilities that did not exist before. Traditional jobs like software engineers, data analysts, etc are changing because of an increased need to accommodate new data science tools and techniques.
Given the huge market demand for both computer science and data scientists, it is expected that there will be a lot more job openings in the future having large numbers of applicants from various backgrounds such as mathematics, statistics, machine learning, etc.
Data Science vs Computer Science: The Future
Computer Science programs are becoming increasingly popular in universities throughout the world. It will take time but eventually computer science graduates will be more prevalent in the workforce. Hopefully, this will lead to a decrease number of technology companies outsourcing their software development and management activities to third world countries where labor costs are low.
Data science is a relatively new field compared to computer science but it has been gaining popularity at a much faster pace. Since the main way of doing data science is through the use of computers, it means that most if not all major corporations will need to hire data scientists in the near future.
Data Science vs Computer Science: Bottom Line
The bottom line is that both computer science and data science are very useful fields of study which have a significant overlap on what they teach students. Data science takes concepts taught in computer science and puts them into a practical context especially when it comes to the use of technology.
Computer science is an important field of study that helps students understand how computers work. It gives them the fundamentals they need to become productive members of society by teaching them critical thinking skills and preparing them for any problem they may encounter in the future. Data science is relatively newer than computer science but it is very useful because it enables people to use the knowledge they acquire to solve practical problems that are encountered in the real world. Data scientists give organizations the tools they need to analyze their data more efficiently and accurately, leading to better business decisions because of increased productivity and efficiency.
Data science is not computer science. They are both different fields requiring different skills and knowledge. While data scientists may need some knowledge of statistics, machine learning, mathematics, etc, this is not enough to consider them as computer scientists (although there might be some overlap between the roles). A data scientist needs to have a working knowledge of software engineering principles like programming languages (R, Python, Java, etc), database querying languages (SQL, HiveQL), distributed computing platforms (Hadoop, Spark, etc), and visualization tools/libraries.
On the other hand, a computer scientist does not need to know R or machine learning libraries but they are expected to have a working knowledge of algorithms and software design principles among many other things.