Computer Science vs. Data Science [What's the Difference?]
The main differences between computer science and data science are easily identified:
Computer science focuses broadly on computing design, architecture, and theory
Data science looks for insights in the mountains of information human activity creates daily
The two fields aren't entirely separate, however. Data scientists use tools and processes developed by computer scientists. Computer scientists' and data scientists' aptitudes intersect when it comes to things like programming skills and algorithm design.
And yet IBM data scientist Misra Turp is nonetheless correct when she writes, "You can do data science without any knowledge of computer science, and you can do computer science without any knowledge of data science."
Confused? Don't be. Most people know instinctively whether they belong on one side or the other of the divide. Computer science is the better path for people fascinated by software, hardware, and pushing the limits of what computers can do. Data science is the better path for people obsessed with pushing the boundaries of statistics, machine learning, AI, and heuristics.
The good news is that both of these academic and career pathways are promising. Computer science salaries and data science salaries are both relatively high. Data science has been described as one of the sexiest tech fields, and becoming a data scientist isn't particularly hard. Computer science is equally enthralling to those who love it, and even entry-level computer science jobs come with great perks.
So, where do you belong? We can help you figure that out.
What is computer science?
Computer science is a broad interdisciplinary field that's hard to sum up in just a few words. That doesn't stop people from trying, though. People say it's the foundation of all computing, but the term "computing" encompasses everything from hardware and networks to data modeling and algorithm design—plus AI, cyber security, and information technology. The BBC writes that "Computer scientists design new software, solve computing problems, and develop different ways to use technology."
What sets computer science apart from disciplines like computer engineering, software engineering, and IT is its breadth. Computer science focuses primarily on computational theory, processes, and pushing the limits of what computers can do across functions and industries, along with the ethical issues inherent in technology's spread.
There are many areas of specialization in CS. What you can do with a degree in computer science will depend on your interests and aptitudes (more on this below).
Computer science definition
The definition of computer science differs depending on who is defining it. Computer scientist and researcher Peter Denning defines it as the evolving answer to the question "What can be automated?"
Dr. Allen Tucker, Bowdoin College professor and Fulbright lecturer has defined computer science as both "the study of computers and algorithmic processes, including their principles, their hardware and software designs, their applications, and their impact on society" and "the study of computers and computing, including their theoretical and algorithmic foundations, hardware and software, and their uses for processing information."
Computer scientist history
Humans have been using computational devices for thousands of years. For most of human history, these devices were analog, like the abacus or the Antikythera mechanism. The first electronic digital computers were created in the 1940s to streamline ballistics calculations and help crack codes in World War II. Scientists around the world rushed to create functional electronic digital computers that could store programs and data. In the 1950s, innovations in programming helped plant the seeds of modern AI.
Computer science arose as an academic discipline in the 1960s. Numerical analyst George Forsythe coined the term, and Purdue University formed the first computer science department in 1962. Ted Hoff and Federico Faggin designed the first microprocessor. IBM launched multiple computer lines. And computer scientists began building ARPAnet, a precursor to today's internet.
The computer revolution dates back to the 1980s, however, when personal computers became more common, and businesses embraced the power of the PC. In 1987, the US National Science Foundation launched NSFnet, a precursor to parts of the modern internet. Then came quantum computing, biological computing and bioinformatics, nanotechnology, machine learning, and the internet as we know it today.
Famous computer scientists
Howard H. Aiken built one of the first electromechanical computers in 1944
Frances E. Allen is famous for her contributions to program optimization and parallel computing
Leonard M. Adleman helped create one of the first public-key cryptosystems
John Bardeen, Walter Brattain, and William Shockley invented the transistor and helped usher in the microprocessor revolution
Kathleen Booth wrote the first assembly language
Grace Murray Hopper invented the compiler and is credited with finding the first computer bug (literally; it was a moth, to be more precise)
Jack Kilby and Robert Noyce invented the integrated circuit
Computer science principles
There are entire websites and thousands of books devoted to computer science principles, making it tough to nail down the fundamental principles of computer science. However, certain principles are essential to multiple branches of the field. These include:
Order in space
What is data science?
Data science is easier to summarize than computer science. This discipline focuses almost entirely on collecting, organizing, and analyzing data and can be described as a mix of math, statistics, and computer science. Data scientists use various computational techniques—coupled with practical statistical analysis—to turn vast quantities of information into actionable insights. People associate data science with business intelligence, but data science has applications in fields as diverse as healthcare, economics, operations research, and athletics.
Data science definition
According to Oracle, "Data science combines multiple fields, including statistics, scientific methods, and data analysis, to extract value from data." Much like computer science, however, data science is often defined in terms of what data scientists do.
Data science history
People have always analyzed data to find out more about the world around them. The earliest people to leverage the power of information may have been ancient farmers who used historical data about weather and crop yields to draw conclusions about future harvests. Modern data science is much younger than computer science, however. People only began using computers for statistical analysis in the 1960s; soon after, an analysis that used to take weeks could be accomplished in hours.
Some people credit William S. Cleveland as the first to use the term 'data science' (in 1999). However, the term appears repeatedly in Peter Naur's Concise Survey of Computer Methods, published in 1974. Naur defined data science as the "science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences." In the late 1970s, the International Association for Statistical Computing was formed. The organization's mission was to "link traditional statistical methodology, modern computer technology, and the knowledge of domain experts to convert data into information and knowledge."
Organizations began using data mining in the 1980s and by the 1990s, at a time when companies were already gathering relatively large amounts of personal customer data. The problem was that the technology necessary to handle this new influx of information didn't yet exist. Jacob Zahavi wrote that late-90s databases "involve millions of rows and scores of columns of data… Another technical challenge is developing models that can do a better job analyzing data, detecting non-linear relationships and interaction between elements… Special data mining tools may have to be developed to address website decisions."
A decade later, those tools were commonplace. In 2011, listings for data science jobs increased by 15,000 percent. Today, jobs in data science are being created faster than colleges and universities can train data scientists.
Famous data scientists
Yoshua Bengio helped design the techniques used to combine huge quantities of data with artificial neural networks; his work led to major advances in natural language processing
Corinna Cortes is known for her contributions to the theoretical foundations of support vector machines and her work on data mining in enormous data sets
Leslie Kaelbling is known for her work involving mobile robotics, reinforcement learning, and decision-theoretic planning
Yann LeCun, the Director of AI Research at Facebook, developed the convolutional neural networks that enabled modern deep learning
Dr. DJ Patil helped Facebook create its first data science program and is credited with popularizing data scientist as a job category
Data science principles
Data science is an evolving discipline, which means data science's core principles are subject to updates. Elements of data science include:
Quantifiable end goals
What degrees do I need to advance in computer science?
You don't necessarily need a master's in computer science—or any degree—to advance in this field, but having a degree from an accredited computer science program can enhance your career prospects and boost your earning potential. The median annual salary for CS bachelor's degree holders is $85,000, while the median annual salary for MSCS graduates is about $102,000. More doors are open to job candidates with advanced degrees.
With a computer science bachelor's degree, you can work in database management, engineering, development, and network architecture. You might not earn as much as experienced computer scientists with master's degrees, but you'll almost certainly earn a salary higher than the national average. Master's degree holders can apply for managerial- and executive-level computer science jobs and command higher salaries.
What degrees do I need to advance in data science?
Undergraduate data science degrees are much less common than data science master's degrees. That may be because the master's is generally regarded as an entry-level data science degree. Many people launch their careers not with a BS in Data Science, but with a BSCS. As is the case with comp sci programs, data science programs tend to look for applicants with strong grades and SAT or ACT scores, plus extracurricular experiences centered around computer science, analytics, or technology.
There's almost no way around it. The answer to the question 'Do I need a master's in data science?' is yes. The top data science master's programs are typically geared toward students with some experience in business analytics and advanced technology or a background in computer science, computer engineering, or statistical mathematics. Given that almost half of all data scientists have doctoral degrees, you should seriously consider earning a doctorate. Data science doctorate programs tend to be small and competitive. Schools often look for candidates with extensive work or research experience in data science plus advanced degrees in mathematics, statistics, computer science, or engineering.
Are data science and data analytics the same?
The difference between data science and data analytics isn't clear-cut. Data analysis manipulates information in many of the same ways data science does. The techniques used by data analysts and data scientists overlap significantly. Some data science experts even use these terms interchangeably.
In general, however, a distinction can be drawn between the level of technical expertise required in each discipline. Data science jobs tend to be much more technical than data analytics jobs, with more programming and mathematical modeling involved. That may be why data scientists are more likely to have advanced degrees.
Career paths for computer scientists
Computer science jobs exist in just about every industry. CS professionals work for health networks, retail firms, research laboratories, marketing agencies, and manufacturing plants. "One of the greatest things about a computer science degree is that it allows you to work in whatever industry you desire," Sam Gavis-Hughson, CEO and founder of Byte by Byte, told US News & World Report.
Average salary for computer scientists
According to PayScale, average salaries in computer science are around $79,000 (a figure generated using entry-level through executive pay). Average salaries aren't particularly illuminating, however, because job title, location, education, and other factors all play a role in how much computer science jobs pay.
Top employers for computer scientists
The companies newly minted comp sci grads want to work for include:
The Walt Disney Company
You need to know that big tech firms aren't necessarily the biggest employers of computer science professionals.
Career paths for data scientists
Titles in data science are driven by company naming conventions. Some employ data scientists, but others create data science roles with titles like:
Data visualization specialist
Domain expert analyst
Machine learning scientist
Be aware that data science jobs are seldom entry-level roles.
Average salary for data scientists
According to Indeed, the average data scientist earns about $123,000. Other salary aggregators find that data scientists earn a little more or a little less, but most report that these professionals tend to earn over six figures.
Top employers for data scientists
According to Diffbot's 2019 State of Data Science, Engineering & AI Report, the companies that hire the most data scientists include:
Should I become a computer scientist or a data scientist?
Data science is still hot, and jobs for data scientists still go unfilled, even if the applicant pool for open positions is growing. Computer science as a field is also a seller's market, with fewer qualified professionals than jobs. Computer scientists and data scientists both enjoy job security and excellent pay, which means that choosing between these fields isn't a matter of picking the one with the better benefits, but rather a matter of looking closely at your aptitudes and interests. Computer science vs. data science? It's a no-brainer. If you like to solve puzzles with information, you might be happiest in data science, but if you prefer to build new things, then computer science might be the better fit.