Career Differences, Computer technology and big data impact many aspects of modern life, from the way we shop to how we navigate cities and connect with others via social media platforms

1. Data Science vs. Computer
Science: Career Differences
Career Differences, Computer technology and big data impact many aspects
of modern life, from the way we shop to how we navigate cities and connect
with others via social media platforms. Data scientists and computer scientists
play an integral role in creating these digitally driven tools, making them user-
friendly, and tapping into their potential to help companies refine their
products and services. That said, the precise roles of professionals in data
science vs. computer science differ significantly in terms of output and
required skill set.
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Data Science vs. Computer Science: Knowing the Key Differences
Data scientists look for meaning in large swaths of data using tools such as
data visualization, data mining, and predictive statistical analysis. They develop
the infrastructures needed for analytics, testing, developing decision-making
through machine learning, and refining final data products.
Computer science, on the other hand, refers to the study of computing design
and architecture. Computer scientists develop computer hardware, software,
and computing networks, working with tools that range from programming
languages to artificial intelligence. They invent and design new approaches to
computing or find innovative ways to use existing computing methodologies.
It’s worth noting that data scientists and computer scientists can have cross-
functional roles. However, data scientists tend to work closely with top
executives to gain insights into company goals. With this understanding they
can strategize ways to leverage company data to improve products and
services. Computer scientists, who typically have backgrounds in computer
engineering, tend to focus on software design and automation.

2. Both jobs are integral to our modern world. A computer scientist might work
as a software developer, creating the codes that underlie the mobile and
computer applications we use to navigate, such as Google Maps. A data
scientist might work to analyze the data generated by such apps. For example,
tracking the most popular driving routes of Google Maps users can provide
useful information to improve the functionality of the app.
Data Science vs. Computer Science Career Outcomes
While data science and computer science are distinct fields, the U.S. Bureau of
Labor Statistics (BLS) combines the two in its projections. Professionals in both
fields can expect a promising job outlook in the coming years. The BLS
projects positions in data science and computer science to increase 22%
between 2020 and 2030 — much faster than the 8% average projected for all
occupations.
Careers in Data Science
Data science experts can choose from a number of careers. The following
positions represent some of the options professionals in the field can pursue:
Data Analyst
Data analysts evaluate data to answer questions and help solve business
problems. By examining trends and patterns in data, they help companies
determine how to lower costs, price products, and improve customer
retention. According to September 2021 PayScale data, data analysts have a
median annual salary of around $62,000.
Computer Systems Analyst
Computer systems analysts examine computer systems and procedures to
determine their efficiency and effectiveness. Based on their findings, they
innovate solutions to problems and leverage opportunities to make
improvements. This involves researching new technologies to determine their
costs and benefits, as well as making recommendations for infrastructure

3. upgrades. In May 2020, computer systems analysts had a median annual salary
of $93,730, according to the BLS.
Data Architect
Data architects create frameworks that manage an organization’s data. The
frameworks they design define the processes used to create, acquire, store,
control, access, and purge data. They collaborate with upper-level
management and other data science experts to evaluate data needs and
strategically leverage company data. Data architects have a median annual
salary of approximately $122,000, according to Pay Scale data as of September
2021.
Applications Architect
Applications architects design the major architectural elements of apps. This
includes the infrastructure, user interface, and middleware. They also conduct
design and code reviews and serve as the technical leaders of application
development teams. Applications architects have a median annual salary of
approximately $117,000, according to PayScale data as of September 2021.
Machine Learning Engineer
Machine learning engineers research, build, and design artificial intelligence
(AI) systems. This involves developing AI algorithms that can make predictions,
choosing appropriate data sets and representation models, and determining
data differences that may influence the performance of models. Machine
learning engineers have a median annual salary of approximately $113,000,
according to PayScale data as of September 2021.
Careers in Computer Science
The field of computer science offers an array of career opportunities for those
with the right skills and expertise. Some in-demand positions in the field
include the following:

4. Software Developer
Software developers design computer applications and programs. This
involves analyzing users’ needs and creating software that meets those needs.
Software developers also test and debug applications, making sure programs
run smoothly. In May 2020, software developers had a median annual salary of
$110,140, according to the BLS.
Computer Hardware Engineer
Computer hardware engineers find ways to increase the power and speed of
computers. They accomplish this by researching, designing, and testing
computer systems and their components. They are also responsible for
updating computer equipment and maintaining compatibility between
computer components and new software. According to the BLS, computer
hardware engineers had a median annual salary of $119,560 in May 2020.
Computer Network Architect
Computer network architects construct data communication networks such as
intranets and local area networks (LANs) that can connect multiple users. This
involves designing infrastructures that safeguard data, upgrading hardware
and software, and troubleshooting problems. According to the BLS, computer
network architects had a median annual salary of $116,780 in May 2020.
Database Administrator
Database administrators construct systems that organize, store, and secure
data such as shipping records, financial figures, and user account information.
They also monitor databases to identify slowdowns, troubleshoot problems in
the system, and back up data to prevent loss. Database administrators had a
median annual salary of $98,860 in May 2020, according to the BLS.
Computer and Information Research Scientist
Computer and information research scientists develop new uses for existing
technologies to help solve problems in other fields such as science, business,

5. and medicine. This involves studying computing problems and creating
models and theories to fix them. It may also involve creating new software
systems or computing languages. In May 2020, computer and information
research scientists had an annual median salary of $126,830, according to the
BLS.
Data Science vs. Computer Science Required Skills
To thrive in any of these careers, professionals in both fields need specific
skills and knowledge.
Data Science Skills
Data scientists ultimately strive to give data meaning. For example, an e-
commerce company like Amazon may rely on data scientists to develop tools
for gathering and analyzing customer purchase and browsing data, allowing
the retailer to make targeted suggestions as to what consumers should buy.
Individuals who want to excel in a data science career must master the
following skills:
Critical Thinking
The first step for a data scientist is to identify the problem or question they’re
trying to resolve. With this in mind, they can decide how to best present data.
Critical-thinking skills are crucial to determining what metrics will most fully
answer the data question at hand.
Mathematical Knowledge
Data scientists must know how to manipulate data to create understandable
visual representations that can be used to make data meaningful.
Mathematical knowledge is needed, in particular a thorough understanding of
statistical modeling.

6. Communication
The outcomes of data analysis usually must be relayed to other people, such
as coworkers or senior company leaders. Verbal and written communication
skills are key, as are visual communication abilities such as being able to create
compelling and easy to understand charts and graphs.
Risk Analysis
Data scientists must frequently consider business risk when working with data
and making proposals. Their work is not conducted in a vacuum. They must
consider the overall purpose of their work and the data, such as capitalizing
on certain customer purchase patterns.
Programming Skills
All data scientists must possess certain technological skills, but different
employers will have different demands. Python is the most popular
programming language employed by data scientists. Others include R; SQL,
which can also be combined with PHP or JavaScript for web applications; and
Hadoop and Spark, two forms of open-source software from Apache that are
used for big data development and testing.
Computer Science Skills
Computer scientists design and perfect computer hardware, software, and
networks. For example, a computer hardware engineer may design, develop,
and test components like circuit boards and memory devices.
Individuals who wish to excel in a computer science career should master the
following skills:
Analytical Skills
When finding solutions to problems, computer scientists may have to work on
code that was developed by other people. They must have the analytical skills

7. to not only evaluate code but identify potential problems and ways to
improve it.
Creativity
Career Differences computer scientist must be able to think outside the box to
develop innovative solutions to problems. Often, the solutions don’t exist
within traditional channels and frameworks, requiring them to develop novel
approaches.
Critical Thinking and Problem-Solving
Computer scientists must be able to think critically when designing new
processing methodologies. Effective problem-solving allows them to identify
what will and won’t work, which saves time in the development and testing
phases.
Programming, Scripting, and Markup Languages
Career Differences data scientists, computer scientists need knowledge of
Python and SQL. They must further master other programming languages
such as HTML/CSS, JavaScript, C++/C#, and Java.
Understanding Source Control
When creating code, multiple team members often work on various parts of
the code and then bring them together to create a final product. Computer
scientists must be adept at source control, which ensures all parts come
together in a uniform whole at the end.
Understanding the Software Development Life Cycle (SDLC)
Career Differences SDLC provides an overview of how software is produced.
Computer scientists must understand the different needed iterations ahead of
each new software release, starting from the minimum viable product (MVP)
and moving to the final product.

8. How Graduates Benefit from a Bachelor’s in Data Science
Career Differences Individuals who are interested in learning more about the
differences between data science vs. computer science can benefit from
exploring higher education options. An ideal program to investigate is the
Maryville University online bachelor’s in data science. The program covers such
in-demand topics as computer science, programming, predictive modeling,
and data analysis. Relevant coursework includes Foundations of Data Science,
Mathematical Statistics, Introduction to Python, and Introduction to SQL.
Learn more about Maryville’s online degree program, how to enroll, and how
the program prepares students for careers in today’s rapidly progressing
technology landscape.