People who are interested in a career in the tech industry may be considering whether to study data science or computer science. It is easy to confuse these two specialities, as they are common in many ways—but there are some key differences between data science vs. computer science.
In simple terms, computer science is the study of systems that make computers function, including operating systems, programming, networking, software development and security. Many people in the tech world consider data science to be a branch of computer science, merging elements of that field with statistical analysis to comb through and make sense of data in order to apply it in meaningful ways.
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Data Science vs. Computer Science: Basic Concepts and Courses
Data science is the study of data in structured, semi-structured or unstructured forms. An individual working in the field of data science collects, processes, stores and analyzes data to gain insight and find solutions to problems.
On the other hand, computer scientists focus on the functional workings of computer systems, software, networks, databases and other aspects of computing.
While these two fields are quite similar, they have distinct differences in the coursework and concepts necessary to work in either field. By taking a closer look at the details, the differences and similarities between these two related sectors can be better illustrated.
Data Science Concepts and Courses
Data scientists collect and analyze data in various forms for a particular purpose or to address a specific question or problem. Data mining, or the process of finding patterns in data, is the primary objective for a data scientist.
Professionals in this specialized career also work on data transformation, which involves changing the structure of data. Data scientists may work with data engineers to create systems for storing and analyzing data.
Statistical analysis and a focus on manipulating data are what set data science vs. computer science apart. More and more schools are offering data science programs that teach specific skills. The courses and concepts listed below are essential for an individual interested in embarking on a career in data science.
- Machine Learning Algorithms: Machine learning is a subset of artificial intelligence that instructs a machine how to learn. These courses focus on three core machine learning algorithms: supervised, unsupervised and reinforcement learning.
- Statistics: Statistics is the collection, analysis and interpretation of data. It’s a fundamental tool for data scientists, as their primary objective is to gather, analyze and communicate data. There are various statistical methods used to analyze data, such as descriptive and inferential.
- Artificial Intelligence: AI refers to the branch of computer science that enables machines to perform tasks that mimic human behavior. There are two types of AI—narrow and general—and depending on the course, you may learn both.
- ARIMA Modeling: Autoregressive integrated moving average, or ARIMA, is a type of statistical model to measure events that happen over a period of time. Students may learn how to use ARIMA in financial analysis.
- Neural Networks: A neural network is an assembly of simple, interconnected processing units inspired by a biological neuron. The networks have multiple layers and each layer learns from the preceding layer, and the last layer processes information for the output.
- Database: A database is a system that stores and organizes data. Database courses will show students how to set up, maintain and utilize the different types of databases organizations use.
- Linear Regression: Linear regression is a common predictive modeling technique. It’s used on data with a linear relationship, meaning the best fit line between the data points is a straight line.
- R Programming: This programming language is used by data scientists for statistical analysis and data visualization. It’s described by the R Foundation as “a language and environment for statistical computing and graphics.”
- Python: As a general rule of thumb, this is a general-use, high-level programming language that’s a common tool used in data science. Programming courses in Python will go over the different libraries that can be used in the data science field.
- Decision Trees: This is a type of supervised machine learning that’s used to categorize and make predictions based on how a previous set of questions were answered. They imitate human thinking, making it easier for data scientists to interpret results.
- Bootstrapping: This is a method of inferring results for a population as a whole based on a collection of smaller, random samples of that same population.
- Logistic Regression: This is a supervised learning algorithm used to predict the probability of an event occurring given the input variable, such as “win/lose” or “yes/no.” Courses in this subject focus on three main types of logistic regression: binary, multinomial and ordinal.
- Probability Theory: Courses on probability theory focus on analyzing random phenomena. The goal is to determine whether or not an outcome will occur.
- Undersampling: Undersampling is a technique used to balance uneven datasets by keeping all of the data in the minority class, and decreasing the size of the majority class. It is a common and important technique students will utilize in machine learning courses.
- Git: Git is an open-source tool commonly used by software developers to code collaboratively. The system allows programmers to work offline or remotely, and sync the local copy of the software with the main copy.
Computer Science Concepts and Courses
Computer science focuses on the design and operation of computer software, hardware and systems. Think about the apps and websites that are used daily among the worldwide population. The people who develop and maintain these applications typically have a computer science degree.
There are a variety of specializations within computer science, like cybersecurity or building user interfaces for websites and apps. In many ways, every specialty within computer science relies on other specialties. These different “tech building blocks” make up the various disciplines of the field.
Computer science courses also cover the ins and outs of web and app development. Specifics include:
- Programming Languages: There are several types of programming languages that computer scientists use in their everyday work. Each language has differences and similarities that make them ideal for certain tasks, and some are more widely used than others. Some programming courses include:
- C: C is used to create software for computers, embedded systems and various other devices. It allows direct access to memory and low-level operations within a system.
- MySQL: MySQL, or structured query language, is used for querying databases. It’s considered the standard query language for relational database systems.
- PHP: This is a general-purpose scripting language used for web development that is able to embed into HTML. PHP simplifies HTML inputs and requires fewer commands than C.
- Data Structures: A data structure is a collection of values. Data structure and algorithm courses focus on how said structures work, the different types available and how to use them.
Careers in Data Science vs. Computer Science
Data scientists and computer scientists can have different career options. However, in some instances, their job opportunities may overlap. Here are some of the current career options for data science and computer science specialists:
Data scientists and computer scientists have many different career options available to them and, in some cases, their job opportunities overlap. Each field requires a great deal of statistical knowledge, technical skills, attention to detail and critical thinking, and each skill may be utilized in different ways depending on an individual’s chosen role. Here are some of the current career options for computer science vs. data science and what each role entails.
Data Science Careers
As a result of the increasing demand for new technology, the demand for computer and information research scientists, which includes data scientists, is growing, too.
Here are some of the most prominent examples of data science careers:
- Data scientists combine analysis with machine learning, data mining, statistical methods and computer science fundamentals. People in this career can go far beyond just analyzing data. They need to understand the entire data pipeline and be able to design and manage projects, from information collection to analysis to creating reports about the results of their studies.
- A data analyst collects, organizes and analyzes data to gain useful insights. They typically use programming knowledge and create algorithms to help them manipulate and organize data. However, the programming requirements are generally more relaxed than those of a data scientist. The role of a data analyst is often an entry-level job for those with ambitions to become full-fledged data scientists. Additionally, some data analysts become experts in a specific area, such as health care or public policy.
- Data engineers design, build and manage the information systems that collect, store and retrieve the information that data scientists and analysts use. A data engineer focuses on the architecture and operation of the data pipeline. They are not concerned with analyzing data.
- A data architect and data engineer relate in the same way as a traditional architect and engineer do. The data architect designs the data management pipeline, while the data engineer builds it. There is considerable overlap between these two positions. In smaller companies, one person may handle both design and implementation duties.
Computer Science Careers
A computer scientist may work in a variety of roles and settings. Computer science degrees are the basis for many IT jobs. These are some common career paths in this field:
- Software developers create, test and improve software and applications. Developers write their programs using computer languages. They are responsible for testing, debugging and updating software as well as the initial coding.
- Software engineers go beyond software development. They don’t just carry out the coding and testing; they use computer science and engineering principles to design software from the ground up. An engineer may be involved in developing software from scratch or creating an entire enterprise system.
- Video game developers design and build video games. They create and test the code that makes it possible for players to interact with the game. A video game developer may work on a specific aspect of the game, such as a particular level. In this career, you work with artists, designers, writers and programmers on development projects.
- Cybersecurity specialists develop security skills for making sure that networks, computer systems and websites remain secure. A cybersecurity specialist may design security systems that protect networks from hackers. They also usually monitor the networks and update security software to respond to new security threats.
- Computer and information research scientists work with, invent and design new approaches to computing technology and find new ways to use existing technologies. They study and solve complex problems in computing that can be used in various settings, like business, science, medicine and more.
Computer Science Skills vs. Data Science Skills
While some skills can be applied to both computer science and data science, others may be dependent on your specific career. Both data science and computer science professionals understand fundamental concepts such as debugging and testing, coding and creating algorithms. In some cases, data scientists earn a bachelor’s degree in computer science before studying data science at the master’s level.
The skills necessary for a career in data science vs. computer science vary. Below, we outline specific knowledge crucial to each of these fields.
Data Science Skills
A data scientist needs specific technical skills they can learn in school or at a data science bootcamp, but they also need to develop traits that help them perform their job well, including:
- Data scientists need to know specific computer languages, database frameworks and systems. These may include R, Python, Java, C++, Matlab and SQL. These are the tools that will allow them to source, store, retrieve and manipulate data.
- Math and statistics are necessary for analyzing data.
- In some positions, data scientists use business analytics tools like MicroStrategy, Business Objects and Tableau.
- Data scientists need to create and work with algorithms to manipulate data and automate the analysis process for large amounts of data.
- Data scientists also need advanced math skills to perform their analysis.
- Critical thinking skills help with data analysis, but they are also vital for planning a project, defining goals and parameters, and working with other data science professionals.
- Data scientists need to be team players. In almost every job, you work as part of a team, so you need to be able to coordinate with others.
- Problem-solving skills are also vital. These abilities can help you design projects that address a specific problem, but they can also aid you if issues arise during analysis.
Computer Science Skills
Computer scientists may learn the fundamentals of computer science through a bootcamp or degree program. They also need to have specific skills related to their area of expertise, which may require earning a master’s degree in the discipline.
- Computer science positions usually require knowledge of design, development and testing methodologies as well as frameworks.
- Math and statistics are vital in many computer science careers. However, a basic knowledge of mathematics can be sufficient for many computer science jobs.
- Organization skills are essential for computer science jobs. These careers require that you get the small details correct.
- Computer science professionals rarely work alone. They are usually part of a team of developers, engineers or tech professionals. Even though you often work alone at a computer terminal, you need to continually communicate and coordinate with other members of a team.
- Problem-solving skills are also vital in computer science fields. Most jobs involve testing systems, software or code. You need to be able to locate and fix problems calmly.
Both data science and computer science can seem abstract on the surface. In either of these careers, you do not have direct contact with your clients or customers. Data science vs. computer science entail different responsibilities and day-to-day tasks, but they both have real-world applications.
Data scientists can perform an analysis of customer behavior patterns to help companies design products and services that meet customer demand. Some data scientists specialize specifically in this type of business analytics.
A more visible use for data science involves coming up with algorithms that offer suggestions to users on websites. For example, on streaming services such as Netflix, an algorithm created with the help of data scientists predicts which shows you will like based on your past viewing history.
Some analysts, known as geospatial scientists, collect data from the environment, places or populations. They often analyze geographic data for companies, governments and urban planners.
Computer science has led to greater data efficiency. Without efficient data platforms, you would not be able to stream video, for example, and sites like Netflix and YouTube would not exist.
The world’s financial systems are also computerized. The digitization helps banks and investors operate more efficiently and it also gives consumers access to financial services online.
One new area of development for computer scientists is remote work. Project management platforms and cloud computing systems, for example, allow employees to work remotely without sacrificing efficiency or the ability to communicate.
Should I Study Computer Science or Data Science?
For individuals who are interested in working with analytics, statistics and technology, both computer science and data science are viable career options. However, there are prominent distinctions between these fields that one should consider before deciding between a career in computer science or data science. The final decision is entirely up to the individual and what skills they want to gain, their personal interests and their overall career goals. Choosing a specific field is the first step in embarking on a career in technology, and each individual should carefully consider their desires and the specifics of the career before making a decision.
Last updated January 2022.