Computer Science vs Data Science: Similarities, Differences, and Role of Data in Computer Science

Computer science has been around for a while. Relatively speaking, data science is a new field that has generated a lot of curiosity, given its wide range of applications and increasing demand. While both overlap in many instances, it is worth exploring the facets of computer science vs. data science.

The confusion between computer science and data science arises because these fields often go hand in hand.

The proximity of both fields leads to confusion regarding their similarities, differences, job roles, and dependence on each other. Data scientists expect proficiency in coding.

 In contrast, computer science professionals need to have a working knowledge of data structures and other data basics. This requirement of knowledge about each other’s field adds to the confusion.

This article aims to clarify many confusions by discussing the difference between data science and computer science.

What are Computer Science and Data Science?

To understand data science vs computer science and their commonalities, you must first understand them in isolation.

 Both are stand-alone disciplines and serve specific purposes .

Let’s first start by understanding the field of data science.

What is Data Science?

You can understand the field of Data Science by examining the nature of a data scientist’s primary job role offered in this discipline. Typically, data scientists first learn about statistics, machine learning algorithms, and the basic structure of data.

They then apply these theories by learning programming and computer science at a later stage. This serves as a prelude to the subsequent discussion on the involvement of computer science in data science.

Data scientists primarily engage in data exploration to enhance decision-making and contribute to an organization’s growth. They also develop products that require the utilization of backend models.

They often focus on statistical modeling and machine learning algorithms. Data science generally tries to make processes more efficient and accurate.

Typical tasks that data scientists perform include-

• Accessing and exploring company data
• Understanding business problems and their impact
• Developing data models and optimizing them currently in production
• Reporting analysis using visualization
• Collaborating with MLOps engineers and computer scientists

Next is the time to understand what computer science is all about.

What is Computer Science?

It isn’t easy to describe the nature of computer science. In a way, this is also becoming increasingly true for data science. It’s because of the various job roles that fall under these disciplines. For example, in data science, one can be involved as a machine learning engineer, data engineer, data analyst, business analyst, etc., with the prime role being a data scientist.

Similarly, in computer science, while the primary job remains of a computer scientist, several specialized job roles are parts of this field. Like, as a system analyst, web developer, hardware engineer: network architect, and other IT-related job roles. Still, by and large, the central theme of this field remains software engineering and any associated work.

Typical tasks that a computer scientist/software engineer / IT professional performs include-

• Exploring business problems, products, software, etc.
• Identifying problems and defining requirements
• Developing software to solve problems and perform unit testing
• Integrating the software into pre-existing systems and understanding their impact
• Performing day-to-day software-related operations and maintenance

Now, as you better understand what these two fields are all about, let’s know CS vs data science by understanding computer science’s role in data science.

Role of Computer Science with Data Science

As mentioned earlier, computer science plays a crucial role in data science. And this assertion can be easily substantiated by the fact that all data scientists must acquire programming skills.

Let us explore the specific areas of data science where computer science involvement is particularly prominent.

• Data Collection

Data Science revolves around data, and data collection is a significant component of data science. The use of computer science allows data scientists to access and deal with big data.

As big data involves computer science- heavy concepts like map-reduce, master-slave, etc., its importance in data science becomes heightened.

Typically, computer engineers are aware of such concepts, and due to the increasing use of big data in data science, data scientists need to get familiar with this aspect of computer science. 

Also read: Spark for Data Science and Big Data Applications

• Data Pre-processing

Data science follows the principle of garbage in and garbage out. This means that if the data on which a model is trained or analysis is done is of inferior quality, the results will be flawed too.

Therefore, it’s essential that the selected data is of the right kind. Also, it should be joined and appropriately managed and contains the information that can resolve the issue. Here computer science gets involved.

SQL is something that most computer scientists are familiar with, and know how to connect and manage databases.

Also read: Why learn SQL for Data Science

Exploratory Data Analysis

Data scientists need to have a basic understanding of languages like Python and R. However, if they have limited capabilities in coding, it takes a long time to perform preprocessing.

It can cause delays in solving imminent business issues. Computer Science here gets involved. As sophisticated and complex ETL operations can be performed using in-depth programming, data scientists can go the extra mile when analyzing.

Visualization

While simple visualizations can be done without heavily relying on computer science, advanced visualization issues require computer science.

For example, a good level of coding effort is required to create complex graphs. Also, if dashboards are to be made that are to be hosted on web servers, then computer science skills are needed.

Machine Learning

The traditional outlook of data science involved statistical modeling that heavily relied on models that could predict outcomes and had high interpretability. Therefore, such models could answer the ‘why’ questions also. Lately, machine learning has taken center stage, which solves more of the operational problem.

Now, interpretability is not a significant concern. Here computer science is relied upon as the term machine learning itself was invented by the discipline of computer science. The computer science mindset can help here.

The main focus is creating a black box through efficient algorithms that provide highly accurate predictions. Thus, the involvement of machine learning in data science has been possible due to computer science.

Also read: Best Machine Learning Tools to Learn

Deep Learning

When AI and deep learning come into play, computer science is paramount in data science. The boundaries between AI and data science have become blurred and intertwined.

Also read: Introduction to Fundamentals of Neural Network and Deep Learning

Acquiring knowledge about AI is now regarded as an integral component of data science.. AI solves complex problems that heavily rely on computer science.

•   Speech Recognition  

Problems like speech-to-text or automatic speech recognition involve data science concepts. But require deep knowledge of various computer science fields and other fields like electrical engineering and linguistics. The Speech recognition software employs data preprocessing, big data analytics, machine learning, and deep learning models.

Also read: Difference between Machine Learning, Pattern Recognition, and Data Mining

•   Image Recognition  

Another major application area of deep learning is recognizing human faces and creating various products using this capability. Here again, computer science gets heavily involved. It combines computer hardware like cameras and software based on deep learning that can provide the solution.  

In short, computer science is heavily involved in data science today, and it’s no longer a statistics or mathematics-heavy field. However, this discussion of CS vs data science can only be completed by discussing the pros and cons of the involvement of computer science in data science.

Also read: What is Image Segmentation

Pros and Cons of Computer Science in Data Science

When exploring data science vs computer science, one must discuss how the involvement of computer science in the field of data science has its advantages and disadvantages. The involvement of computer science in data science is increasing. Hence, their interdependence has led to discussions of the benefits and disadvantages such involvement poses.

Pros

•  Headstart

Computer science can give you a good start in the technical aspects of the data science field. It allows for a good chunk of human resources to be available for the data science domain. They must learn the theoretical aspects, such as statistical and machine learning algorithms, and start in the field.

• Scaling and Optimization 

Computer science facilitates the scalability of products and solutions generated by data science. Additionally, optimization is achievable since code serves as the foundation for all operations within the realm of data science.

•  New Outlook 

Data scientist employs individuals from a computer science background generally. Because of this, new perspectives are constantly brought to the table. The viewpoints are not limited to statisticians and mathematicians, helping the field of data science overall.

Cons

•  Over-involvement in Computer Science 

Data Science and Computer Science are interdependent. This gives rise to the problem of individuals developing a ‘computer science mindset’.

Every problem is solved like an operational problem requiring a software solution. Data science has become less of a decision-making tool and more of an automation tool.

All these eventually lead to data scientists and other professionals in this field needed to focus on its business aspects. Spending less and less time on gaining business knowledge and getting involved in the business application aspect of the field.

Therefore, it’s important to remember that data science is not computer science and must maintain its unique characteristics.

•  Less Space for other fields 

Data Science is an amalgamation of multiple fields, such as statistics, programming, mathematics, reporting, and business decision-making. In countries like India, there is a high saturation of computer science professionals.

As a result, a significant trend emerges where computer science professionals dominate the field of data science.

This dominance leads to limited opportunities for individuals with backgrounds in statistics and mathematics despite their crucial role in the practical functioning of data science. However, these individuals play a crucial role in the practical functioning of data science.

The fact is that you have to live with the pros and cons mentioned above. However, let’s also discuss the other side. You now know the involvement of computer science in data science.

However, it’s also vital to discuss vice versa, i.e., the role of data science in computer science and the amount of data involved in it. We discuss how data is found in computer science and the role it plays there.

What is data in computer science?

Data in computer science is critical as it allows machines to communicate and is responsible for the functioning of the whole computer science ecosystem. While data as such can be just a sequence of symbols, upon interpretation, it can become information. Data can be digital, using binary numbers, or represented through analog signals.

• Involvement of Data Science in Computer Science

Computer Scientists deal with data all the time and are often required to access, clean, and transform it. This is where data science gets involved in the field of computer science.

Computer Scientists in such scenarios leverage packages associated with data science, such as NumPy and even Pandas, in the case of the structured dataset, to make it usable.

To understand data quality and perform ETL processes, computer scientists look at the field of data science for effective solutions.

• Data Processing Cycle

Data involves itself at every step of computer science as it allows machines to communicate and be useful. In the field of computer science, data actively involves itself at every step of a ubiquitous data processing cycle., i.e.-

1.  Input : When data is input and is made ready for processing. For example, data is input into magnetic discs and tapes for later processing.
2.  Processing : Data is again used to transform it and make it more useful. Here the input data is processed to calculate various metrics, KPIs, etc.
3.  Output : The processed data results in other data, often in particular formats. These can include tables, graphs, summary statistics, etc.

• Types of Data

Interestingly, the data at every step mentioned above can be in different formats. In computer science and data science too, data can be found in the following three forms-

1. Structured Data: Tables, Matrices, Dataframes, etc.
2. Semi-structured Data: JSON, XML, etc.
3. Unstructured Data: Text, Audio, etc.

Therefore, data is at the heart of computer science and is available in various shapes and forms. Often, data science tools are required to deal with it, establishing the role of data science in computer science.

To conclude this article, let’s finally discuss the major similarities and differences between the fields of data science and computer science.

The differences between data science and computer science become more prominent when we discuss the required skills and responsibilities for professionals in these domains. When choosing between data science and computer science jobs, you should know which skills you’ll have to develop to pursue those roles.

Learning about the required skills will help you clarify doubts and determine which field best matches your interests. Both domains are distinct and focus on different aspects of technology, so the skills required are also different.

Data science professionals must have the following skills to succeed:

• Data extraction
• Data wrangling and exploration
• Data visualization
• Programming (SQL, R & Python)
• Statistics
• Machine learning
• Deep learning
• Big data frameworks

On the other hand, the necessary computer science skills are: 

• Knowledge of basic computer systems
• Data structures
• Technical writing
• Software development
• Mathematics
• Operating systems

As you can see, the difference is significant. Data science skills focus more on working with large datasets, while computer science skills focus more on working with computers and software applications.

To end the discussion, let’s understand the basic similarities and differences between the CS and data science fields by comparing the roles of data scientist and computer scientist.

Data Science Responsibilities:

The primary responsibilities in a data science role are:

Data mining

Data mining involves extracting and identifying patterns in large data sets using statistics, machine learning, and database systems. As a data science professional, you’ll have to collect data from multiple sources you choose according to their quality and reliability. You’ll also have to clean and optimize the data so it generates accurate results.

Analyzing data

One of the critical responsibilities of data science professionals is to analyze data to find insights. They use various machine learning algorithms and deep learning implementations to see trends and patterns in the data. Some of the most common machine learning algorithms they use are classification, decision trees, random forest, etc.

Sharing insights

As a data science professional, you’ll have to share the insights you gathered from the data. Data science professionals usually share their insights with stakeholders, such as business leaders and investors, which requires them to be adept at storytelling and data visualization.

Computer Science Responsibilities:

Following are some of the most common responsibilities in computer science roles:

Writing code

As a computer science expert, you must be familiar with multiple programming languages, such as Python, C#, C++, Java, and more. You’ll have to write code, test it, and debug any issues it may have.

Software development

Computer science professionals develop software applications that help users perform specific tasks. They develop different software components and combine them to create a fully functioning application.

Collaborating on projects

Computer science professionals work in teams in most cases, so they should have strong interpersonal and communication skills. They must also be familiar with different collaboration and project management software, such as Zoom, Todoist, and others.

Similarities

1. Requirement of Business Understanding: Both work in companies and are trying to solve business problems. Hence, they have to understand the business problems.
2. Knowledge of Data: Both deal with data and need to know data types and structures of data and databases etc.
3. GitHub Collaboration: As coding is done collaboratively in both job roles, knowing and using GitHub is common.
4. Programming Skills: Software engineers always have to know to code. And data scientists, as discussed, need to code to implement their theoretical concepts.
5. Both can start in each other’s role: Data Science requires coding, which is why software engineers can become data scientists. The opposite is also true because a data scientist may already have some coding exposure and can enter the field of computer science.

Differences

1. Academic Background: The most fundamental difference between a data scientist and a computer scientist stems from their educational backgrounds. The academic experience of a data scientist can vary greatly, whereas software engineers typically hold an engineering degree in IT, software engineering, etc.
2. Machine Learning vs. Software Design: The code data scientists write is for developing ML models. Software engineers do so to design, build and maintain software.
3. Business Decision Involvement: Data scientists are more involved in decision-making. Software engineers focus primarily on software development and enhancing a firm’s IT capability.
4. Skills: Data scientists focus more on business acumen, communication, reporting, and visualization skills. Software engineers concentrate more on software-based concepts.
5. Tools: The difference between data science and computer science can also be understood through the tools used in both fields. While both have some overlap, both use different tools. Data scientists using SQL, SAS, R, Python, Jupyter Notebook, Docker, and Kubernetes. At the same time, software engineers rely more on testing software, such as Visual Studio, GitHub, Atom, Slack, and Object-Oriented Programming languages – C, C++, Java, etc.
6. Approach: Computer scientists rely on agile, spiral, and waterfall approaches. Data scientists focus on algorithm implementation, pattern recognition, data visualization, etc.
7. Data Sources: For software engineers, user requirements and the need for new feature development form the basis of data sources. Data scientists can consider almost everything a data source, from social media to transactions to sensor and machine log data.

How to Choose Between Data Science vs. Computer Science for Your Career?

Choosing between data science and computer science is relatively easy if you understand both domains. Data science suits those who like to work with numbers and statistics.

Data science roles will require you to collect and analyze large quantities of data. Although it uses several aspects of computer science, data science depends primarily on statistics and mathematics.

Conversely, computer science and computer science jobs are more suitable for building things. Computer science roles are also ideal for studying human interactions and these technologies.

You should pick the domain that matches your career interests and aspirations.

You may also like to read: Is Data Scientist an IT Job? Learn About Different Roles & Skills

Conclusion

Given all the points covered on the issue of data science vs computer science in this article, it is evident that data science heavily relies on computer science. Computer science, in turn, also relies on data science to a limited degree as it also deals with data in its daily operations.

However, we must note that the article does not imply that computer scientists should or shouldn’t pursue a career in data science. One must understand both fields first in isolation, appreciate their interdependence, and finally make a decision.

FAQs: 

• Data science vs Computer science, which is better?

When it comes to data science vs computer science, there is no field better than the other, as they both have their role to play in an organization. As discussed in the article, both fields solve unique problems. Your decision on the discipline to pursue should be based on your interest, skills, and capabilities.

• What pays more, data science or Computer Science?

Given the substantial demand for data scientists and lack of supply, data science pays around 25,000 USD more.

• Can a computer scientist be a data scientist?

A computer scientist can become a data scientist like other professionals, such as statisticians or MBA graduates can become data scientists. Data science is an amalgamation of multiple fields, allowing several individuals to become data scientists.

Computer scientists have an edge over the technical aspects of data science. Still, they must learn about other aspects, such as statistics, machine learning algorithms, business understanding, reporting, visualization, storytelling, etc.

• Is a computer scientist the same as a data scientist?

Both job roles are different. However, some functionalities are related, such as the importance of data in computer science and the need to code in data science.

• Do data scientists code?

Yes, data scientist code to implement the theoretical understanding of statistics and machine learning. They need to write scripts to analyze data and develop models. The nature of coding, however, is different from what a software engineer does and is more data-driven.

We hope this article provides valuable insights into the distinctions and similarities of CS with data science. Please contact us if you have any questions or want to delve deeper into these subjects.