What Are The Key Differences Between Computer Science And Data Science?
What Are The Key Differences Between Computer Science And Data Science?
Technological advancements have been disruptive in the last decade with the emergence of Artificial Intelligence (AI), robots, and blockchain technology. The modern techy world breeds data and information which has led to widespread opportunities in the fields of Data Science and Computer Science.
In this blog, we will dive into the differences between Computer Science and Data Science, their roles in the digital era, and the process through which students can make a career in these fields. So, let us dive in!
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Table Of Contents
- What Is Computer Science?
- What Is Data Science?
- Difference Between Data Science And Computer Science
- Roles Of Data Science And Computer Science In The Digital Era
- How To Make A Career In Computer Science And Data Science?
- The Final Say
What Is Computer Science?
Computer science is the study of computers, data structures, and computational systems. It deals closely with programming languages, algorithms, and development of softwares. Nowadays, with the increasing popularity of ChatGPT and AI, more and more people are drawn toward coding and learning about computational science.
Computer science is closely linked to mathematics and engineering and involves the study of topics like calculus, binary systems, statistics, and numerical analysis. There are four other disciplines similar to Computer science:
Computer scientists, engineers, and technicians have revolutionised today’s world by utilising their knowledge of computers and harnessing the power of technology.
What Is Data Science?
Data science is an emerging field in the modern world that offers highly-paid jobs at some of the biggest organisations in the world. It is the study of data to perform analysis to help businesses scale. It involves the study of mathematics, AI, and statistics. Data scientists analyse the data of companies and market trends and accordingly, devise strategies to help companies increase their profits.
Data science is used to perform mainly three different kinds of analysis:
1. Descriptive Analysis
The Descriptive analysis gives information about the trends in data by using graphical representations like pie charts, bar graphs, histograms, etc. It identifies the low-performing and high-performing months and helps to gain insights into the business.
2. Predictive Analysis
Predictive analysis analyses data from the past to make predictions about the possible future outcomes. This allows businesses to identify their past mistakes, correct them, and prevent them from happening again in the future.
3. Prescriptive Analysis
This type of analysis in data science not only predicts the future but also talks about possible situations that may arise if a particular methodology is followed. Prescriptive analysis uses advanced techniques like graph analysis, simulation, and network systems to analyze the data.
Difference Between Data Science And Computer Science
Computer science and data science are interconnected but very different from each other. Both deal with data, but the way they use it is different. The key differences between computer science and data science are:
1. Computer science is the study of computers and computational systems, while data science is the statistical study of data that involves performing different types of analysis for businesses to grow.
2. Computer science focuses on harnessing technology and developing improvised computational designs, whereas data science is the science of using these technologies for information analysis and interpretation.
3. The people who have a degree in computer science or similar disciplines are called computer scientists while the ones who have a degree in data science are called data scientists.
4. Computer science performs operations on data (information), while data Science analyses this information and interprets the trends using various statistical techniques.
5. Computer science is closely related to Information Technology (IT) and computer engineering, while data science is linked to data analytics and machine learning.
Roles Of Data Science And Computer Science In The Digital Era
The advancement in computer science and data science has caused significant changes in the modern world, such that it has almost become unrecognisable in the last decade. Some complementary roles of data science and computer science in the digital era are:
1. Data Processing
Data processing involves interpreting and analysing information using Structured Query Language (SQL). SQL is a coder-friendly programming language that makes the process of analysing data easier. All data scientists and computer scientists are required to learn SQL.
Visualisation is the way in which the data is presented. It is an important element of data science as well as computer science. In data science, trends are identified in data by making data visualisations using graphical and statistical representations.
3. Machine Learning
Machine learning is a subset of AI and computer science and involves using data, and algorithms to make accurate predictions. It allows computers to learn from the past.
4. Collection Of Big data
Both computer science and data science use ‘Big Data’. Big data is the term used for large amounts of data that conventional software can’t understand. This Big data is collected by computers and is used in making decisions and performing analysis in the field of data science.
5. Performing Analysis
Data science and computer science play complementary roles in analysing data by using different techniques. Computers collect data which is used by data scientists to perform analytics and the outcomes of these analytics can be further used by computer scientists to work on the advancement of computers.
Thus, data science and computer science are different from each other but ‘data’ is the missing link between them.
How To Make A Career In Computer Science And Data Science?
Interested in computer science or data science? Well, you can build a high-paying career in these fields by becoming a computer scientist, data scientist, or computer science engineer. Follow these steps to build a career in computer science or data science:
Pursue a Bachelor’s degree in computer science/data science or an engineering programme from a reputed university with excellent faculty. Check out Bansal Group of Institutes and enroll in our Computer Science Engineering programme!
Earn a Master’s degree in the same discipline to gain more expertise and become more adept at your discipline.
Attain hands-on experience in data science/computer science by doing internships or joining organisations/startups that work in your niche (specialisation).
The Final Say
Computer science and data science are topics of conversation in today’s digital world. Computer science is the study of computational designs and systems, while data science deals with data analytics and data mining. They perform complementary roles in the modern era but are different from each other.
1. What is Computer Science?
Computer Science is the study of computers, data structures, and computational systems. It deals closely with programming languages, algorithms, and software development . It involves the study of topics like calculus, binary system, statistics, and numerical analysis.
2. What is Data Science?
Data Science is the study of data to perform analysis to help businesses scale. It involves the study of mathematics, AI, and statistics. Data scientists analyse the data of companies and market trends and, accordingly, devise strategies to help companies increase their profits.
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