In this blog, we will go through the following topics –
So let’s go ahead.
What is Computer Science?
Computer Science, in simple terms, can be defined as the study of computers and computing concepts. It includes computer design, computer architecture and their applications in technology and science.
Computer science deals with both software and hardware both, and other components like the internet and networking, with a large number of research areas to study further in.
The hardware part deals with the study of computer design and its working processes whereas the software part deals with programming concepts and languages. Computer science also comprises Compilers and Operating Systems.
It is the science that enables you to program a computer to do whatever you want. This discipline focuses on problem solving, innovation across industries, and teaches how to utilize computers to create, not just to consume.
What is Data Science?
Data science is a field that blends the multiple disciplines of machine learning, algorithms, data inference, programming, mathematics, and statistics to extract useful inference from raw data and solve complex problems.
A research by Sciencedaily.com, from the year 2013 revealed that 90% of the total data present in the world was created in the 2 years prior to that. Imagine that. In a matter of two years, we accumulated 9 times the total data collected over thousands of years of humankind combined.
An estimate showed that the amount of data by the end of 2020 was have been a whopping 45 zettabytes. To make all of this information of value and of use to us, and to apply it to the real world and practical scenarios, we need data science.
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Using Data Science, information and knowledge are extracted from data, to make decisions and predictions, using machine learning, prescriptive analytics, and predictive causal analysis.
It is the field that helps businesses, not just in recognizing their markets and improving their decision-making, but also in getting them closer with their customers. Data Science deals with structured as well as unstructured data. It involves studying historical data.
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As data-driven decision-making is becoming more and more popular with time, each company, be it big or small, seeks professionals who can analyze and understand raw data, and help the company utilize it efficiently.
Skills: Data Science vs Computer Science
Data Science Skills
The skills needed for Data Science are –
- Technical Skills
- SQL Databases
- Apache Spark and Hadoop
- Python Programming and R Programming
- Machine Learning and Artificial Intelligence
- Mathematics and Statistics
- Data Visualization using Tableau and Excel
- Non-Technical Skills
- Communication Skills
- Business Strategy
- Intellectual Curiosity
Computer Science Skills
When you complete a degree in Computer Science, you learn about algorithms, software development, computer architecture, operating systems, security, networking, skills like programming and everything needed to operate a computer. You learn how to use technologies to solve problems.
Some skills and knowledge needed to pursue a career in Computer Science are –
- Technical knowledge about computers
- Programming skills
- Software Development
- Requirement Analysis
- Software Testing
- Slack and GitHub
- Atom and Visual Studio
- Microsoft Azure
- Software Integration
- Data Analysis
- Operations and Maintenance
- Attention to detail and Problem-solving Skills
- Creativity and Communication Skills
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The list of tools and programming languages in Computer Science is endless. So the skill set really depends on the job role that you want to go for.
Career: Data Science vs Computer Science
Data Science Careers
Data science has evolved as the most promising career option for skilled professionals. Data Science has already been declared as The Hottest Profession of the 21st Century. The different jobs available in the Data Science industry are –
- Data Science
- Data Analyst
- Business Intelligence Analyst
- Data Mining Engineer
- Data Architect
- Applications Architects
- Data Engineers
Read our blog Data Science Tutorial to learn more about the careers in Data Science
Data Scientists are like superheroes who make sense of unstructured, messy, raw data from sources that don’t fit into databases neatly, such as emails, social media feeds, and smart devices. They collect, cleanse and organize data.
Data Science career is in high demand and Data Scientists are very well paid. Such a high demand and the extreme lack of skilled professionals makes it a good field to gain expertise and to pursue a career in.
Computer Science Careers
Computer Scientist is an umbrella term for all professionals who work in the field of computer science. They work on the nuts and bolts of the computational process. There are multiple job roles in the industry, like –
- Software Engineer
- Hardware Engineer
- Software Tester
- Systems Analyst
- Business analyst
- Product manager
- Network architect
- Cloud computing engineer
- Web Developer
- Database Administrator
- Network Architect
- Mobile application designer or developer
- Research and development (R&D) scientist
- Computer scientist or computer science researcher
- Artificial intelligence and machine learning engineer
Now that we have an understanding of both these fields and the skills to make a career in them, see the similarities and differences in Data Science vs Computer Science.
Similarities: Data Science vs Computer Science
The similarities between Computer Science and Data Science are –
- Both roles expect you to be a leader in Technology.
- Both have overlapping skills.
- Each of them uses computational processes.
- Both need you to understand business and products.
- Both roles require some understanding and knowledge of data.
- Both need you to be fluent with GitHub.
- Both Data Science and Computer Science are cross-functional.
- Both require proficiency in programming.
- Both follow a systematic and scientific approach.
- Both professionals can switch roles from one to the other.
Read our blog on Computer Programming Courses in 2021
Differences: Data Science vs Computer Science
The similarities between Computer Science and Data Science are –
- Computer Science puts more emphasis on Software Design.
- Data Science puts more emphasis on Machine Learning algorithms and Artificial Intelligence.
- Education for Data Science is different from Computer Science and requires more specific training, course, or expertise.
- Computer Scientists study Computer Engineering.
- Computer Scientist is a role with more variety.
- Data Scientists should have a knowledge and background in statistics.
- Computer Science is more automation and object-oriented.
- Data Scientists work along with other business-facing roles comparatively more.
- Data Scientists usually study Computer Science first and then specialize in Machine Learning and other related fields.
- While Computer Science can include Data Science, its main theme is Software Engineering
Comparison: Data Science vs Computer Science
Let’s compare them against different parameters and explore their fundamental differences.
|Parameter||Computer Science||Data Science|
|Definition||Study of Computers and technologies related to it.||Study of Data and extracting information from it.|
|Usage||Advancement and Technological Growth. To use computers efficiently.||Managing Data and utilizing it efficiently.|
|Benefit||Performance and Speed||Reducing Redundancy of Data and extracting insights from it.|
|Which one is older?||Computer Science is the older one, dating back to almost 2 hundred years ago. ||Out of the two fields, Data Science is comparatively newer.|
|Education||A bachelor’s degree in technology or engineering in Computer Science||A degree in CS, Engineering, IT, Statistics and Mathematics, or any related field|
|Skills and Knowledge||Programming, Algorithms, Networking, Computer Architecture, Operating System||Statistics, Mathematics, Machine Learning, Artificial Intelligence|
|Curriculum||Interaction between hardware and software is an important part of the curriculum, along with algorithmic training||Hardware training is not important for data science, whereas algorithmic training is included.|
|Specializations||Artificial Intelligence, Network Security, Biocomputation, Human-Computer Interaction, Mobile Computing, Information Systems||Healthcare, Bioinformatics, Business Analysis, Statistical Modelling, Genomic Data Sequence, Robotics|
|Job Role||Computer Scientists work with algorithmic processes ||Data Science works with Data to extract insights|
|Industry ||This is the basic science for the Computer Science Industry||Data Science is a specialization in the Computer Science industry|
|Applications||Applies to all technical, small, and large-scale companies||Applies to companies who have large amounts of data|
|Field||Covers the entire technology field. This is the superset.||It is a subset of the Computer Science field|
|Real-time usage||Usage of ultra-fast and advanced devices like supercomputers||Handling and maintaining a large amount of customer data|
Understand the differences between Data Science, Deep Learning, Artificial Intelligence, and Machine Learning in this comparison blog on Data Science vs ML vs AI vs Deep Learning!
MS in Data Science vs MS in Computer Science
To start your career as a Computer Scientist, you will first need to complete a Bachelor’s degree in Computer Science, preferably a degree in Engineering or Technology. Getting a master’s degree will give you an advantage in this competitive field.
Pursuing an MS in Computer Science will help expose you to various fields in the industry. To join the program you need a strong knowledge of science, math, analytical skills, and logical reasoning. All the things that you learn in this program, will have many applications in the IT field.
Coming to the curriculum, while pursuing MS in Computer Science, you will get to explore areas like operations, databases, software design, networks, and systems. In the theoretical part, you will gain further knowledge of logic, algorithms, performance modeling, complexity, programming, etc.
Undergraduate programs focusing on knowledge of Mathematics, Data Analysis and Statistics are good options to give you a solid foundation for an MS in Data Science. An aspiring Data Scientist will need to choose to specialize in the Data Science niche.
Most Data scientists are highly educated. According to KDnuggets, amongst every 100 data scientists, 88 of them have at least a Master’s degree, and 46 of them have PhDs. A strong educational background is very essential to gain the deep knowledge required to be a successful Data Scientist, and an MS in Data Science will give you that background.
Getting an MS in Data Science will give you an upper edge in a Data Science interview and leave the impression that you have a good understanding of the field.
To crack interviews, go through these Data Analyst Interview Questions and Answers.
While choosing between getting MS in Data Science vs MS in Computer Science seems like a difficult choice, it can be easier if you decide what career you want to pursue. Pick Data Science if you want to become a Data Scientist, and pick Computer Science if you want to keep your options open.
Data Science vs Computer Science Which is better
Computer Science is a field incorporating all computational processes. It is the superset of many related fields. The work on developing applications and software, testing those software, network security, and system architecture. This discipline is the nuts and bolts of building the technological infrastructure of the modern world in the 21st century.
Data Science is comparatively a newer field, a subset of computer science. It can also be looked at as the child of Computer Science and Statistics. Data Science helps with studying and analyzing data to yield meaningful and actionable insights.
Both fields require problem-solving and analytical skills. Paying attention to detail, have good communication skills, and understanding businesses are also very important.
Since both these fields can go hand in hand, there is no definite answer to Data Science vs Computer Science which is better. Both disciplines are in-demand, exciting, and very important in shaping the modern world.
Which one of the two you pick should depend on your own skill sets, interests, and choice of career. If you like to build things and innovate, computer science might be the better choice for you, whereas, if you’re good with numbers and like to analyse, it might be a better idea to pick Data Science.
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