In this blog, we will go through the following topics –
Watch this Data Science Course video to learn more about its basic concepts:
So let’s go ahead.
Growth of Data and the Need for 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 inferences from raw data and solve complex problems.
A research by Sciencedaily.com, from the year 2019 revealed that 90% of the total data present in the world was created in the 2 years before 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 2023 was have been a whopping 45 zettabytes. To make all of this information of value and use to us, and to apply it to the real world and practical scenarios, we need data science.
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 to their customers. Data Science deals with structured as well as unstructured data. It involves studying historical data.
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.
Exploring the Basics of 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, hardware, 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, and innovation across industries, and teaches how to utilize computers to create, not just to consume.
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
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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
The list of tools and programming languages in Computer Science is endless. So the skill set depends on the job role that you want to go for.
Career: Data Science vs Computer Science
Data Science Careers
Data science has evolved into the most promising career option for skilled professionals. Data Science has already been declared 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 Architect
- Data Engineer
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 make it a good field to gain expertise and pursue a career.
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 between Data Science and Computer Science.
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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 2023
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 in 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 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
Check out this blog to learn about Top Data Engineering Projects for Beginners!
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 Modeling 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!
Here is a general overview of the salary trends for Data Science and Computer Science in the United States, India, the United Kingdom, and Australia:
In the US, both Data Science and Computer Science professionals command competitive salaries. According to salary data from sources like Glassdoor and PayScale, the average annual salary for Data Scientists in the US ranges from $100,000 to $150,000, depending on factors such as experience and location. Computer Science professionals, such as software developers or computer systems analysts, can earn average salaries ranging from $70,000 to $120,000 per year.
In India, the salary ranges for Data Science and Computer Science professionals are generally lower compared to the US. According to data from sources like Naukri and Glassdoor, the average annual salary for Data Scientists in India can vary between INR 6 lakhs to 20 lakhs. Computer Science professionals, such as software engineers or developers, may earn average salaries ranging from INR 4 lakhs to 15 lakhs per year.
In the UK, both Data Science and Computer Science roles tend to have competitive salaries. According to salary data from sources like Indeed and PayScale, the average annual salary for Data Scientists in the UK ranges from £40,000 to £70,000. Computer Science professionals, such as software engineers or IT consultants, can earn average salaries ranging from £30,000 to £60,000 per year.
In Australia, Data Science and Computer Science professionals also enjoy competitive salaries. According to salary data from sources like Seek and PayScale, the average annual salary for Data Scientists in Australia ranges from AUD 80,000 to AUD 130,000. Computer Science professionals, such as software developers or IT managers, can earn average salaries ranging from AUD 70,000 to AUD 120,000 per year.
To crack interviews, go through these Data Analyst Interview Questions and Answers.
MS in Data Science vs MS in Computer Science
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 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.
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.
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Data Science vs Computer Science Which is better
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.
On the other hand, 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 that 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.
The choice between Data Science and Computer Science depends on your interests and career goals. If you are passionate about programming, software development, algorithms, and building computer systems, Computer Science might be a better fit for you. On the other hand, if you have a strong inclination towards working with data, analyzing trends, and deriving insights, Data Science could be a more suitable path.
If you have any more questions, post them in our Data Science Community!