Computer Scientist vs Data Scientist

Updated June 14, 2023

Difference Between a Computer Scientist and Data Scientist

Computer Science is an approach to systematically studying algorithms, processing, communication, storage, etc. Computer scientists, thus, must be adept at analyzing and modeling problems. He is also expected to have a firm foundation in the crucial areas and in-depth knowledge in one or more areas of the discipline. It’s a science and technique of problem-solving.  Data scientists, on the other hand, are expected to know various scientific methods, algorithms, and processes to extract knowledge and information from data in various forms which can be either structured or unstructured. The concept is quite similar to data mining which uses meta keywords to extract the relevant information.

Let us study more about Computer Scientists and Data Scientists in detail

Computer Science deals with theory, experimentation that forms the basis for the design and use of computers. Computer scientists have a wide range of specialties, such as being proficient in understanding architectures, software systems, artificial intelligence, computational science, graphics, and software engineering.

As data science is known to be a concept of unifying statistics, machine learning, data analytics, and their related methods, data scientists are expected to derive and generate meaningful data from the already processed data to provide businesses with future insights, risk predictions, and ways of risk mitigation.

Head To Head Comparision Between Computer Scientists vs Data Scientists (Infographics)

Below is the Top 7 Comparision Between Computer Scientist vs Data Scientist

Key differences between Computer Scientist and Data Scientist

Following is the Difference Between Computer Scientist and Data Scientist are as follows

1. A computer scientist aims to simplify problems and classify them into smaller chunks. In contrast, a data scientist will tackle the problems from a business standpoint and dive deep into the data analytics lifecycle.
2. A computer scientist then uses a classification algorithm and improves it to meet the problem statement, which can be done by devising a new architecture or playing with regularization methods. In contrast, a data scientist uses techniques like cleaning datasets, normalizing, imputing missing, statistical testing, cross-validation, fitting models, etc.
3. A computer scientist applies the concepts of computation, computer design, and algorithms to a specific design problem. In contrast, a data scientist applies the ones emerging from machine learning, classification, uncertainty classification, cluster analysis, computational science, databases, data mining, data visualization, statistics, mathematics, information science and also computer science.

Computer Scientist vs Data Scientist Comparision Table

Basis of Comparison	Computer Scientist	Data Scientist
Primary Responsibility	They are known to shape the science of technology	They are known to discover the meaning of big data
Skillset	My Skillset includes Advanced computing, and in-depth experience in creating and building enterprise-scale applications, security solutions, database systems, and automated systems.	Expected to be knowledgeable in mathematics and computer science so that large data collections can be analyzed using data mining, predictive analysis, data visualization, and efficient data management.
What they do	Responsible for developing next-gen technology in computer software, cybersecurity, and smart systems	Expected to be SMEs (Subject Matter Experts) in one or more skills. The discipline will be used to clarify the relevance and usage of large data sets and therefore enhance organizational decision-making can be fostered.
Why are they important	They are the prime movers and drivers of today’s technological inventions	Data is one of the most crucial aspects of a company, and the sheer quantity of it requires experts to process and transform that raw data into meaningful information.
Potential Salary (approx.)	Ranges from $68,665 to $146,810 across the range of professionals	The median expected salary for big data professionals is $124,000 per annum
Applications	General Science Physics Chemistry Biology Anthropology Sociology Neuroscience Genetics Geology Robotics Health and Medicine Search Engine for the human body Fuzzy thinking can spot heart risk A look inside swine flu virus Handling epidemics in the virtual world Enzyme Design Speedup Heart surgery in 3D Training simulator environments X-rays spotting breast cancer Mapping infectious disease Virtual surgery Aiding chemists in superbug battle Environment Use of Wi-Fi mesh network to keep a watch on melting glaciers Robot fish to eat pollution Tornado simulators- Titanic Twisters Monitor endangered species count Data collected from volatile ice sheets Sociology Biology Self-directed robots making a discovery Genomes comparison with written text Automated cell screening system Reinventing molecular clues to the evolution Protein pattern in tissues automated Bats classifying plants from echo 3D models to optimize systems Astronomy Simulations for supernovae explosions Vivid 3-D to explore new ways 19 mirror technique to capture lights from an end edge of space Human assistance Speech is especially challenged, and the ones with cerebral palsy get voice. Brain-controlled wheelchair Smart homes and smart bathrooms Paralysed people walk in the virtual world Work with robot arm via thought Music Systems to better musicians’ performance with ideal performance Instant backing band creation for singers Art Bringing old painting warrior to virtual life Stress and Strain prediction Literature Government Exploration Cars Sports Linguistics	Deriving data from Internet Search Engines: Google Yahoo Ask Bing Duckduckgo AOL Digital Advertisements are targeted to a specific audience. Data scientists extract the audience. Advertisements include: Display banners Digital Boards Digital Ads Recommender Systems are used to: Promote products Post suggestions per user’s interest and relevance of the information Generate traffic Image Recognition Speech Recognition: Machine learning techniques Natural Language processing Gaming Price/Feature comparison websites Airline Route planning Predict flight delays Class of airplanes to be bought Decision-related to connecting and non-connecting flights Effectively run customer loyalty programs Fraud and Risk Detection Customer profiling Past expenditures Weird transactions Delivery logistics The best route to ship Best suited delivery time Best mode of transport Marketing Human Resources Finance Health Care Government Policies Self-driving cars Robots
Other potential careers	Computer Engineer Application Programmer Application Developer Database Architect Database developer IT Engineer Data Center Manager Network Administrator Mobile Specialist Network Architect Systems Architect Networks engineer Web developer Systems programmer	Business Systems Analyst Business intelligence Manager Clinical researcher Data Analyst Computational Biologist Database Developer Data Strategist Financial Analyst Health Informatics Analyst Predictive modeler Marketing analyst Research Analyst Statistician Risk Analyst

Conclusion

Both these Difference Between Computer Scientist and Data Scientist streams have their own set of roles and responsibilities to be taken care of, and they both aim to make the world a better place. If you want to pursue a career in any of these, now you know which one to choose.
Stay tuned to our blog for more articles.

Recommended Article

This has been a guide to Computer Scientist vs Data Scientist. Here we discuss head-to-head comparison, key differences, and comparison tables. You may also look at the following articles to learn more –

1. Computer Science vs Data Science
2. Data Science vs Data Visualization
3. Data Science Vs Data Mining
4. Data Science vs Web Development