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Neela
Student
Tampa, Florida
1
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What is the difference between baseline computer science and data science/analytics?
Can a business degree include data science? Also does it still count as a STEM field?
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4 answers
Leo Godin
Lead Data Engineer
1
Answer
Keene, New Hampshire
Updated
Leo’s Answer
TL;DR
Data science and analytics are subfields within computer science.
There are many business degrees with a data science or analytics focus.
I would consider a such a degree to be STEM related, others may disagree.
Computer science is the study of algorithms, data structures and computational processes. This is a software-focused field. While computer engineers design physical hardware, computer scientists design the methods to utilize that hardware. While we often think of this as programming, it is not. Programming is a method we use to implement the algorithms, data structures, and computing patterns. For instance, defining the steps required to implement a Netflix recommendation falls under computer science. Defining an efficient way to search for a single number within a list of 10,000 numbers also falls under computer science. Implementing these things in code falls more under software engineering. The two fields are closely related, but not the same thing. CS degrees often include both of these disciplines.
Data science and analytics are concerned with answering questions with data. Data scientists will develop and utilize algorithms for determining patterns in data. This is a broad field with many specializations. When we hear terms like deep learning, artificial intelligence, machine learning, we are generally hearing about data science. It is one of the many sub-fields within computer science. On any given day, a data scientists might create charts and graphs to make complex data easy to interpret. They may implement a machine-learning algorithm to predict failures in a power grid before they happen. They may even work on clinical drug trials to determine if a drug is likely to create a better outcome for patients.
To better understand data science, take a free course on Coursera
Data science and analytics are subfields within computer science.
There are many business degrees with a data science or analytics focus.
I would consider a such a degree to be STEM related, others may disagree.
Computer science is the study of algorithms, data structures and computational processes. This is a software-focused field. While computer engineers design physical hardware, computer scientists design the methods to utilize that hardware. While we often think of this as programming, it is not. Programming is a method we use to implement the algorithms, data structures, and computing patterns. For instance, defining the steps required to implement a Netflix recommendation falls under computer science. Defining an efficient way to search for a single number within a list of 10,000 numbers also falls under computer science. Implementing these things in code falls more under software engineering. The two fields are closely related, but not the same thing. CS degrees often include both of these disciplines.
Data science and analytics are concerned with answering questions with data. Data scientists will develop and utilize algorithms for determining patterns in data. This is a broad field with many specializations. When we hear terms like deep learning, artificial intelligence, machine learning, we are generally hearing about data science. It is one of the many sub-fields within computer science. On any given day, a data scientists might create charts and graphs to make complex data easy to interpret. They may implement a machine-learning algorithm to predict failures in a power grid before they happen. They may even work on clinical drug trials to determine if a drug is likely to create a better outcome for patients.
Leo recommends the following next steps:
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
6342
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Dear Neela,
Exploring the Contrast Between Core Computer Science and Data Science/Analytics
Core Computer Science:
Focus: Core computer science is primarily concerned with the exploration of algorithms, programming languages, software creation, computer structure, and the theoretical underpinnings of computing.
Applications: It predominantly revolves around creating software applications, designing algorithms, comprehending computational theory, and interacting with hardware systems.
Skills: Core computer science stresses the importance of abilities such as coding, algorithm creation, software development, system analysis, and understanding of computer architecture.
Data Science/Analytics:
Focus: Data science/analytics is about deriving insights from data through various methods, including statistical analysis, machine learning, data mining, and visualization.
Applications: It finds use in diverse areas such as business intelligence, healthcare analytics, financial modeling, marketing analytics, and predictive modeling.
Skills: Data science/analytics necessitates proficiency in statistics, data visualization, machine learning algorithms, and data cleaning and preprocessing techniques.
Contrasting the Two: The primary distinction between core computer science and data science/analytics is their focal points. While core computer science grapples with the basic principles of computing and software creation, data science/analytics is dedicated to extracting significant insights from large data sets using statistical and machine learning methods.
Is Data Science a Component of a Business Degree?
Indeed, a business degree can encompass courses related to data science. Numerous universities offer specialized programs such as a Master’s in Business Analytics or a focus in Data Science within their business degrees. These programs aim to provide students with the necessary blend of business savvy and analytical skills for roles that involve using data to make strategic decisions.
Categorization within the STEM Field:
Data science is recognized as a segment of the STEM (Science, Technology, Engineering, Mathematics) field due to its substantial dependence on mathematical and statistical principles. Since it applies scientific methods to analyze data and resolve complex issues using technological tools like programming languages and machine learning algorithms, it is included in the broader STEM category.
Top 3 Reliable Sources Used to Respond to this Question:
Harvard Business Review: The Harvard Business Review offers perspectives on the convergence of business management and data analytics.
IEEE Xplore: IEEE Xplore is a digital library that provides access to high-quality technical literature in engineering and technology disciplines.
Forbes: Forbes addresses a broad spectrum of topics, including business trends related to data science and analytics across various sectors.
May God Bless You,
James.
Exploring the Contrast Between Core Computer Science and Data Science/Analytics
Core Computer Science:
Focus: Core computer science is primarily concerned with the exploration of algorithms, programming languages, software creation, computer structure, and the theoretical underpinnings of computing.
Applications: It predominantly revolves around creating software applications, designing algorithms, comprehending computational theory, and interacting with hardware systems.
Skills: Core computer science stresses the importance of abilities such as coding, algorithm creation, software development, system analysis, and understanding of computer architecture.
Data Science/Analytics:
Focus: Data science/analytics is about deriving insights from data through various methods, including statistical analysis, machine learning, data mining, and visualization.
Applications: It finds use in diverse areas such as business intelligence, healthcare analytics, financial modeling, marketing analytics, and predictive modeling.
Skills: Data science/analytics necessitates proficiency in statistics, data visualization, machine learning algorithms, and data cleaning and preprocessing techniques.
Contrasting the Two: The primary distinction between core computer science and data science/analytics is their focal points. While core computer science grapples with the basic principles of computing and software creation, data science/analytics is dedicated to extracting significant insights from large data sets using statistical and machine learning methods.
Is Data Science a Component of a Business Degree?
Indeed, a business degree can encompass courses related to data science. Numerous universities offer specialized programs such as a Master’s in Business Analytics or a focus in Data Science within their business degrees. These programs aim to provide students with the necessary blend of business savvy and analytical skills for roles that involve using data to make strategic decisions.
Categorization within the STEM Field:
Data science is recognized as a segment of the STEM (Science, Technology, Engineering, Mathematics) field due to its substantial dependence on mathematical and statistical principles. Since it applies scientific methods to analyze data and resolve complex issues using technological tools like programming languages and machine learning algorithms, it is included in the broader STEM category.
Top 3 Reliable Sources Used to Respond to this Question:
Harvard Business Review: The Harvard Business Review offers perspectives on the convergence of business management and data analytics.
IEEE Xplore: IEEE Xplore is a digital library that provides access to high-quality technical literature in engineering and technology disciplines.
Forbes: Forbes addresses a broad spectrum of topics, including business trends related to data science and analytics across various sectors.
May God Bless You,
James.
Nicole Baillie
Data Analyst
483
Answers
Atlanta, Georgia
Updated
Nicole’s Answer
Hi Neela. Terrific question!
In addition to the awesome answers already provided, I will add that for sure having a computer science background is a very helpful tool if one is interested in data science /analytics.
In my experience, the practice of using data to solve problems can expose you to building a good and sustainable relationship with your clients/partners/customers. The added benefit of having a computer science and/or programming background is the flexibility it can bring to the problem you are trying to solve. Oftentimes, that flexibility means you can move faster by implementing changes faster. Change...and being comfortable with change, is a large part of working in data and data analytics. Data changes all the time and so do the problems that need to get solved. If you are lucky, you get to solve one problem and then have the know-how to move on to solving another.
For sure data science can be and is big and splashy..AI and machine learning are very significant parts of data analytics. For people who are interested in the data analytics field, but are just starting their journey, there are numerous ways to learn and engage in data and trends that can be of benefit. I encourage you to consider learning a few programming languages as they can be very helpful building blocks whether your focus in business (fintech is a business focus where there is lots of data science/analytics) or another career path.
Hope you find this advice helpful. Best of luck to you!
In addition to the awesome answers already provided, I will add that for sure having a computer science background is a very helpful tool if one is interested in data science /analytics.
In my experience, the practice of using data to solve problems can expose you to building a good and sustainable relationship with your clients/partners/customers. The added benefit of having a computer science and/or programming background is the flexibility it can bring to the problem you are trying to solve. Oftentimes, that flexibility means you can move faster by implementing changes faster. Change...and being comfortable with change, is a large part of working in data and data analytics. Data changes all the time and so do the problems that need to get solved. If you are lucky, you get to solve one problem and then have the know-how to move on to solving another.
For sure data science can be and is big and splashy..AI and machine learning are very significant parts of data analytics. For people who are interested in the data analytics field, but are just starting their journey, there are numerous ways to learn and engage in data and trends that can be of benefit. I encourage you to consider learning a few programming languages as they can be very helpful building blocks whether your focus in business (fintech is a business focus where there is lots of data science/analytics) or another career path.
Hope you find this advice helpful. Best of luck to you!
Vamsi Ambati
Engineer
2
Answers
Fremont, California
Updated
Vamsi’s Answer
Computer Science delves into the wonderful world of a computer's inner workings and related systems, offering an exciting opportunity for those fascinated by technology. Data science, on the other hand, focuses on harnessing the power of data to craft meaningful stories that can drive impactful insights for companies. Business executives can then capitalize on these findings to make well-informed decisions.
When exploring the business side, it's important to strengthen your skills in statistics, as this will greatly aid in data analysis. With a solid foundation in Computer Science, you'll be well-equipped to collect data and implement machine learning models that can transform raw data into valuable information.
So, go ahead and embrace the exciting new challenges that lie ahead in the intersection of Computer Science, Data Science, and Business! The synergy of these fields can lead to endless possibilities and fulfilling achievements. Together, let's build a brighter future by turning data into actionable insights and informed decisions!
When exploring the business side, it's important to strengthen your skills in statistics, as this will greatly aid in data analysis. With a solid foundation in Computer Science, you'll be well-equipped to collect data and implement machine learning models that can transform raw data into valuable information.
So, go ahead and embrace the exciting new challenges that lie ahead in the intersection of Computer Science, Data Science, and Business! The synergy of these fields can lead to endless possibilities and fulfilling achievements. Together, let's build a brighter future by turning data into actionable insights and informed decisions!