Data science and computer science often go hand in hand, but what makes them different? What do they have in common? After working in several different positions in the data science departments of various companies, I discovered some general qualities common to the data science process, as well as how IT is integrated into this process as well. Anyone currently working in either field or looking to enter either field should note the differences between these two disciplines, as well as when one requires concepts and principles of the other.
Usually, a data scientist will benefit first from learning computer science and then specializing in machine learning algorithms. Some data scientists, however, start by jumping straight into statistics before learning to code, focusing on the theory behind data science and machine learning algorithms. That was my approach, and I learned computer science and programming afterwards.
That said, does a data scientist really need to understand computer science? The short answer is yes. Although computer science encompasses data science and is particularly essential to artificial intelligence, I believe that the main component of computer science is software engineering. Here, I will highlight the differences between these two disciplines and their practice, as well as their respective similarities. I’ll also dig deeper into the focus of each area, including common tools, skills, languages, steps, and concepts.
What does a Data Scientist do?
So what does a data scientist actually do? We often hear the buzzwords in the tech industry, but are they really the keywords we use in our daily work? The answer is both yes and no.
Without a doubt, I use many major tools and languages at least on a daily basis. As a data scientist, I have to explore company data while determining how that data affects a product. Ultimately, any data scientist will be encouraged to study current data, find new data, and solve business and product problems, all using machine learning algorithms (e.g., the random forest). While computer scientists can solve some of these same problems, for the sake of the “data scientist” title, the role requires someone who is solely focused on machine learning algorithms as a method of making an otherwise manual process non- only more efficient but also more precise.
Here are some of the steps in the data science process that a data scientist can expect to use.
What does a Data Scientist do?
- Explores current data and finds new data.
- Uses SQL to query and understand business data.
- Uses Python or R to explore data in a dataframe or something similar.
- Performs exploratory data analysis using libraries such as pandas_profiling.
- Isolates the business issue and the possible impact a model would need to have to be successful.
- Finds and runs basic machine learning algorithms against the null or current process.
- Optimizes the final or set of algorithms for the best results.
- Displays results with some type of visualization (e.g. Seaborn, Table)
- Work alongside a computer scientist, perhaps, or an MLOps engineer.
- Deployment and prediction with your final model in the enterprise ecosystem.
- Summarizes the improvements.
As you can see, this process can sometimes be shared with others like AI Engineers, Data Engineers, Computer Scientists, MLOps Engineers, Software Engineers, etc. What makes the role of the data scientist unique is its focus on machine learning theory and its effect on a business problem.
And here are some of the tools a data scientist can expect to use.
What tools does a data scientist use?
- R, SAS
- Jupyter notebook
- Air flow
Although the data science process is quite set in stone, much like the scientific method, the tools a data scientist uses are open to negotiation. That being said, I would say that most data scientists primarily use SQL, Python, and a Jupyter Notebook or something similar, as these tools or languages can be applied to any business. Some companies, however, will have certain preferences or requirements that require the use of Google Data Studio on Tableau, for example.
What does a computer scientist do?
Although the field of computer science is more widespread and varied than the specific job title of computer scientist, there are certain roles that carry this name. Despite this, computer science jobs tend to specifically involve software engineering. Other tasks that might fall under the umbrella of IT include, but are not limited to, database administration, hardware engineering, systems analysis, network architecture, web development and a plethora of IT roles.
This variety makes a computer scientist role a bit more difficult to precisely define, which is similar to data science’s inclusion of machine learning operations, data engineering, data analytics , etc Ultimately, you and the company you work for will need to define your role in IT. Looking at a job description, of course, is an easy way to find out what a specific sub-role looks like.
Here are some of the steps in the IT process that an IT professional can expect to go through.
What does a computer scientist do?
- Includes company, data, products and, of course, software.
- For a specific problem, define the requirements.
- Understands and designs the system and software.
- Implements the process and performs unit tests.
- Understands how the software will be integrated and how it affects the system.
- Oversees operations and maintenance.
While this process doesn’t exactly resemble that of a data scientist, it still shares some of the broader aspects of a more technical process, including but not limited to understanding software, data and implementing an improvement, then analyzing and reporting on its effect.
And here are some of the tools and languages that a computer scientist can expect to use.
What tools does a computer scientist use?
- Testing software
- Python and other object-oriented programming languages
- Microsoft Azure
A computer scientist can expect to use a wide range of tools and languages. Again, the toolkit depends on your area of interest: is it software engineering, is it network analysis, is it computer science? I hope you can find a role for you that not only matches your skillset, but also one that you prefer to do.
Data science versus computer science: similarities and differences
Now that we’ve discussed the main qualities and expectations of these two roles, let’s explore both the similarities and differences between them. Of course, there are more points to discuss, but these are some of the main ones based on my experience.
Here are the similarities you can expect between the two roles.
Data science versus computer science: similarities
- Both require an understanding of the business domain and its products.
- Both require a working knowledge of business data.
- Both roles typically require proficiency in using Git or GitHub.
- Both follow a systematic approach to scientific processes.
- Both are expected to be technology leaders.
- Both usually require proficiency in at least one programming language.
- Both can start in one role and move on to the other.
- Both are cross-functional.
And here are the differences you can expect between the two roles.
Data science versus computer science: differences
- Data scientists focus on machine learning algorithms, while computer scientists focus on software design.
- IT encompasses more information and roles offer more variety.
- The training needed is different for everyone, usually reflected in the differences between a computer science degree and a data science degree.
- Data scientists usually have a background in statistics, while computer scientists have a background in computer engineering.
- Computer scientists are generally more automation and object-oriented.
- Data scientists often work most closely with product managers or other business-facing roles.
Since these roles both include other sub-roles, they may differ significantly from each other in one company, but be surprisingly similar in another.
Data Science vs Computer Science: An Overview
As you can see, these positions require different skills, tools and languages; however, they also share some of these same qualities. The primary focus of a data scientist is to solve business problems using machine learning algorithms while the primary job of a computer scientist is either directing object-oriented programming and software engineering , or computer management, which requires a general working knowledge of all things computer. – bound in a company.