While the focus and primary goals of a data scientist and software engineer could be very similar, the methods and tools to achieve them are very different.
Both the data scientists and software engineers work to automate the processes to increase their efficiency and help businesses.
Data scientists will approach a problem more comprehensively — establishing a problem statement, querying the given data, exploratory data analysis, model building and development, and finally, interpreting the results.
On the other hand, software engineers focus mainly on developing functionalities, programs, and applications to help the end-users.
In this article, we will take a detailed look into the differences and similarities between a software engineer and a data scientist, simultaneously.
What is Data Science?
Data science can be described as extracting the data from various mediums and converting it into knowledge. Only after businesses have the required particular understanding of the data, can they make important decisions.
The last decade has seen a great rise in the importance of data science and the requirement of data scientists has increased extensively.
Businesses are in an excellent competitive environment, and only if they are making the right decisions at the right time they can keep up with the pace of competition.
And, this is not possible without the assistance of data scientists and data science.
What is Software Engineering?
Unlike data science, which is very vast and difficult to explain, software engineering is very self-explanatory. It involves developing new programs and software using data science and development skills.
However, the job of a software engineer does not end once the program is developed. We know that there is no such thing as bug-free software, the secondary role of software engineers is to patch existing software constantly and make sure that it is performing as per requirements.
Differences Between Data Scientist and Software Engineers
Data scientists are data-driven and well-rounded individuals, and they possess advanced technical skills to analyze the data.
With the given data, they can build complex quantitative algorithms to organize and synthesize extensive information and extract the information needed from it.
On the other hand, software engineers usually use the knowledge provided by data scientists to develop programs for end-users.
Without data scientists, it is tough for software engineers to operate, and without software engineers, the role of data scientists becomes obsolete.
But, before we move to elaborate on the differences between software engineers and data scientists, we would take a look into the differences between their respective fields — data science and software engineering Now that we have made a comparative analysis between data science and software engineering, we will take a look into the differences between software engineers and data scientists:
Qualifications and Skills Required for a Software Engineer vs. Data Scientist
As we have discussed earlier, the primary concentration of software engineers is on coding, while data scientists work on machine learning, statistics, data visualization, and thus creating order out of chaos.
Hence, the required skillset of these two fields varies, and so do the qualifications of relevant professionals.
In this section, we shall take a detailed look into the required skills and qualifications for software engineers and data scientists:
● Data Scientist
The required skills for a data scientist are as following:
- Domain knowledge
- Statistics and probability
- Data mining
- Machine learning language
- Quantitative analysis
- Programming knowledge
- Scientific and business knowledge
- Big data processing: including both unstructured and structured data (NoSQL DBS & SQL)
- Building data products and visualization of the data to make it understandable.
- Understanding and analyzing end-user requirements
- Core programming languages (like, c++, java, c, etc.)
- Data modeling skills
- Software testing
- Software configuration tools (puppet, chef, etc.)
- Build and release skills
- Project management skills
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