Why is Software Engineering important in Data Science?November 27, 2021 2021-12-02 18:11
Why is Software Engineering important in Data Science?
Why is Software Engineering important in Data Science?
It is very difficult to quantify how much impact does data science and software engineering has on our lives. Most of us can hardly remember the dark age of just few years ago , where you couldn’t ask Siri for the directions to the nearest restaurant. If you do remember that time, you probably wouldn’t wanna go back.
Today you don’t have to drive around in your car just to find a restaurant to have a nice meal, instead you can ask your mobile assistant (developed by software engineer), which will trigger an algorithm (developed by data scientist) and will show you the location of nearest restaurant on you phone map application (developed by software engineer).
And this is only regarding our personal lives. These technologies have made a much bigger impact on industries. Using software and big data, businesses are able to make data-informed decisions. This means being better able to identify an audience, anticipate their needs, give them what they want, and make bigger profits.
What is data science?
Data science is hard to define exactly, but you could think of it as “the use of algorithms and statistics to draw insights from structured and unstructured data”. The goal of a data scientist is going to depend quite a lot on the problem they’re examining. From organizations trying to meddle with petabytes of data, a data scientist’s role was to help them utilize this opportunity to find insights from this data pool. They will use their computer science, statistics, and mathematical skills to analyze, process, interpret and store data.
In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
What is software engineering?
Software engineering has two parts: software and engineering. Software is a collection of codes, documents, and triggers that does a specific job and fills a specific requirement. Engineering is the development of products using best practices, principles, and methods. So, software engineering is defined as a process of analyzing user requirements and then designing, building, and testing software application which will satisfy those requirements.
In software development, the goal is to create new programs, applications, systems, and even video games. Because there’s no such thing as bug-free software, an inescapable secondary goal for software engineers is to constantly patch and iterate on existing software to make it better and ensure it performs as required. Behind every software products there are so many stages involved, which all are done by software engineers.
Data Science V/S Software Enginnering
Both Data Science and Software Engineering domains involve programming skills. Where Data Science is concerned with gathering and analyzing data and Software Engineering focuses on developing applications, features, and functionality for the end-users.
|Data Science||Software Engineering|
|Data Science focuses on gathering and processing data.||Software Engineering focuses on the development of applications and features for users.|
|Includes machine learning and statistics.||Focuses more on coding languages.|
|Deals with Data Visualisation tools, Data Analytics tools, and Database Tools.||Software Engineering deals with programming instruments, database services plan instruments, CMS devices, testing devices, integration apparatus, etc.|
|Deals with Exploratory Data.||Software Engineering focuses on systems building.|
|Data Science is Process Oriented||Software Engineering is methodology-oriented.|
|Skills include programming, machine learning, statistics, data visualization.||Skills include the ability to program and code in multiple languages.|
Data science deals with data and prediction and it is often not obvious what a software engineer has to do with this data-centric or data-driven team. This is because:
- A software engineer in a data science team is only an engineer with a knowledge of data;
- A data scientist knows mathematics and statistics to understand the problem and the product;
- They also know programming languages to build the model
So the question arises, how is software engineering important for data science or what does a software engineer brings in the data science team, and here is the answer:
Importance of Software Engineering
Software Engineer plays an important role when it comes to productization of data science application by adding hardware, enhancing the performance, so that the data science work can be provided to external customers. Some of the responsibilities are:
Building APIs: Data scientist converts the models to APIs that can be easily used by other applications but a Software engineer has to ensure that the APIs created from the model is scalable, flexible and reliable. They also use the models built by data scientists and tests and deploys them.
Model Examination: The final product relies totally on the software engineer. They has to make sure that the model made by the data scientist can be used as a common model and that it can be easily managed. By easy management, it means that they has to make sure that the model can be easily moderated to suit the other product requirements as well. For this reason, they need to be updated with all the changes made in the code.
Model testing and deploying: Any model, big or small, complex or easy, made by data scientists must be tested. His job is to review the code or the model created by the data scientist. Unit testing, branch testing, integration testing, security testing of the model is a part of his job. After testing, they take a decision to deploy the model.
And now since all the software requires basic data like customer needs, famous functionalities, etc. , data scientist are becoming an important part of software development team as well. So we can safely say that both are dependent on each other and completes each other.
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