May 17, 2023
Software Development vs. Data Science Development Process
Automation is at the very fabric of numerous business processes. Nearly every sector is striving to adopt the automation of core business processes. To achieve this, organizations need the necessary software tools required to run these processes and machinery. That’s where software and data science development come into play. Let’s see what the data scientists’ and software developers’ work is about.
When done correctly, data science and software development can streamline workflows, reduce dependency on manual labor, and save costs. This article will explore the intricacies of data science and software development processes, detailing everything from what they are to the key stages involved in the processes.
In this article, we want to bring the data science development process closer to you. We want to show you what data scientists’ work is like. Later in the text, we’ll move to the software development process and see how it differs from the data science development process.
Key Takeaways
- Data science development usually covers problem identification, data preparation, modeling, evaluation, deployment, and continuous improvement.
- Data preparation is often one of the most demanding stages because raw information must be collected, cleaned, transformed, validated, and delivered reliably.
- Data scientists focus on experimentation, statistical analysis, feature engineering, and model evaluation, while data engineers build the pipelines and platforms that make trustworthy data available.
- Software development follows more predictable functional requirements, whereas data science projects contain greater uncertainty related to data quality, feasibility, and achievable model performance.
- Data science projects should be estimated in stages because discoveries made during exploration and modeling can change the scope, timeline, and required infrastructure.
- Deployment is not the end of a data science project: production models require monitoring, maintenance, data-quality controls, and periodic reevaluation.
What is the data science development process?
The term data science describes the study that uses modern tools and techniques to process massive volumes of data to find unseen patterns and derive meaningful information that can be used to inform business decisions.
Therefore, data science development is a culmination of the steps taken to design, build, deliver, and maintain data science projects. Data science projects vary in complexity and purpose. However, these processes typically follow a specific sequence of processes. Let’s discover the data science process.
Key steps in the data science development process
The general steps involved in a typical data science development process are:
Data Science Process: Problem identification
Without a clearly defined problem, you cannot ensign a system to tackle it. Therefore, the first step in any data science process is problem identification. Here, data scientists seek to understand the way data science may be useful in a specific domain and the specific tasks it can accomplish.
Besides identifying specific challenges, this step also helps data scientists to come up with a way to measure their success throughout the project and determine the type and amount of resources required to complete the project. Defining the scope, data requirements, dependencies, success criteria, and areas of uncertainty is also essential when determining how to estimate a data science project. In some cases, big problems are broken down into smaller tasks that can be solved and estimated individually. [1]
Data Science Process: Data Processing
This is arguably the most important phase in the data science process. It’s also the longest and most challenging step in the process. Effective data preparation for AI initiatives typically starts with identifying and acquiring the necessary data, assessing its quality, cleansing it, and transforming it into a format suitable for the model. The exact approach may vary depending on the available sources, the type of data, and the steps planned for the modeling process.
For instance, you may choose to use streaming or batch processing to feed data into your model. Likewise, image data is typically fed into data science projects in an array.
After settling on a specific way to feed data into the model, data scientists perform the necessary preprocessing steps to ensure it is clean enough for use. In production environments, data scientists typically define the analytical and feature requirements, while data engineers build and maintain reliable pipelines that deliver the required data. Understanding data science vs. data engineering helps organizations assign these responsibilities correctly and avoid gaps between model development and production infrastructure.
Data Science Process: Modeling
This is the shortest and easiest phase of any data science project. The form it takes depends on several factors, including the problem at hand, how you defined success in the first step, and how you processed the data.
Data scientists already have a modeling method in place at this phase since they decided on it in the problem identification phase. This may include anything from using simple graphical exploration to more complex forms like regression or clustering.
Regardless of the method chosen, most of the work at this phase revolves around ensuring the data is in the correct format and then training the model. That’s what data scientists are also responsible for.
Data Science Process: Evaluation
Before the model can be deployed, data scientists first have to evaluate it to ensure it meets the project’s requirements. At this phase, data scientists typically use the measures of success defined during the problem identification phase.
Depending on how they set up the model and processed their data, data scientists may choose to use a holdout data set or training data set to evaluate the model.
The main purpose of an evaluation is to test how well the model performs in terms of accuracy and reliability. A model’s accuracy measures how well it performs in terms of the project’s goals, and its reliability measures how consistently the model achieves its goals. [2]
Data Science process: Deployment
Once data scientists have carefully evaluated the model and now are satisfied with the results, it’s time for the final stage in the data science process – deployment. At this stage, data scientists deploy the model into production so it can perform the intended goals. This can be making changes to the business, checking whether previous changes were successful, or continuously receiving and processing live data.
What is the software development process?
Software development, also known as the Software Development Life Cycle (SDLC), is a structured approach used to develop software for a system or project. The system provides an international standard for organizations and software engineers to build and improve their computer programs.
The SLDC offers a defined structure that software engineers follow in designing, creating, and maintaining computer programs. This way, development teams are able to build effective programs within a predefined budget and timeframe.
Software development is typically composed of various steps and approaches that break the development process into smaller steps that can be done in order or simultaneously in parallel.
Key steps in the software development process
A typical software development life cycle is comprised of six steps, including:
Software development: Needs identification
Needs identification works much like your average market research process. The main reason for making needs identification is to determine the software product’s viability and identify any potential pitfalls.
The process typically involves identifying ideal functions and services necessary to make the product useful to the end user. Software developers can get this information by brainstorming or performing surveys on potential customers to figure out what they need.
Software development: Requirement analysis
This is the second stage of the software development process. Here, project stakeholders deliberate and determine the proposed product’s technical and user requirement specifications. This enables them to create a detailed outline of the scope of the project, project components, and testing parameters. It’s also the stage where developers choose the ideal approach for the project.
As such, requirement analysis is a team effort that involves numerous stakeholders, including project managers, software engineers, users, testers, and quality assurance teams. At the end of this stage, development teams record their decisions on a software Requirement Specification document, which acts as a reference point throughout the project’s implementation.
Software development: Design
In the design phase, software engineers and developers discuss and draw up advanced technical requirements required to meet the project’s requirements. They discuss various factors, including team composition, risk levels, applicable technologies, budget, project timeframe, and architectural design.
All this information is recorded on the Design Specification Document (DSD), which specifies the product’s design, components, front-end representation, and user flows. This way, developers and testers get a template to refer to, thus reducing the possibility of delays and flaws in the finished product.
Development and implementation
In the development stage, software engineers write code based on the predetermined product specifications and requirements. The front-end developers build user interfaces, while the database administrators create and allocate all relevant data in the database. Also, based on the company’s procedures and guidelines, programmers also test and review each other’s code.
At the end of the coding process, developers deploy the product in a testing environment, which allows them to test the pilot project before making it readily available to users. This way, they can eliminate errors and improve the product’s performance.
Software development: Testing
As the name entails, the testing phase involves testing the software product for bugs and verifying its performance before making it available to end users. Testers mainly use exploratory testing or a test script to verify the performance of individual components of the software. If they detect any problem with the code, they fix the issue and then run the test repeatedly until they eliminate all bugs. [3]
Software development: Deployment and maintenance
After testing and validation, developers can now deploy the software and make it available for users. They also create a maintenance team to handle customer queries with the software. Most software issues require a minor hotfix, but more severe issues require a software update.
Types of software development approaches
There are four main types of software development approaches. They include:
Waterfall approach
A waterfall approach in software development is a sequential approach to software development. The model involves a series of phases that must be completed one at a time before moving to the next phase. This means that the phases don’t overlap, and activities do not interact.
In the waterfront approach, developers determine users’ needs, define project requirements, design the system, and test it before delivery to the end user. This approach is mostly used for large projects with well-defined requirements. Unfortunately, its sequential nature makes it unsuitable for complex projects with changing requirements. [4]
Spiral approach
The spiral approach in software development is a risk-driven approach to software development. The process typically involves repeated planning cycles, risk analysis, development, and testing. With each cycle, software engineers use data from previous tests to inform new changes to mitigate any project risks. This makes it a suitable option for complex projects with significant risk since it allows the early detection and mitigation of issues. [5]
Agile approach
The agile approach in software development is one of the most flexible approaches to software development. The process emphasizes collaboration and continuous feedback between the developers and other stakeholders.
This approach is iterative and incremental development whereby each alliteration delivers a small part of working software. As such, the approach allows for rapid adaptation to changing requirements and user feedback, thus promoting the early detection and resolution of problems. [6]
Incremental approach
As the name suggests, the incremental approach in software development involves delivering working software in small, incremental cycles. Software engineers typically develop the software in a series of iterations, with each iteration adding new functionality or improving the performance of existing functionalities.
In this approach, project managers divide the project into small, manageable pieces, which are developed in a series of iterations. Each iteration involves planning, designing, developing, testing, and deploying a small functional subset of the software. Each iteration is also tested and approved before moving to the next one. [7]
The software development process vs. the data science development process
Although the software and data science development processes both take a systematic approach to problem-solving and involve using technology and programming languages, they have numerous differences.
For starters, they solve different kinds of problems. Take the software development process, for instance. It typically involves building software products or applications that solve a specific problem for a user or group of users. On the other hand, the data science process aims to uncover insights and patterns from data to enable decision-making or improve understanding of a specific domain.
Both approaches also differ in the tools and methods they use. The software development process relies primarily on software engineering principles like agile development, version control, and testing frameworks. Conversely, the data science process involves statistical modeling and machine learning techniques, which require a deep understanding of data analysis and visualization tools.
The two approaches also differ in terms of their validation process. The software development process, for instance, involves testing the software against predefined criteria to ensure that it functions as intended. On the other hand, the data science process’s validation process involves using processes that evaluate the model’s performance and its predictive ability.
Final thoughts on data science and software development
Although the software and data science development processes appear similar on the surface, they have distinct goals, tools, and validation processes. Additionally, despite requiring a rigorous approach to problem-solving and a deep understanding of programming languages and technology, data scientists and software developers take a different approach to solving problems and solving problems in different domains.
For instance, the software development process focuses on creating software products and applications to solve specific problems. In contrast, the data science process aims to uncover insights and patterns from data in order to enable decision-making or improve understanding of a specific domain.
By recognizing the differences between data science and software development, and between the work of software developers and data scientists, organizations can better understand the skills and expertise needed to undertake each task successfully. This way, organizations can leverage them correctly in order to deliver value to their customers and stay competitive in a rapidly evolving business environment.
References
[1] Tutorialspoint.com. Programming Methodologies. URL: https://www.tutorialspoint.com/programming_methodologies/programming_methodologies_understanding_the_problem.htm. Accessed April 20, 2023
[2] Slide share.net. Software Development Process Evaluation. URL: https://www.slideshare.net/cunniman/6-the-software-development-process-evaluation-2871704. Accessed April 20, 2023
[3] Imperva. com. Machine Learning Testing for Data Scientists. URL: https://www.imperva.com/blog/machine-learning-testing-for-data-scientists/. Accessed April 20, 2023
[4] Tutorialspoint.com. SDLC Waterfall Model. URL: https://www.tutorialspoint.com/sdlc/sdlc_waterfall_model.htm, Accessed April 20, 2023
[5] Geeksforgeeks.org. Software Engineering Spiral Model. URL: https://www.geeksforgeeks.org/software-engineering-spiral-model/, Accessed April 20, 2023
[6] Techtarget.com. Agile Software Development. URL: https://www.techtarget.com/searchsoftwarequality/definition/agile-software-development. Accessed April 20, 2023
[7] Javapoint.com. Software Engineering Increment Model. URL: https://www.javatpoint.com/software-engineering-incremental-model, Accessed April 20, 2023
FAQ
Why are data science projects more difficult to estimate than traditional software projects?
A traditional software project can often be divided into functions that have clearly defined expected behavior. A data science project includes additional uncertainty related to data availability, data quality, experimental results, achievable model performance, and production integration. The initial estimate should therefore be refined after discovery, data exploration, and proof-of-concept stages rather than treated as a fixed commitment established before the data has been examined.
Which parts of a data science project should be estimated separately?
The estimate should distinguish business discovery, data access, data preparation, exploratory analysis, model development, evaluation, application integration, production deployment, documentation, and maintenance. Infrastructure and data engineering work should also be estimated separately from experimentation because building reliable pipelines and production environments may require different specialists and technologies. Dividing the project into stages makes it possible to update the estimate as new information becomes available.
When should a data engineer join a data science project?
A data engineer should be involved early when information is fragmented across systems, difficult to access, generated in real time, or too large to process manually. Early collaboration helps ensure that the data required during experimentation will also be available reliably in production. Data engineers typically handle ingestion, storage, transformation, orchestration, scalability, and data-quality infrastructure, while data scientists concentrate on analysis, feature development, and model evaluation.
How can a team estimate the effort required for data preparation?
The team should first profile representative samples and identify missing values, duplicates, inconsistent formats, invalid records, labeling requirements, access restrictions, and differences between source systems. It should also determine whether historical data reflects the environment in which the model will operate. Fragmented, unstructured, poorly documented, or inaccessible data usually increases both engineering effort and project uncertainty, so data preparation should be assessed before finalizing the complete project estimate.
How can data leakage be prevented during model development?
The training, validation, and test datasets should be separated before preprocessing operations that learn information from the data, such as scaling, feature selection, or imputation. Transformations should be fitted only on the training subset and then applied to validation and test data through a reproducible pipeline. Teams must also verify that no feature contains information that would only become available after the real-world prediction is made.
What should the acceptance criteria for a data science solution include?
Acceptance criteria should combine technical and business measures. Depending on the use case, these may include predictive performance, false-positive and false-negative rates, latency, reliability, operating cost, interpretability, fairness, security, and the measurable effect on the targeted business process. The criteria should also define the situations in which a prediction requires human review or must not be used automatically.
Who is responsible for a model after it has been deployed?
Production responsibility is usually shared by data science, data engineering, software engineering, MLOps, business owners, and risk or compliance teams. The organization should assign owners for model performance, data pipelines, infrastructure, business outcomes, incident response, and approval of significant updates. Monitoring should continue after deployment because changes in data, user behavior, systems, or the operating environment can reduce the quality and reliability of the solution.
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