Data scientists are great mathematicians with a lot of cross-disciplinary knowledge and a super ability for analysis. The task of this specialist is to find the ideal formula for training artificial intelligence. Among all the existing algorithms, they should look for the one that is better suited to solving the project’s problems and understand what exactly is going wrong. However, in order to increase the competitive advantage of the company, data scientists need to cooperate with software engineers, like dedicated Laravel engineer
Working with data is more research-oriented than software development, for instance, Laravel application development. Laravel developer can take over the technical side of the issue. At any stage of the work, both data scientists and engineers must feel responsible for the problem and be able to contribute. There is continuous communication, so that potential inconsistencies are identified early. In this article, we’ll take a closer look at the challenges a software developer and data scientist face in the process and how collaboration between them can be improved.
Problems Software Engineers and Data Scientists May Be Encountered and Its Solution
Working closely with data, scientists help engineers develop analytical and research skills to write better code. The exchange of knowledge between users of data warehouses and data lakes is improving, making projects more flexible and providing more sustainable long-term results.
The data scientist and the developer are working towards 2 common goals: improving the products for customers and improving the decisions made by the business. However, in the process of work, problems arise and specialists need to solve them together:
Finding insight in the data
The data scientist can find the problem in identifying new data sources that can be integrated into predictive models, while the developer focuses more on problems that are based on specific requirements.
Solution: The developer should focus on the implementation of the solution, the requirements for which are determined gradually, while the data scientist focuses on the more theoretical area of research and discovery.
Poor data quality
Errors in data collection and sampling are cited as reasons for poor quality. Data quality issues also make it difficult for data scientists to be confident that they are doing the right thing. For a developer, this is fraught with the fact that the product he received from the data scientist is initially incomplete. Worth mentioning that both software engineering and data science projects have pretty high failure rates with up to 75% of software projects failing and 87% of data science projects never making it to production.
Solution: The data scientist’s job is to fix data quality issues, even if they are the primary consumers of data. Soon the task is transferred to the developer and then, he starts his part of the work.
Data integration from multiple sources
Often the data is in different locations and must be combined for analysis. Factors that make the data difficult to understand include lack of documentation, inconsistent schemas, and multiple possible interpretations of data labels.
Solution: Data is stored in silos, so the developer and data scientist’s job is to find and create keys that combine disparate sources into templates that help them learn and improve the customer experience.
Communicating task criteria to developers
In communication between data scientists and developers, the problem of miscommunication can arise. Often, developers are not interested in the data scientist’s tools as they have other responsibilities.
Solution: The data scientist should explain the problem in detail and enlist the support of the engineering team to collect high quality data.
How Software Engineers and Data Scientists Can Collaborate Together
Along with the emergence of such a position as a data scientist and the proliferation of big data, there was a need for collaboration between an engineer and a data scientist with a lot of experience in mathematics, who started programming.
Ways of cooperation:
When transferring production data to data scientists, the following situation may arise: they may have either too limited access or very large access to the database. In the first case, they are constantly requesting access to the data export, in the second, they are making queries that constantly affect the production database.
To solve this problem, there is a need to define a way to transfer all raw data to data scientists in a separate environment from production. The key idea is that because we don’t know what data might be needed in the future, we keep everything flat in a place easily accessible to data scientists. The storage space is exactly what a software engineer should create. The value of Laravel developers can be proved in such a case.
Data scientists usually work with one-off scripts that contain, for example, SQL queries. For the next job, they can copy data from the previous script to another. One way to create such a library is to set aside time each week to work on it, as data scientists will gradually understand what transformations they need to do often. A software engineer can help with such a library. A software engineer can review new writing code and find opportunities to add new functionality to a data analysis toolbox.
The result of the data scientist’s work is algorithms that extract information from raw data. The specialist improves the algorithm so that it is better than yesterday and in line with business goals. There is a basic need for a continuous evaluation process for data science algorithms. This process must be built into the product itself. The goal is for the engineer to use his big system building skills and the data scientist to guide him through the correct problem setting. This will create a good opportunity for cooperation.
Working with data follows the GIGO (garbage in – garbage out) principle: if data scientists deal with potentially incorrect data, then the results of even the most sophisticated analysis algorithms will be incorrect. Software engineers solve this problem by building pipelines for processing, cleaning and transforming data and allowing the data scientist to work with high-quality data.
By working closely with engineers, data scientists can focus on the research side, creating out-of-the-box machine learning algorithms. And engineers need to focus on scalability, data reuse, and ensure that the input and output pipelines for each project are aligned with the global architecture. This separation of duties ensures consistency in a dedicated Laravel team working on different machine learning projects.
Conclusion
Collaboration helps to effectively create new products. Speed and quality are achieved thanks to the balance between creating a service for everyone and the implementation of each specific need or project. In companies that aim to develop a culture of working with data and build business processes on its basis, data scientists and software engineers complement each other and create a complete data analysis system.
