The spectrum of code-centricity on data science platforms ranges from “code-free” to “code-based.” Data science platforms frequently boast that they provide environments that require no coding, and that are code-friendly as well. Where a given platform falls along this spectrum affects who can successfully use a given data science platform, and what tasks they are able to perform at what level of complexity and difficulty. Codeless interfaces supply drag-and-drop simplicity and relatively quick responses to straightforward questions at the expense of customizability and power. Code-based interfaces require specialized coding, data, and statistics skills, but supply the flexibility and power to answer cutting-edge questions.
Codeless and hybrid code environments furnish end users who may lack a significant coding and statistics background with some level of data science capabilities. If a problem is relatively simple, such as a straightforward clustering question to identify customer personas for marketing, graphic interfaces provide the ability to string together data workflows from a pool of available algorithms without needing to know Python or other coding languages. Even for data scientists who do know how to code, the ability to pull together relatively simple models in a drag-and-drop GUI can be faster than manually coding them, and this also avoids the problem of typos and reduces the need for debugging code technicalities at the expense of focusing on the pure logic without distractions.
Answering a more advanced question may require some level of custom coding. Your data workflow may be constructed in a hybrid manner, composed of some pre-built models connected to nodes that can include bespoke code. This permits more adaptability of models, and makes them more powerful than those restricted solely to what a given data science platform supplies out of the box. However, even if a data science platform includes the option to include custom code in a hybrid model, taking advantage of this feature requires somebody with coding knowledge to create the code.
If the problem being addressed is complex enough, sharper coding, statistics, and data skills are necessary to create appropriately tailored models. At this level of complexity, a code-centric interactive development environment is necessary so that the data scientist can put their advanced skills into model construction and customization.
Data science platforms can equip data science users and teams with multiple interfaces for creating machine learning models. What interfaces are included say a fair bit about what kind of end users a given platform aims to best serve, and the level of skill expected of the various members of your data science team. A fully-inclusive data science platform includes both a GUI environment for data analysts to construct simple workflows (and for project managers and line of business to understand what the model is doing from a high-level perspective), as well as a proper coding environment for data scientists to code more complex custom models.