There are few main concepts that are important to understand the terms used to get more familiar with DSS.
Each task in DSS is organized in individual projects to manage the data and associated tasks.
A DSS project is structured in the form of a flow
Any pre-processing or data manipulation on the datasets are managed using recipes. Recipes are the building blocks of your data applications. Each time you make a transformation, an aggregation, a join, … with the Data Science Studio, you will be creating a recipe.
There are two types of recipes used widely in DSS:
Visual recipes: Provide basic manipulation functionalities like data cleaning, filtering, grouping etc.
Code recipes: Used for integrating technical programming like R, Python etc.
The dashboard communicates result and give insights based on the analysis performed on the datasets.
This provides visual analysis of the dataset prior to the implementation on the flow which helps to dive deep into the data directly.
Jobs: Every build on the dataset is recorded as jobs to keep track of activities in the flow
Scenarios: Helps in automating and scheduling the tasks in the flow
Notebooks: DSS allows to draft code in interactive programming environment to make the analysis easy and efficient
Webapps: Users with Web coding skills can create advanced custom Web Apps using our dedicated editor and REST API
For more introduction on concepts of DSS, please navigate here.