DSS - Basic Concepts

There are few main concepts that are important to understand the terms used to get more familiar with DSS.

Projects 

Each task in DSS is organized in individual projects to manage the data and associated tasks.

Flow  

A DSS project is structured in the form of a flow 

Datasets 

Recipes

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. 

Dashboard  

The dashboard communicates result and give insights based on the analysis performed on the datasets.

Analysis 

This provides visual analysis of the dataset prior to the implementation on the flow which helps to dive deep into the data directly. 

Other concepts 

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.