Dataiku Data Science Studio(DSS) Designer-Beginners Bootcamp

Navigate the Dataiku data science studio designer studio UI, exploring the landing page, projects, design node, and credentials. Build flows, datasets, recipes, notebooks, and automation to develop pipelines across zones.

Import raw data from cloud storage into Dataiku DSS Designer by configuring zones, selecting S3 datasets, previewing CSV files, and computing metadata before moving to a flow.

Implement risk ranges from FICO scores, define loyalty by days active, apply targeted risk, then clean data and prepare a dataset with a primary key using regex.

Sort data by risk range, demographics, and targeted reward using the sort recipe, then filter for immunity booster kit records to reveal counts by risk and demographic group.

Explore the stack recipe in Dataiku Data Science Studio, learning to union two datasets with similar schemas, address incompatibilities through preparation and column renaming, and verify results.

Explore the split recipe to divide cleaned data into train and test datasets using a random ratio, enabling model training and evaluation with CSV Metastore outputs.

Explore the python code recipe introduction in Dataiku DSS Designer, learning how core recipes auto-generate input/output code, convert datasets to dataframes, apply pandas and numpy operations, and write back.

Learn how to use Python code recipes in the DSS designer to create columns with constant and random values, import libraries, delete and rename columns, and validate data flow.

Create charts and dashboards in data iq data science studio, from vertical bars to line charts with date handling. Build interactive dashboards using tiles, filters, and presentation mode.

Explore statistics cards in Dataiku DSS designer series, using univariate and bivariate analysis, automatic card suggestions, and visual insights from demographics, risk, and rewards.