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Low Code/No-Code Data Literacy with KNIME: From Basic to Advanced

Low Code/No-Code Data Literacy with KNIME: From Basic to Advanced

2h 18mAdvanced2024-05-21

Authors

KNIME AG Licensor

KNIME AG Licensor

Course details

Low-code/no-code data analytics tools have been instrumental in enabling the transition from manual, code-heavy, and resource-intensive environments to an automated, scalable, and insights-driven focus. Instead of commands, low-code/no-code platforms have built-in functionality that enables users to start creating data science applications via a drag-and-drop interface. In contrast to coding approaches, low-code/no-code technologies offer an opportunity for users to concentrate on data science and less on the lines of code. The intuitive visual environment also enables business users with less technical skills to build and consume applications. In this course, learn how to use KNIME Analytics Platform to access, blend, analyze and visualize data in a low-code/no-code environment. For anyone who works with data, this course can take your data literacy to the next level by learning how to use a tool that makes sophisticated data analytics accessible to non-technical people.

Skills covered

KNIMEData AnalysisData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off

Concepts

1. KNIME Analytics Platform

  • 01 - What is KNIME Analytics Platform
  • 02 - What is a node What is a workflow
  • 03 - How to install KNIME Analytics Platform
  • 04 - Tour of the user interface of KNIME Analytics Platform
  • 05 - Build your first workflow with KNIME Analytics Platform
  • 06 - How to import and export KNIME workflows
  • 07 - The KNIME Community Hub
  • 08 - Collaboration and execution on KNIME Community Hub
  • 09 - How to install KNIME extensions

2. Data Access

  • 10 - Data access with KNIME - Reading files
  • 11 - Data access with KNIME - Accessing databases

3. Data Cleaning

  • 12 - Data cleaning with KNIME - How to filter rows
  • 13 - Data cleaning with KNIME - How to filter columns
  • 14 - Data cleaning with KNIME - How to handle missing values

4. Data Transformation

  • 15 - Data transformation with KNIME - Numbers, strings, and rules
  • 16 - Data transformation with KNIME - How to split cells

5. Workflow Documentation

  • 17 - How to keep workflows tidy and documented

6. Data Merging

  • 18 - Data merging with KNIME - How to concatenate tables
  • 19 - Data merging with KNIME - Value lookup
  • 20 - Data merging with KNIME - How to join two tables

7. Data Aggregation

  • 21 - Data aggregation with KNIME - Simple aggregations
  • 22 - Data aggregation with KNIME - The GroupBy node
  • 23 - Data aggregation with KNIME - Pivoting

8. Data Export and Visualization

  • 24 - Data export with KNIME - Write to a CSV file
  • 25 - Data visualization with KNIME - How to create a scatter plot
  • 26 - Three steps to build an interactive dashboard with KNIME

9. Flow Variables

  • 27 - Flow variables in KNIME Analytics Platform - Concept and application

10. Components

  • 28 - What is a component in KNIME Analytics Platform
  • 29 - Create, modify, and configure a component in KNIME Analytics Platform

11. Workflow Control

  • 30 - What is a loop
  • 31 - Build, execute, and debug a loop in KNIME Analytics Platform
  • 32 - Switch workflow branch execution in KNIME Analytics Platform

12. Error Handling

  • 33 - Error handling with try-catch in KNIME Analytics Platform

13. Date and Time Data

  • 34 - Handling date and time data with KNIME - Convert strings, extract date, and time fields
  • 35 - Handling date and time data with KNIME - Modify and filter date and time data

14. Databases

  • 36 - Querying databases from KNIME Analytics Platform
  • 37 - Modify database tables from KNIME Analytics Platform

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