Data Science Professionals
with KNIME

CODELESS Technique for Non-Programmer | Basic & Advance Proficiency Level

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About the Course

This professional competency certification program is developed and designed for professionals who want to start their journey in Data Science and explore career path as data science professionals. Is a partnership professional certification program by NASPSOFT (Malaysia) and KNIME (Switzerland). In this course, participants will learn how to use visual programming tools, KNIME Analytics Platform software to perform data science task, including data exploration, manipulation and machine learning. NO PROGRAMMING require. It is a CODELESS approach in data science.

In this course, participants will learn:

Create data science workflow with KNIME Analytics Platform without writing code.
Perform data science tasks, including data exploration, transformation and manipulation to gain insight.
Build machine learning workflow for predictive analytics using KNIME Analytics Platform.

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What is KNIME Analytics Platform

KNIME Analytics Platform is an open-source and free visual programming tools with integrated environment for data science and machine learning task. The full version of the software is completely free with no limitation of usage. It capable to perform:

Data Exploration - Univariate & Multivariate Analysis
Data Manipulation - Import/Export Data, Merge, Join
Machine Learning - Regression, Classification, Clustering, Recommendation System
Performance Metrics Computation & Validation - R-Squared, Confusion Matrix, Cross-Validation
Data Visualisation - Heatmap, Scatter Plot, Line Chart
Productionisation - Model Deployment, Dashboard

Competency Assessment by KNIME

The assessment is conducted by KNIME to measure your expertise of utilising KNIME as data science platform. Be certified internationally to gain more confidence and better recognitions for your data science skills.

L1: Basic Proficiency in KNIME Analytics Platform

Basic knowledge and skills to use KNIME Analytics Platform for data science.

Answer all 15 multiple-choice questions in 30 minutes.

General concepts of KNIME Analytics Platform
Import and exporting data
Create basic workflow
Basic data exploration & manipulation

L2: Advance Proficiency in KNIME Analytics Platform

Test your knowledge and skills at advance level about KNIME for more advance applications.

Answer all 15 multiple-choice questions in 30 minutes.

Flow variables
Nodes's configurations
Data and Times Data
Workflow Control
Advance Data Exploration & Visualisation

Anchor 1

Course Modules

Module 1: Basic KNIME for Data Science

Participants will learn about the fundamental concepts of data science and demonstrate its application using appropriate and easy to understand case study on how industry apply use the knowledge into their business. They also learn on how to use user-friendly visual programming tools to perform basic data processing task, such as importing data, basic exploration, manipulation and aggregation. This module will prepare participants to attend for competency certificate L1: Basic Proficiency in KNIME Analytics Platform.

Durations: 1-Day (6-hrs)

Data Science Fundamental
Understanding KNIME Platform
Functional Node & Configuration
Import and Export Data
Data Types
Building Logical Processing Workflow
Basic Data Manipulation & Aggregation

Module 2: Advance KNIME for Data Science

This module will teach participants with more advance topics on how to use KNIME Analytics Platform to process data. Participants will learn more advance features and nodes to perform complex operation in data science process. This module will prepare participants to attend for competency certificate L2: Advance Proficiency in KNIME Analytics Platform.

Durations: 1-Day (6-hrs)

Flow variable
Nodes Configuration
Dates and Times
Workflow Control
Data Visualization
Data Exploration

Module 3: Machine Learning with KNIME

This module teach participants specific topics on machine learning for predictive modelling. Learn to build machine learning workflow to generate predictive model using logical step-by-step approach. Participants will also learn how to validate the performance of the machine learning model and interpret the findings obtained from predictive analytics.

Durations: 1-Day (6-hrs)