Course Code: knbsp
Duration: 35 hours
Overview:

This instructor-led, live training (online or onsite) is aimed at data professionals who wish to use KNIME to solve complex business needs.

It is targeted for the audience that doesn't know programming and intends to use cutting edge tools to implement analytics scenarios, and at data scientists who wish to program in Python and R for KNIME.

By the end of this training, participants will be able to:

  • Install and configure KNIME.
  • Build Data Science scenarios
  • Train, test and validate models
  • Implement end to end value chain of data science models

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Outline:

Data Science with KNIME Analytics

KNIME Analytics Platform: Overview

  • Installation
  • Starting and customizing KNIME Analytics Platform
  • Nodes, data and workflows
  • The data science cycle

Data Access

  • Read Data from file
  • Accessing REST Services

ETL and Data Manipulation

  • Row & Column filtering
  • Aggregators
  • Join & Concatenation
  • Transformation: Conversion, Replacement, Standardization, and New Feature Generation
  • Data Preparation for Time Series Analysis

Exporting Data

  • Write to a file
  • Generating a Report

Data Visualization

  • Interactive Univariate Visual Exploration
  • Interactive Multivariate Visual Exploration
  • Advanced Visualization Features

Predictive Analytics using KNIME

  • Data Mining Basic Concepts
  • Regressions
  • Decision Tree Family
  • Model Evaluation

Controlling the flow

  • Workflow Parameterization: Flow Variables
  • Re-executing Workflow Parts: Loops
  • Cleaning up your Workflow

Hands on KNIME Analytics Platform based Case Study

KNIME with Python and R for Machine Learning

  • What is KNIME?
  • KNIME Analytics
  • KNIME Server

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Preparing the Development Environment

  • Installing and configuring KNIME

KNIME Nodes

  • Adding nodes
  • Accessing and reading data
  • Merging, splitting, and filtering data
  • Grouping and pivoting data
  • Cleaning data

Modeling

  • Creating workflows
  • Importing data
  • Preparing data
  • Visualizing data
  • Creating a decision tree model
  • Working with regression models
  • Predicting data
  • Comparing and matching data

Learning Techniques

  • Working with random forest techniques
  • Using polynomial regression
  • Assigning classes
  • Evaluating models

Summary and Conclusion