Oreilly – Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME 2025-1

Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME. This course is an introduction to data science, introducing you to fundamental data science concepts, methods, algorithms, and applications. You will also learn how to implement data science best practices using a visual, low-code/no-code, and intuitive software environment called KNIME.

What you will learn:

• Data Science Explained: Defining Data Science and Understanding Related Terms and Concepts
• Introduction to data science tools: Familiarity with various data science tools and platforms and using the KNIME platform
• Developing Machine Learning Models with KNIME: Developing and testing machine learning models using KNIME
• Best practices in data science: Familiarity with best practices in data science, such as dealing with data imbalance, cross-validation, and model ensemble techniques
• Text Analysis: Learn the fundamental concepts of text analysis and natural language processing, and its applications such as sentiment analysis and topic modeling.

Who is this course suitable for?

• This course is suitable for anyone interested in learning data science and starting to practice it.

Details of the course Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME

• Publisher: Oreilly
• Instructor: Dursun Delen
• Training level: Beginner to advanced
• Training duration: 4 hours and 46 minutes

Course headings

• Introduction
• Data Science Made Easy: Introduction
• Lesson 1: Data Science Overview
  Topics
  1.1 Definition, Terminology, and a Simple Taxonomy
  1.2 Data Science Process
  1.3 Data Science Methods and Algorithms
  1.4 AI/ML Evolution
• Lesson 2: Data Science Tools
  Topics
  2.1 Tool Landscape
  2.2 Introduction to KNIME AP
  2.3 Nodes and Extensions
  2.4 KNIME Demo with Iris Dataset—Part 1
  2.5 KNIME Demo with Iris Dataset—Part 2
• Lesson 3: ML Model Development with KNIME
  Topics
  3.1 Data Ingestion and Preparation—Part 1
  3.2 Data Ingestion and Preparation—Part 2
  3.3 ML Model Building and Testing
  3.4 Comparative Assessment
• Lesson 4: Best Practices in Data Science and AI/ML
  Topics
  4.1 Data Balancing for Class Imbalance Problem
  4.2 Cross Validation for Bias-Variance Tradeoff
  4.3 Model Ensembles (with Bagging Boosting)
  4.4 Model Explainability (XAI)
• Lesson 5: Text Analytics
  Topics
  5.1 Overview of Text Mining and Natural Language Processing (NLP)
  5.2 Text Mining Process
  5.3 TM Applications―Sentiment Analysis
  5.4 TM Applications―Topic Modeling
• Summary
• Data Science Made Easy: Summary

Prerequisites for the Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME course

Course images

Sample course video

Download link

Download Part 1 – 1 GB

Download Part 2 – 396 MB

File(s) password: www.downloadly.ir

File size

1.3 GB