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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
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