End to End Data Science Practicum with Knime

Overview

The course starts with a top down approach to data science projects. The first step is covering data science project management techniques and we follow CRISP-DM methodology with 6 steps below:

Syllabus

• **Introduction to Data Science Projects**
Overview of Data Science
Importance of Project Management in Data Science
Introduction to the CRISP-DM Methodology
• **Business Understanding**
Defining Project Objectives
Assessing Project Feasibility
Identifying Key Stakeholders
Translating Business Goals into Data Science Goals
• **Data Understanding**
Data Collection Techniques
Data Exploration and Profiling in Knime
Identifying Data Quality Issues
Initial Data Visualization
• **Data Preparation**
Data Cleaning and Preprocessing in Knime
Feature Engineering
Data Transformation Techniques
Handling Missing Data and Outliers
• **Modeling**
Choosing the Right Modeling Techniques
Building and Testing Models in Knime
Hyperparameter Tuning
Cross-validation Strategies
• **Evaluation**
Model Performance Metrics
Validation and Evaluation of Model Results
Aligning with Business Objectives
Interpreting Results for Stakeholders
• **Deployment**
Model Deployment Strategies in Knime
Model Monitoring and Maintenance
Creating a Deployment Workflow in Knime
• **Case Study Application**
Applying CRISP-DM to a Real-world Scenario
Team-based Project Work in Knime
Presentation of Findings and Recommendations
• **Conclusion and Course Wrap-up**
Lessons Learned from Practicum
Tips for Continuous Learning in Data Science
Resources for Further Study in Knime and Data Science

Taught by

Prof. Dr. Şadi Evren Şeker

Subjects

Data Science