How to Do Practical Data Science - From Real-World Examples

Overview

Explore real-world Data Science projects, their business impacts, and methodologies. Gain insights into executing successful DS initiatives and structuring conclusions for practical applications.

Syllabus

• Introduction to Data Science
Overview of Data Science and its importance
Key roles and responsibilities in Data Science projects
• Overview of Data Science Methodologies
CRISP-DM Process Model
Agile Data Science Approaches
• Data Collection and Understanding
Identifying relevant datasets
Data acquisition techniques
Exploring and visualizing data
• Data Cleaning and Preprocessing
Handling missing values
Data transformation and normalization
Feature engineering techniques
• Real-World Data Science Project Examples
Case Study 1: Retail Customer Segmentation
Problem definition and business impact
Methodology and results
Case Study 2: Predictive Maintenance in Manufacturing
Problem definition and business impact
Methodology and results
• Model Building and Evaluation
Choosing the right algorithms
Training, validation, and testing
Performance metrics and model comparison
• Communicating Data Science Results
Data storytelling and visualization
Tailoring communication to stakeholders
Structuring concise and actionable conclusions
• Deployment and Productionization
MLOps basics
Deployment strategies for models
Monitoring and maintaining models in production
• Ethical Considerations and Best Practices
Understanding biases in data
Ensuring transparency and fairness
Data privacy and security concerns
• Conclusion and Future Trends in Data Science
Summary of key takeaways
Emerging technologies and methodologies in Data Science
Continuing education and career development in Data Science
• Practical Workshop (Optional)
Group project using a provided dataset
Application of learned methodologies
Presentation of findings and recommendations

Subjects

Conference Talks