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Beginnt 4 June 2026 22:26

Endet 4 June 2026

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

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

code::dive conference

6076 Kurse


1 hour 5 minutes

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Übersicht

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

Lehrplan

  • 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

Fachgebiete

Conference Talks