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

Overview

Discover practical applications in Data Science by delving into real-world examples and understanding their business impacts. This session provides invaluable insights into successfully executing Data Science projects, focusing on concrete methodologies and structuring conclusions that are ready for real-world application.

Hosted on YouTube, this event is ideal for professionals looking to enhance their understanding and application of Data Science within the realm of Artificial Intelligence.

It falls under Artificial Intelligence Courses and Conference Talks, offering a blend of theoretical and practical knowledge for an engaging learning experience.

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

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Subjects

Conference Talks