Nadal - Lecture on Data Science Principles

Introduction

• Definition of Data Science: Intersection of data engineering, scientific methods, and domain expertise.

Key Components

Data Engineering

• Data collection: Methods to collect data from diverse sources.
• Data cleaning: Techniques to preprocess and clean data.
• Data transformation: Processes to change data into the desired format.

Statistical Methods

• Descriptive statistics: Summarizing and describing data features.
• Inferential statistics: Making predictions or inferences about a population from a sample.

Machine Learning

• Supervised learning: Models trained on labeled data.
• Unsupervised learning: Models trained on unlabeled data.
• Reinforcement learning: Models that learn by interacting with the environment.

Real-world Applications

• Healthcare: Predictive models for disease diagnosis.
• Finance: Fraud detection algorithms.
• Marketing: Customer segmentation and targeting.

Challenges in Data Science

Data Quality

• Incomplete data: Handling missing values.
• Noisy data: Techniques to smooth out noise.
• Inconsistent data: Ensuring data consistency.

Ethical Considerations

• Data privacy: Protecting user data.
• Bias in algorithms: Ensuring fairness and avoiding discrimination.

Conclusion

• Importance of continuous learning and adaptation in the field of Data Science.
• Need for collaboration between domain experts, data engineers, and statisticians.