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The journey from raw data to actionable insight involves several critical steps. First, there's **data collection and engineering**. This means gathering information from diverse sources, ensuring its quality, and structuring it in a way that makes it usable for analysis. In a city like *Hong Kong*, this could involve combining traffic sensor data, public transport schedules, air quality readings, retail sales figures, and even anonymized mobile phone location data. Then comes *exploratory data analysis (EDA)*, where we dive deep into the data to discover patterns, anomalies, relationships, and trends that might not be immediately obvious. This is where we start to see the story the data is telling us, identifying key drivers and potential indicators. For instance, we might discover a strong correlation between public holiday dates and increased tourist footfall in certain districts. These insights, while not *predictions* in themselves, form the foundation for building more sophisticated models.
