Global Certificate in Urban Mobility Data Analytics

• Introduction to Urban Mobility Data Analytics: Defining urban mobility, data analytics, and their intersection. Understanding the importance of data-driven decision making in urban mobility planning.
• Data Collection and Management: Identifying and gathering relevant data from various sources (e.g., public transportation, ride-sharing, GPS, sensors). Ensuring data quality, consistency, and completeness.
• Data Cleaning and Pre-processing: Techniques for handling missing values, outliers, and incorrect entries. Normalizing and transforming data for further analysis.
• Data Visualization: Exploratory data analysis using visual methods. Presenting data in charts, graphs, and maps to reveal patterns, trends, and correlations.
• Statistical Analysis: Applying statistical methods to analyze urban mobility data. Hypothesis testing, regression analysis, and time-series analysis.
• Predictive Modeling: Building predictive models using machine learning algorithms. Identifying patterns and relationships between variables to forecast future trends.
• Transportation Mode Detection: Detecting transportation modes in multi-modal datasets. Distinguishing between walking, cycling, public transportation, and private vehicle usage.
• Traffic Flow and Simulation: Modeling traffic flow, congestion, and public transportation systems. Understanding the impact of infrastructure changes on urban mobility.
• Ethics and Privacy in Urban Mobility Data Analytics: Addressing ethical concerns, ensuring data privacy, and complying with regulations. Understanding the potential impact of data analytics on society and individuals.