Mobile Data Science (2024 Fall)

1This lecture introduced key concepts in data science with a focus on sensor data analysis, covering topics such as mobile and fixed sensing, contextual computing, and data visualization. The session emphasized the importance of understanding the data collection process, handling noise, and managing missing values. Techniques like outlier detection, imputation, and data preprocessing using tools such as Python were discussed. Practical applications included using machine learning models to analyze sensor data for various contexts, preparing students to tackle real-world data science challenges.

Time Series Analysis and Forecasting (2024 Fall)

2This lecture covered the basics of time series analysis, focusing on identifying patterns like trends and seasonality and introducing the ARIMA model for forecasting. Techniques for handling non-stationary data were discussed, along with methods for evaluating model accuracy using metrics such as MAE and RMSE. Practical applications using tools like R and Python were demonstrated, preparing students to apply these methods to real-world datasets.

Data Structure (2024 Spring)

3This course covers fundamental data structures and algorithms essential for efficient data manipulation and problem-solving. Lectures include topics such as inheritance, polymorphism, and recursion, with a focus on advanced structures like trees, hash tables, and heaps. Key algorithms such as sorting, searching, and graph traversal (including Dijkstra’s algorithm) are explored in depth. The course emphasizes practical implementation and performance analysis, preparing students to tackle complex computational problems using efficient data structures.

Data Structure (2023 Spring)

4This course provided an in-depth exploration of fundamental and advanced data structures and algorithms. Topics included array manipulation, linked lists, stacks, queues, and more complex structures such as trees, graphs, hash tables, and heaps. Emphasis was placed on algorithmic efficiency, exploring sorting and searching algorithms, dynamic programming, and graph traversal techniques like breadth-first and depth-first search. Practical implementations were carried out in Java, allowing students to apply these techniques in solving computational problems efficiently.

Cloud Computing (2022 Fall)

5This course introduced the principles of cloud computing, focusing on cloud architecture, deployment models (IaaS, PaaS, SaaS), and virtualization. Lectures covered resource management, distributed storage systems, and cloud security. Students learned to work with cloud service providers such as AWS and Azure, exploring tools for cloud application deployment and scaling. Key concepts such as containerization (Docker), microservices, and serverless architectures were demonstrated, preparing students to design and manage scalable cloud-based solutions.

Sensor Data Science(2022 Spring)

6This course focused on the collection, processing, and analysis of sensor data. It explored how data is gathered from mobile and fixed sensors, handling noisy and incomplete datasets. Key topics included sensor fusion, data imputation, outlier detection, and feature extraction. Tools like Python were used for data preprocessing and building machine learning models to analyze sensor data for context-aware systems. The course prepared students to apply data science techniques to real-world sensor data in various domains, such as healthcare and smart cities.