| Description
| This course aims to develop students’ abilities to apply data analysis techniques to solve practical problems. The course covers data types and data preprocessing, including data collection, cleaning, integration, transformation,and feature engineering techniques for data preparation. It also introduces
commonly used data mining methods, such as classification, clustering,association analysis, and predictive model construction. In addition, the course integrates big data analytics concepts with practical applications to cultivate
students’ capabilities in analyzing large-scale, diverse, and high-velocity data.Furthermore, the course explores Natural Language Processing (NLP) and text mining techniques, including word segmentation, term frequency analysis, sentiment analysis, topic modeling, and text classification, with applications in social media, review data, and document analysis. Through case studies and hands-on analytical tool practices, students will enhance their practical
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