"Big Data Mining and Analysis" is a core compulsory course for undergraduate majors in Data Science and Big Data Technology. It aims to cultivate students' ability to mine implicit, previously unknown relationships, patterns, and trends with potential value for decision-making from a large amount of data (or text), and use these effective information and rules to establish models for decision support, providing methods, tools, and processes for predictive decision support. Data mining technology helps users reveal the essential laws of development, discover the evolution characteristics and changing trends of data objects, and explore data patterns. It plays an important practical role in analyzing social and economic development trends, structures, and predictions in the current big data environment. This course aims to enable students to master basic skills such as Python scientific computing, data processing, and mining modeling through teaching. They will be able to use third-party extension packages such as Numpy, Pandas, Scikit-learn, and association rule algorithm code in Python to process, calculate, and analyze basic data mining problems and sample data. They will also gain a preliminary understanding of the basic knowledge of the deep learning framework TensorFlow 2.0 and the basic principles of multi-layer neural networks, convolutional neural networks, and recurrent neural networks, thus providing basic support for other professional courses or complex application problems.
