Big Data Science and Technology major mainly learns the knowledge and technology related to big data processing and analysis, as well as machine learning, deep learning, artificial intelligence and other aspects.

1. Big Data Fundamentals

Big Data Science and Technology majors need to learn the fundamentals of the big data field, such as Hadoop, Spark and other big data frameworks and their components, understand distributed computing, and be familiar with data storage and processing methods.

2. Data Mining

Data mining is an important content in the big data science and technology major, which requires learning the principles and implementation methods of various data mining algorithms, including clustering, classification, association rule mining, anomaly detection and so on.

3. Machine Learning

Machine Learning is a technology widely used in Big Data and an integral part of the Big Data Science and Technology major, which requires learning different machine learning models and algorithms such as Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, etc., and learning to utilize TensorFlow, Keras and other frameworks to build neural networks for deep learning and training.

4. Data Visualization

Big Data Science and Technology majors also need to master data visualization techniques, presenting the results of data processing in a graphical way, which can help to better understand the data and find anomalies, including statistical charts, 3D visualization and large screen display.

5. Artificial Intelligence

Artificial Intelligence has a great deal to offer in today's era of big data, and big data science and technology majors also need to learn about artificial intelligence, such as causal reasoning, natural language processing and other technologies, and to master the combined use of artificial intelligence and big data.

In summary, learning big data science and technology majors need to master the basics of big data, data mining, machine learning, data visualization and artificial intelligence, etc. Understanding these technologies can help to obtain valuable information from massive amounts of data and use data-driven methods to help decision makers make more accurate judgments and decisions.