CPSC 330 is a course in applied machine learning, and is closely related to the field of data science. The course is about using systems that learn from data to make predictions or produce insights.
There is overlap between CPSC 330 and CPSC 340. However, CPSC 330 is about using machine learning whereas CPSC 340 involves implementing machine learning algorithms from scratch. CPSC 330 is broader, and includes topics like data cleaning and communicating your results; CPSC 340 goes deeper into the algorithms and the mathematical / numerical considerations underlying them. CPSC 340 has many more prerequisites, especially in terms of math courses. The best choice will depend on your interests and goals. It is also perfectly reasonable to take both courses.
Topics that are only in CPSC 330:
build an ML pipeline
data cleaning
survival analysis
time series data
natural language processing
communicating your results
and more
Topics that are only in CPSC 340:
probabilistic models
optimization and loss functions (this is a major topic in CPSC 340)
dimensionality reduction
and more
Topics that appear in both courses (but typically in a very different way):
basic classification and regression methods: decision trees, KNN, etc.
fundamentals of learning
clustering
recommender systems
deep learning