STATS 418
Tools in Data Science
Description: Lecture, three hours; discussion, one hour. Requisites: courses 404, 405. Limited to Master of Applied Statistics students. Tools for data acquisition, transformation and analysis, data visualization, and machine learning and tools for reproducible data analysis, collaboration, and model deployment used by data scientists in practice. Advanced R packages, analytical databases, high-performance machine learning libraries, big data tools. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Spring 2025 - Nate is a great instructor who has extensive industry experience in Data Science field. He also bring a few friends/ex-colleagues from previous work or UCLA Department of Statistics alumni. His class note and slides are very clear and useful. The entire coursework/projects are hands on experience what he have learn from his 10+ years of industry not heavily focus on R and theoretical but I would easy this is more practical learning experience. The class projects could be rigor depending on the subject of your choice. The requirement is simply implement a web API service using LLM. (i.e. OpenAI API, Huggingface, or Langchain) I highly recommend whomever in the field or who wants to be in DS.
Spring 2025 - Nate is a great instructor who has extensive industry experience in Data Science field. He also bring a few friends/ex-colleagues from previous work or UCLA Department of Statistics alumni. His class note and slides are very clear and useful. The entire coursework/projects are hands on experience what he have learn from his 10+ years of industry not heavily focus on R and theoretical but I would easy this is more practical learning experience. The class projects could be rigor depending on the subject of your choice. The requirement is simply implement a web API service using LLM. (i.e. OpenAI API, Huggingface, or Langchain) I highly recommend whomever in the field or who wants to be in DS.
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