Social Survey Analyst Level 2 1st Written Exam - Subject 1: Survey Methodology and Design

Part I LangChain Practice in code how to load a PDF as page-by-page Documents, chunk it, convert the chunks into vectors using embeddings, and index them in a vector store Convert questions into vectors and perform similarity searches (including scores, vectors, and MMR), then wrap the search in a Retriever (Runnable) and connect it to RAG Review practical criteria for assessing search quality, security and governance (including prompt injection), and common mistakes Part II Implement in R the Random Forest algorithm, which overcomes the limitations of decision trees, and accurately interpret model results A complete workflow ready for practical use, including hyperparameter tuning, cross-validation, and ROC curve analysis Part III Building an Automated Classification and Response System for Complaints and Inquiries - Practical Prompt Chains and Multi-Agent Systems

Understand the core principles of the DBSCAN algorithm for discovering clusters of complex shapes and learn how to apply it to real-world data using R. Develop your density-based clustering skills through hands-on practice covering the concepts of core points, border points, and noise points, as well as parameter tuning, visualization, and result interpretation.

We are rooting for your success in your exam preparation. The lecture is based on the content included in the PDF materials. PDF download link: https://www.inflearn.com/clip/1364/%EC%82%AC%ED%9A%8C%EC%A1%B0%EC%82%AC%EB%B6%84%EC%82%AC-2%EA%B8%89-%ED%95%84%EA%B8%B0

Learn the core functions of SPSS in a short period to pass the Social Survey Analyst Level 2 practical exam. Master SPSS know-how along with problem-solving techniques—ranging from data entry to descriptive statistics, cross-tabulation, t-tests, ANOVA, and correlation/regression analysis—to perfectly prepare for the practical exam.