STAT 364 - Scalable Statistical Inference for Big Data with Applications Learning Course

[STAT 364 - Scalable Statistical Inference for Big Data with Applications Learning Course] STAT 364 - Scalable Statistical Inference for Big Data with Applications STAT 364 explores scalable statistical methods and computational techniques for analyzing large-scale datasets, focusing on applications in big data analytics, machine learning, and scientific research. The course covers topics such as distributed computing, parallel algorithms, stochastic optimization, and statistical inference in the context of massive datasets. Emphasis is placed on understanding the challenges of big data analytics, including data preprocessing, model fitting, and hypothesis testing using scalable statistical approaches. Through hands-on projects and case studies, students learn how to implement scalable algorithms, leverage cloud computing platforms, and apply statistical techniques to extract meaningful insights from complex and high-dimensional data sources. The course equips students with advanced skills in scalable statistical inference, computational efficiency, and data-driven decision-making, preparing them for careers in data science, computational statistics, and research-intensive industries.This Learning Course is a platform that helps students to study and learn. The platform provides all the required material for the students to study. The material includes Study guide, Exam Questions & Answers, Study Notes and other resources that are useful for the students. It also gives information about the upcoming exams so that students can prepare themselves accordingly.