Statistical Analysis for Big Data
Description
Core statistical concepts and methods essential for analyzing large datasets, including descriptive statistics, probability theory, hypothesis testing, and regression analysis.
Learning Objectives
Students will master fundamental statistical concepts and apply them to big data contexts, perform descriptive and inferential statistical analysis, understand probability distributions, conduct hypothesis testing, implement regression analysis, and apply statistical methods for data quality assessment and validation in large-scale datasets.
Topics (10)
Fundamental probability concepts, probability distributions, and their applications in big data analytics and machine learning.
Methods for making inferences about populations from sample data, including hypothesis formulation, testing procedures, and interpretation of results.
Statistical sampling methods adapted for big data environments to ensure representative samples while managing computational complexity.
Statistical and mathematical techniques for reducing the number of variables in datasets while preserving important information and patterns.
Statistical methods for analyzing time-ordered data to identify trends, seasonal patterns, and make predictions in big data environments.
Bayesian approach to statistical analysis providing probabilistic frameworks for inference and decision-making under uncertainty in big data contexts.
Statistical methods for monitoring data quality, detecting anomalies, and controlling analytical processes in big data environments.
Sophisticated statistical methods designed to handle the complexity, volume, and variety challenges inherent in big data analytics.
Application of descriptive statistical measures including mean, median, mode, variance, standard deviation, skewness, and kurtosis to big data contexts.
Statistical methods for analyzing relationships between variables, including correlation coefficients, linear regression, multiple regression, and model validation.