Course Overview
Data Science Theory provides the conceptual foundation for reasoning with data, models, uncertainty, inference, experimentation, and interpretation. Learners study probability, statistics, regression, methodology, experimental design, bias, and ethical data use as the theoretical basis for responsible analytical work.
Data Science Theory Topics
-
Probability and Statistics
Focused study of probability and statistics as a core topic within data science theory, emphasizing conceptual understanding, technical vocabulary, reasoning patterns, and links to practical computing systems.
-
Statistical Inference
Focused study of statistical inference as a core topic within data science theory, emphasizing conceptual understanding, technical vocabulary, reasoning patterns, and links to practical computing systems.
-
Regression and Modeling
Focused study of regression and modeling as a core topic within data science theory, emphasizing conceptual understanding, technical vocabulary, reasoning patterns, and links to practical computing systems.
-
Data Analysis Methodology
Focused study of data analysis methodology as a core topic within data science theory, emphasizing conceptual understanding, technical vocabulary, reasoning patterns, and links to practical computing systems.
-
Experimental Design
Focused study of experimental design as a core topic within data science theory, emphasizing conceptual understanding, technical vocabulary, reasoning patterns, and links to practical computing systems.
-
Data Ethics and Bias
Focused study of data ethics and bias as a core topic within data science theory, emphasizing conceptual understanding, technical vocabulary, reasoning patterns, and links to practical computing systems.