This course serves as the first part of a two-course sequence designed to build strong statistical thinking for learners in business, engineering, and the social sciences. It introduces the essential foundations of data analysis, equipping learners with the ability to understand, describe, and model real-world uncertainty. Beginning with data types and measurement scales, the course guides learners through effective methods for organizing, summarizing, and visualizing information using graphs, tables, and descriptive statistical measures.
Learners gain a deep understanding of variability, distributional characteristics, and standardized measures such as skewness and kurtosis. Building on this, the course develops core probability concepts, including random experiments, events, axioms, conditional probability, independence, and Bayes’ theorem. These topics provide a conceptual bridge to the study of random variables.
The course then introduces discrete and continuous random variables, their probability distributions, and their applications in real-life decision-making. Key distributions—including Binomial, Poisson, Uniform, Normal, and Exponential—are covered both conceptually and computationally using MS-EXCEL, ensuring that learners acquire hands-on skills in calculating probabilities, percentiles, and distribution parameters.
This course lays the groundwork for Part II of the sequence, which covers confidence interval estimation, hypothesis testing, analysis of variance (ANOVA), and regression techniques. Together, the two courses provide a complete introduction to statistical methods for decision-making and data-driven problem solving.
