Topic 3

Lesson 3. Basic Data Analysis Methods and Tools

Objective: formation of students' complex of theoretical knowledge, critical analytical thinking and practical skills in the field of application of descriptive statistics methods, aggregation, sampling and filtering of data, as well as making informed management decisions based on the concept of Data-Driven Decision Making (DDDM). The student must master the fundamental mathematical concepts of descriptive statistics, the essence of measures of central tendency (arithmetic mean, median, mode) and measures of variability (range, variance, standard deviation), as well as comprehend the structure of the data-driven decision-making cycle and the nature of cognitive interpretation traps. In the learning process, special attention is paid to understanding and comparative analysis of the sensitivity of statistical indicators to outliers (in particular, the robustness of the median), mastering the algorithmic pattern Split-Apply-Combine (split - apply - combine) and studying the architecture and functionality of pivot tables (Pivot Tables). Students should learn to practically apply the tools of analytical spatial and statistical sampling (Sampling), perform simple and multi-criteria logical filtering using AND, OR, NOT, Regex operators and configure multi-level hierarchical sorting of arrays. An important analytical and assessment result is the formation of the ability to differentiate correlation from causation (Correlation vs Causation), identify and eliminate such typical errors of interpretation as Simpson's paradox, survivorship bias, confirmation bias and neglect of the basic scale, which provides a high culture of interpretation of facts and allows you to formulate reliable conclusions for the optimization of engineering and business processes.