Engineering Data Analysis
Descriptive statistics, probabilistic modeling, hypothesis validation, exploratory distributions, and outlier mining.
Engineering Data Analysis (CS303): Complete Digital Textbook & Labs
Comprehensive curriculum-aligned chapters, embedded interactive simulators, deep micro-concepts, and university exam/viva solutions.
University Units & Micro-Concepts (5 Units)
Click on any Unit below to reveal its Topics, Interactive Labs, and Deep Theory content.
The Mean Lies & Tukey's IQR Outlier Laboratory
EDA definition, steps, importance, data types (numerical, categorical, continuous, discrete), distributions, and Python for EDA (Jupyter, Pandas basics).
The EDA Reasoning Loop, Measurement Scales & Tidy Data
Understand the disciplined 6-step EDA cycle, distinguish measurement scales (nominal, ordinal, interval, ratio), understand wide vs long tidy data layouts, and implement first-pass Pandas auditing.
Introduction to EDA: Philosophy, Data Structures & Pandas Toolkit
John Tukey's exploratory data philosophy, structured vs unstructured data, Pandas Series and DataFrames, indexing, vectorized filtering, and basic diagnostic profiles.