This reading walks learners through end-to-end engineering data workflows—cleaning raw sensor data, selecting and applying numerical methods, and interpreting results—serving as a contextual overview of how individual techniques combine into realistic engineering pipelines.
Topics
This module covers 7 topics. Open a topic to see its subtopics.
Module Content
9 items.
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This reading introduces the SciPy library within the Python scientific computing ecosystem, covering its major submodules, their engineering applications (stress analysis, curve fitting, differential equations), and how learners should set up and navigate SciPy for numerical work.
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This reading explains the concept of root finding and demonstrates how to use scipy.optimize tools (such as brentq and fsolve) to locate function zeros, covering method selection, bracketing, convergence, and practical engineering use cases where analytical solutions are unavailable.
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This reading covers numerical integration theory and practice using scipy.integrate, addressing both formula-based integration (quad) and data-based integration (trapezoid, Simpson's rule), with guidance on when and how to apply each tool to real engineering problems.
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This reading teaches polynomial and linear curve fitting using numpy.polyfit, explaining how to model noisy experimental data with best-fit lines or polynomials, interpret coefficients, assess fit quality with R², and apply the workflow to engineering datasets.
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This reading covers nonlinear curve fitting using scipy.optimize.curve_fit, explaining how to define custom model functions (e.g., exponential, power-law), estimate parameters, interpret uncertainty via covariance, and apply the technique to engineering relationships that linear methods cannot capture.
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This reading explains residuals analysis as a method for evaluating curve-fit quality, teaching learners how to compute, plot, and interpret residuals to detect model inadequacy, bias, or violations of assumptions after fitting linear, polynomial, or nonlinear models.
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This reading walks learners through end-to-end engineering data workflows—cleaning raw sensor data, selecting and applying numerical methods, and interpreting results—serving as a contextual capstone showing how individual numerical techniques integrate into complete engineering analysis pipelines.
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Learners drag SciPy and NumPy function names to their matching input/output descriptions to reinforce recognition of key numerical methods functions, serving as a self-check activity within the module on numerical methods and curve fitting.