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Free & open · CC BY-SA 4.0

Data Analysis
for Engineers

A project-based course in Python for data analysis — Python foundations, pandas, NumPy/SciPy, plotting, and statistics, ending in six applied case studies drawn from Philippine engineering practice. Built for engineers who already have a technical foundation and want job-ready data skills on top of it.

Start Module 1 6 modules · 63 sections · runs on your machine
notebook · module 6 · case study 6.1run in colab →
from scipy import stats
# annual-max 1-day rainfall, mm — 30-yr record
loc, scale = stats.gumbel_r.fit(rain)

# design-storm depth by return period
for T in (2, 10, 50, 100):
    d = stats.gumbel_r.ppf(1 - 1/T, loc, scale)
    print(f"{T:>3}-yr {d:6.1f} mm")
  2-yr   203.0 mm
 10-yr   300.8 mm
 50-yr   386.5 mm
100-yr   422.8 mm  ← design basis

The curriculum

  1. M1Python Foundations for EngineersPython language fundamentals  ·  project: Equipment Energy & Cost Analyzer10 §
  2. M2Data Manipulation with PandasTabular data manipulation  ·  project: Sector Energy Demand Summary Report12 §
  3. M3Numerical Computing with NumPy & SciPyNumerical computing & scientific methods  ·  project: Extreme Load Analysis (Gumbel Method)11 §
  4. M4Data VisualizationPlotting & charting  ·  project: Multi-Panel Facility Energy Report Figure10 §
  5. M5Statistical Analysis & Introduction to Machine LearningStatistics & intro ML  ·  project: Facility Energy Regression Model12 §
  6. M6Philippine Engineering ApplicationsApplied case studies & capstone  ·  project: Capstone Project (3 tracks)8 §

└ modules 1–5 come before module 6 · finish each Knowledge Check before moving on

Want it guided?

The optional coaching cohort adds Docker, SQL/PostgreSQL, Metabase, and Git on top of these modules — plus two self-sourced capstone projects, live sessions twice a week for five weeks, a private community, and a certificate.

See the coaching package