Skills

First Three Projects Shipped (Beginner to Public App)

5 milestones · 16 weeks

Write, run, and ship your first Python programs — from a terminal hello-world to a deployed web app that real strangers can use.

Master Python for Real Projects

5 milestones · 12 weeks

Write Pythonic code, work with APIs and external libraries, build and test a data pipeline, and publish a real Python package on PyPI.

Earn Databricks Certified Data Engineer Associate

0 milestones · 10 weeks

Pass the Databricks Certified Data Engineer Associate exam. Covers five areas: Databricks Lakehouse Platform, ELT with Spark SQL and Python, Incremental Data Processing with Delta Lake, Production Pipelines, and Data Governance. The foundational certification for data engineers working on the Lakehouse architecture.

Build Your First Game in Python

3 milestones · 8 weeks

Write a game in Python that someone else can actually play — a quiz, a word game, a simple platformer, or anything with a win condition. Use Pygame Zero (free, beginner-friendly) or follow the CS50 Games course (free from Harvard). Proof is a video of the game running or a link to the code with clear run instructions — the game must work, not just compile.

Quantitative Social Research

0 milestones · 10 weeks

Conduct a real survey study (minimum 40 respondents) or secondary data analysis using a named real dataset (BHPS, GSS, ANES, NLSY, or equivalent). Document your methodology, produce your analysis output (SPSS, R, Stata, or Python), and write a sociologically-framed interpretation of your results. Proof is your methodology record, analysis output, and written interpretation plus a documented review by a sociologist or social statistician who challenges your operationalisation of key variables — specifically, how your concepts were measured and whether the chosen dataset is representative for your question. Your written response is a required part of the proof.

Computational Physics Modelling

0 milestones · 10 weeks

Build a real computational physics model or simulation using numerical methods — finite difference, Monte Carlo, molecular dynamics, or numerical integration of differential equations — implemented in code (Python with NumPy/SciPy, Julia, or equivalent). The proof is the submitted code (must be runnable as-is with documented dependencies), parameters with rationale, numerical outputs, visualisation of results, and a written validation section comparing the model output to an analytical solution or published reference value where one exists. Computational physics modelling is fully accessible — all tools are free and open source. Reviewed by a physicist who runs the submitted code independently to confirm it produces the documented outputs; the reviewer also asks you to modify a specific parameter and predict the outcome, then verify it during the review session.

Power Systems Analysis

0 milestones · 8 weeks

Perform a power systems analysis for a defined electrical network — either a real distribution network (using public utility data) or a documented study network. The analysis must cover: a single-line diagram of the network with all bus voltages, line impedances, and load specifications, a load flow analysis calculating real and reactive power flows on all branches and bus voltages under normal operating conditions, a fault analysis calculating fault current for at least one three-phase bolted fault at a specified bus, and a protection coordination analysis identifying whether the existing protection devices (or proposed devices) will correctly isolate the fault. Preferred proof: analysis of a real utility or industrial distribution network using professional software. Accessible alternative: pandapower (Python library, free and open-source) or OpenDSS (free, from EPRI) applied to a published IEEE test network (IEEE 9-bus, 14-bus, or 30-bus systems — all publicly available). Hand calculation for a simplified 3-bus network is also accepted with documented methodology. Proof artifacts: the single-line diagram and network specification (design artifact) and the load flow and fault analysis results (analysis artifact). Verification: a power systems engineer reviews the fault analysis — 'at this fault level, does the upstream breaker clear within its rated interrupting time?' — requiring you to reason through your own protection coordination.

Signal Processing Application

0 milestones · 6 weeks

Design and implement a signal processing application for a defined signal and task: filter design (low-pass, high-pass, band-pass, or band-stop), spectral analysis, noise reduction, or feature extraction. The application must be non-trivial — a real signal with real noise characteristics, not a textbook ideal case. Required documentation: signal specification (frequency content, noise type, SNR, sample rate), filter or algorithm specification (design method, order, cutoff frequencies, ripple), frequency response plot of the designed filter or algorithm, performance validation comparing before and after processing with quantitative metrics (SNR improvement, stopband attenuation achieved), and a documented sensitivity analysis testing at least one design parameter. Preferred proof: a real signal from physical measurement hardware. Accessible alternative: Python with scipy.signal and numpy (free), MATLAB Online free tier, or GNU Octave (free) applied to publicly available signal datasets (PhysioNet ECG data, NOAA seismic data, urban noise datasets). Proof artifacts: the filter or algorithm specification (design artifact) and the frequency response and performance validation plots (analysis artifact). Verification: an electrical or signal processing engineer reviews the performance analysis — 'this filter removes the noise but what useful signal components are also attenuated?' — requiring you to quantify the trade-off.