Beginner
Data Science with Python
From zero to insights. A comprehensive beginner's guide to setting up your environment, cleaning messy datasets, and uncovering stories through data visualization.
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Deconstructing Digital Architecture
From CPU pipelines to financial modeling. Exploring the intersection of hardware logic, Python-driven data science, and quantitative engineering.
01 @dataclass 02 class Instruction:
03 op: str 04
addr: int 05 06 // Simulate CPU cycle
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Featured Insights
Deep-architecture blueprints and hand-picked guides to mastering complex distributed systems.
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Intermediate
Finding P values and false hope: A Guide to Risk and Quant Finance
From simple bets to complex bond portfolios. Explore the mathematical structures of uncertainty, debt mechanics, and the Python tools used to model them.
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Intermediate
From Transistor to System: A Friendly Guide to Computer Architecture
Demystifying the ghost in the machine. A journey from raw silicon and jumping electrons to the sophisticated multicore systems that power our modern world.
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Commit History
Main branch / technical logs
#4aa2821 @ Intermediate Apr 12, 2026
The Random Walk: From Coin Flips to Stochastic Calculus
Discover how a simple coin flip evolves into the mathematics powering modern derivative pricing. From Binomial Trees to the Wiener Process and the Heat Equation; we handle all in Python.
$ view source --file finding-p-values-and-false-hope/9-random-walk.md _
#7c05fec @ Advanced Mar 8, 2026
Is Sweet Bonanza 1000 Rigged? A Statistical Analysis of 60,000 Spins
We analyze 60,000 spins of Sweet Bonanza 1000 using Python, Bootstrap CI, and Hypothesis Testing to see if the 96.53% RTP holds up against extreme volatility.
$ view source --file expected-riches-and-expensive-lessons/2-sweet-bonanza-1000.md _
#73a6af1 @ Intermediate Mar 1, 2026
Value at Risk (VaR) vs. CVaR: A Practical Guide for Investors
Stop using abstract volatility. Learn how to calculate Value at Risk (VaR) and Conditional VaR (CVaR) using Python to understand your actual downside potential.
$ view source --file finding-p-values-and-false-hope/8-var.md _
#3ed9ce9 @ Advanced Feb 22, 2026
A Python-Based Statistical Audit of Gates of Olympus
Using Python, statistics, and 40,000 spins to analyze RTP, variance, bonus frequency, and volatility in Gates of Olympus through hypothesis testing and simulation.
$ view source --file expected-riches-and-expensive-lessons/1-gates-of-olympus.md _
#6beed8e @ Intermediate Feb 15, 2026
Stock Options 101: A Guide to Calls, Puts, and Pricing
Unlock the basics of stock options. Learn the difference between calls and puts, American vs. European styles, and how Greeks like Delta and Theta impact pricing.
$ view source --file finding-p-values-and-false-hope/7-options.md _
#25fa5b4 @ Beginner Feb 8, 2026
The Visual Storyteller: Mastering EDA with Seaborn and Python
Turn raw data into actionable insights. Learn how to use Seaborn and Matplotlib for Exploratory Data Analysis (EDA), including heatmaps, Q-Q plots, and distribution analysis.
$ view source --file data-science-with-python/3-visualising.md _
#690a342 @ Intermediate Feb 1, 2026
Portfolio Allocation: The Math Behind the Efficient Frontier
Master the quantitative core of Modern Portfolio Theory. Learn how to use Markowitz optimization and Python to build an efficient frontier for asset allocation.
$ view source --file finding-p-values-and-false-hope/6-allocation.md _
#7778373 @ Beginner Jan 25, 2026
The Cleaning Machine: 6 Steps to Trustworthy Data with Pandas
Master the 6-step framework for data wrangling. Learn to handle missing values, remove outliers using IQR, and validate data quality using Python and Pandas.
$ view source --file data-science-with-python/2-cleaning.md _