FREE Reverse Engineering Self-Study Course HERE

Today's Tutorial [January 22, 2026]

Lesson 87: ARM-32 Course 2 (Part 22 – Hacking Character Variables)

This tutorial will discuss hacking character variables.

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Reverse Engineering Data

Master the art of reverse engineering data and discover how to deconstruct, analyze, and rebuild datasets for deeper understanding and actionable insights.

Download & Install Conda HERE

Create Conda Environment

conda create --name prod anaconda
conda activate prod
conda update --all

00-CLT: Central Limit Theorem

This chapter covers the Central Limit Theorem within Data Science.

-> Click HERE to read the FREE Jupyter Notebook.

01-EDA: Exploratory Data Analysis

This chapter covers an extensive exploratory data analysis of the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

02-Clustering: Clustering

This chapter covers an extensive KMeans and PCA clustering utilizing the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

03-SLR-LR: Simple Linear Regression - Linear Relationship

This chapter covers simple Linear Regression utilizing a single linear feature within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

04-SLR-NLR: Simple Linear Regression - Non-Linear Relationship

This chapter covers simple Linear Regression utilizing a single non-linear feature within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

05-SLR-FLF: Simple Linear Regression - Fitting Linear Feature

This chapter covers simple Linear Regression fitting a single linear feature within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

06-SLR-FNLF: Simple Linear Regression - Fitting Non-Linear Feature

This chapter covers simple Linear Regression fitting a single non-linear feature within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

07-SLR-FNLF: Simple Linear Regression - Fitting Linear Feature Predictions

This chapter covers simple Linear Regression fitting a single linear feature prediction within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

08-SLR-FNLF: Simple Linear Regression - Fitting Non-Linear Feature Predictions

This chapter covers simple Linear Regression fitting a single non-linear feature prediction within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

09-SLR-MLFP: Simple Linear Regression - Measuring Linear Feature Performance

This chapter covers simple Linear Regression measuring a single non-linear feature model's performance within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

10-SLR-FMM: Simple Linear Regression - Fitting Multiple Models

This chapter covers simple Linear Regression fitting multiple models for a single linear feature within the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

11-MLR-AF: Multiple Linear Regression - Additive Features

This chapter covers simple Linear Regression additive features and how they shift the best fit line up or down.

-> Click HERE to read the FREE Jupyter Notebook.

12-MLR-AF: Multiple Linear Regression - Interactive Features

This chapter covers multiple Linear Regression additive features and how they rotate the best fit line and change the slope.

-> Click HERE to read the FREE Jupyter Notebook.

13-LR: Linear Regression

This chapter covers Linear Regression by comprehensively modeling with Statsmodels and creating a complete inference engine utilizing the Penguins dataset.

-> Click HERE to read the FREE Jupyter Notebook.

License

Apache License, Version 2.0