1. Diagnostic Profile
220 hrs
Total Curriculum
22 Wks
Estimated Duration
8 Nodes
Core Milestones
Feb 2027
Completion Target
Math/Foundations
Python/Data
Classical ML
Deep Learning
Specialization
Python Core & Vectorized Computing
Estimated Effort: 30 Hours
Master Python 3 fundamentals, list comprehensions, OOP principles, NumPy vector operations, and Pandas dataframe manipulation.
Core Concepts:
Python control flow, NumPy broadcasting, Matrix dot products, Pandas aggregation, Matplotlib visualization.
⚠️ Common Pitfall: Falling into tutorial hell without writing independent scripts. Avoid copy-pasting tutorials without typing every line manually.
🛠️ Milestone Project & Code Spec
Required Prerequisite
Project: Vectorized Data Pipeline & EDA Tool
Build a local CLI tool that ingests CSV datasets, detects missing features, performs z-score normalization with NumPy, and plots class distributions.
import numpy as np
import pandas as pd
def normalize_features(X: np.ndarray) -> np.ndarray:
"""Vectorized z-score normalization"""
mean = np.mean(X, axis=0)
std = np.std(X, axis=0) + 1e-8
return (X - mean) / std
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