How jimmyu2foru18/algorithms-programming-technique-6010 works
This repository is a collection of Python and data-analysis course materials: lecture notebooks, staged assignments, and datasets for practice. A learner’s workflow is to study lecture material, work through assignment notebooks and exercises, and use the supplied data in the relevant analysis activities. The tree supports these content groupings, but there is no README or sampled notebook source, so relationships between particular notebooks and datasets are not asserted.
Main entry points: Day1.ipynb Day 1 · Day2.ipynb Day 2 · Day3.ipynb Day 3 · Day4.ipynb Day 4
Components 18
Class Lectures
Assignments
- Assignment 1
Assignment 1 - Assignment 2
Assignment 2 - Assignment 3
Assignment 3 - Assignment 4
Assignment 4
NumPy Learning
- NumPy Intro
Intro_to_numpy.ipynb - NumPy Extra
Numpy Extra Learning/Numpy_ed.ipynb - Cereal Exercise
Assignment 3/Part 2 Numpy/CerealNumPy.ipynb
Practice Data
- Cereal Data
Assignment 3/Part 2 Numpy/cereal.csv - Baseball Data
Assignment 4/02 Baseball Databank/Data - Stock Data
Assignment 4/01 Microsoft stock data Example/2026-q1.csv
Other components
- Learner
- Problem Notebook
Problems.ipynb
Connections 14
- Learner → Day 1: opens
- Learner → Day 2: opens
- Learner → Day 3: opens
- Learner → Day 4: opens
- Learner → Day 5: opens
- Learner → Day 6: opens
- Learner → Assignment 1: works through
- Learner → Assignment 2: works through
- Learner → Assignment 3: works through
- Learner → Assignment 4: works through
- Learner → NumPy Intro: studies
- Learner → NumPy Extra: studies
- Learner → Cereal Exercise: works through
- Learner → Problem Notebook: practices with