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https://github.com/9oelm/rdsa
(re)studying data structures and algorithms. Yeah i know, it's that time of the season again.
https://github.com/9oelm/rdsa
algorithms data-structures data-structures-and-algorithms interview jupyter-notebook
Last synced: 7 days ago
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(re)studying data structures and algorithms. Yeah i know, it's that time of the season again.
- Host: GitHub
- URL: https://github.com/9oelm/rdsa
- Owner: 9oelM
- License: mit
- Created: 2018-12-12T17:09:23.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-10-08T14:45:38.000Z (about 2 years ago)
- Last Synced: 2024-10-29T12:56:28.300Z (about 2 months ago)
- Topics: algorithms, data-structures, data-structures-and-algorithms, interview, jupyter-notebook
- Language: Jupyter Notebook
- Homepage:
- Size: 8.88 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# rdsa
(re)studying data structures and algorithms# What is this repo for
Preparation for coding interviews with concentration on data structures and algorithms.## How does this repo work
This repo uses jupyter notebook. The contents include a good mix of coursera/edx lectures and coding quizzes from the Internet.## Viewing from Github
Github supports reading jupyter notebook on web, but image files on the web can only be read on a local notebook launch.
If you want to view the images (mostly PPT slides), download a clone and launch the notebook.## Contents
### [Algorithmic toolbox](https://www.coursera.org/learn/algorithmic-toolbox?specialization=data-structures-algorithms)
* [Week 1: Programming Challenges](https://github.com/9oelM/rdsa/tree/master/1-algorithmic-toolbox/week-1-programming-challenges)
* [Week 2: Algorithmic warm up](https://github.com/9oelM/rdsa/tree/master/1-algorithmic-toolbox/week-2-algorithmic-warm-up)
* [Week 3: Greedy algorithms](https://github.com/9oelM/rdsa/tree/master/1-algorithmic-toolbox/week-3-greedy-algorithms)
* [Week 4: Divide and Conquer](https://github.com/9oelM/rdsa/tree/master/1-algorithmic-toolbox/week-4-divide-and-conquer)
* [Week 5: Dynamic programming 1](https://github.com/9oelM/rdsa/tree/master/1-algorithmic-toolbox/week-5-dynamic-programming-1)
* [Week 6: Dynamic programming 2](https://github.com/9oelM/rdsa/tree/master/1-algorithmic-toolbox/week-6-dynamic-programming-2)### [Data structures: an active learning approach](https://stepik.org/course/579/syllabus)
* [1. Time complexity](https://github.com/9oelM/rdsa/blob/master/2-Data-Structures-An-Active-Learning-Approach/1-time-complexity.ipynb)
* [2. Computational complexity](https://github.com/9oelM/rdsa/blob/master/2-Data-Structures-An-Active-Learning-Approach/2-computational-complexity.ipynb)
* [3. Fundamentals of cpp](https://github.com/9oelM/rdsa/blob/master/2-Data-Structures-An-Active-Learning-Approach/3-fundamentals-of-cpp.ipynb)# Getting started on your computer
* clone this repo
```bash
git clone https://github.com/9oelM/rdsa.gitcd rdsa
```* [make sure you are up to date beforehand](https://askubuntu.com/questions/94102/what-is-the-difference-between-apt-get-update-and-upgrade)
```bash
sudo apt-get updatesudo apt-get upgrade
```* install the packages needed
```bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.shbash Miniconda3-latest-Linux-x86_64.sh
```(read from the `environment.yml`)
```bash
conda env create --file environment.ymlconda activate notebook
```(manual)
```bash
conda create -n notebookconda activate notebook
conda install jupyter
conda install xeus-cling -c conda-forge # optional: for c++ kernel
```* start the notebook
```bash
jupyter notebook # localjupyter notebook --ip=0.0.0.0 --port=8080 --no-browser --allow-root # on cloud
```# Nice help & resources
* [Jupyter docs](https://jupyter.readthedocs.io/en/latest/index.html#)
* [Jupyter notebook docs](https://jupyter-notebook.readthedocs.io/en/latest/index.html)
* [Python time complexity](https://wiki.python.org/moin/TimeComplexity)