/USD-MSADS

Primary LanguageJupyter NotebookMIT LicenseMIT

Masters of Science in Applied Data Science - University of San Diego

This repository is where I have files and references that I can share for anyone who finds them useful.

For the most part, these are some of the notes that I refer to on a regular basis.

Courses

Spring 2024

  • ADS 500A - Probability and Statistics for Data Science
  • ADS 500B - Data Science Programming

Summer 2024

  • ADS 501 - Foundations of Data Science and Data Ethics
  • ADS 502 - Applied Data Mining

Fall 2024

  • ADS 505 - Applied Data Science for Business
  • ADS 506 - Applied Time Series Analysis

Spring 2025

  • ADS 507 - Practical Data Engineering
  • ADS 508 - Data Science with Cloud Computing

Summer 2025

  • ADS 503 - Applied Predictive Modeling
  • ADS 504 - Machine Learning and Deep Learning for Data Science

Fall 2025

  • ADS 509 - Applied Text Mining
  • ADS 599 - Capstone Project

Custom functions

These are a few functions I wrote to help with some of the coding that we have to do repeatedly.

These functions have been saved in my jcds repository.

The easiest way to import these functions is to first install the httpimport module.

Then, import the jcds module:

import httpimport

with httpimport.github_repo('junclemente', 'jcds', ref='master'):
    import jcds
I am no longer using this method and opting to import them from my repository but keeping this here for reference.

The functions from this notebook can be imported into another notebook by using the %run command in a code cell:

%run <path to notebook>

This will make the functions useable in the notebook.

Environments Creating a conda environment from a yml file

To create an environment with a specific name:

conda env create -n ENVNAME -f ENVNAME.yml

To create an environment using the name in the YAML file:

conda env create -f ENVNAME.yml

Update environment:

conda env update -f local.yml --prune