This site contains created and curated resources and review notebooks to support you on your journey through Codeup’s Data Science program and beyond. This site will grow with you throughout the course, so bookmark it and keep it handy for support!
I highly suggest making your own Github Page as a portfolio that shows off your own work throughout the program. It would be a great link to share on your LinkedIn and drop on your resume for potential employers to check out!
Resources
Want to make your own Github Page like this one as a personal portfolio? Check out this resource map I created that contains everything you need to get started. This will get your site up and running in less than half an hour! When you’re ready to make your markdown files and notebooks beautiful, check out the beginner Markdown & HTML Tutorial below. Want to get even MORE creative? Check out the materials housed on the Video Tutorial Resources page below.
This tutorial will show you how to use lots of exciting markdown and html code in your README files as well as your Jupyter Notebooks! Documentation is everything in making a Jupyter Notebook readable, so learning to use some Markdown and HTML to make it look great is worth your while.
This page houses the links to helpful video tutorials on a variety of topics. I will add new Lunch ‘n’ Learn videos here as you all volunteer to give them, too!
This page houses helpful resources for you to use when preparing yourself for interviewing.
This page is a collection of resources for putting together and giving great presentations.
Data Science Tools
SQL Review Notebooks and Resources
This page houses helpful resources for you to use when getting into SQL for data analysis.
Python and Pandas Review Notebooks and Resources
This page houses helpful resources for you to use when learning python and pandas for data analysis.
PySpark Review Notebooks and Resources
This page houses helpful resources for you to use when learning and using PySpark.
Stats Review Notebooks and Resources
This page houses helpful resources for you to use when learning and reviewing statistic for data science.
Want an overview of REST APIs and using the python
requestsmodule? This notebook provides just that and lots of extra resources to play with througout.
Time Series Review - Acquire and Prepare
This page revisits acquiring data by hitting an API in less depth than the API Review Notebook but also moves into some preparing and light exploring of data with a DateTimeIndex.
BeautifulSoup Data Acquisition Review
This notebook walks you through acquiring data by using python’s webscraping library, BeautifulSoup, and the requests library. I also create functions that make the process repeatable.
Machine Learning
Classification Project Review Notebook
This page walks through the data science pipeline for a simple classification project. Lots of handy code snippets and explanations.
Regression Project Overview Notebook
This page walks through the data science pipeline for a simple regression project. It is much less detailed than the Classification Review notebook, but it contains helpful explanations and code snippets to work your way through a ML Regression Project.
Links to Workshops and Extras
This page houses helpful resources for you to use when getting into python and pandas including interactive Google Colab notebooks you can make a copy of for yourself and edit as your own notebook.
Intro to ML Workshop - Classification
This page houses helpful resources for you to use when getting into machine learning. The workshop contains basic python and pandas for data analysis, Google Colab notebooks you can copy and edit, and other relevant resources. The Canva presentation link is here.
She Code Connect Python Workshop
This links to a Google Colab Notebook for an Intro to Python Workshop I created and delivered for She Code Connect. If you or someone you know are interested in having fun learning some of the very basics of python, the interactive notebook lives at the link above, and the video of me walking through the notebook lives here.