[PUBLISHER] Merge #22

* PUSH NOTE : portfolio cleanup.md

* PUSH NOTE : copart internship.md

docs/copart internship.md

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+---
+share: true
+category: obsidian
+---
+# Data Science at Copart
+The company Copart is right next to my house. Right now, as of 2 days ago, they have a position open for a data scientist internship right now! I meet all of the requirements, and have the portfolio to support it. Now I just need to put all that together for a
+
+## The Job
+
+> Copart is looking for a data scientist who will work closely with IT and various other departments to drive insight into data and deliver machine learning solutions to improve Copart's operations. This data scientist will also design/manipulate large scale data sets from a multitude of sources, work to operationalize and integrate machine learning solutions into Copart's current products and visualize and report on findings and results to provide insight to the organization.  
+
+**Job Duties**
+
+- Develop new predictive models using advanced techniques
+- Apply critical thinking to ensure data integrity and quality control is applied to each dataset, model and other analysis prior to presenting with internal customers
+- Coordinate with different functional teams to operationalize, and monitor machine learning solutions
+- Apply statistical methodologies such as cluster and regression analysis, if necessary.
+- Act as a proponent of data science/analytics to senior leadership and others by being able to explain the benefits of machine learning, and other techniques.
+- 6 Months of experience (relevant academic internships & projects can be considered in lieu of professional experience) with machine learning, statistical modeling, and data mining techniques.
+- Bachelor or Master's degree in highly quantitative field (computer science, mathematics, machine learning, statistics) or equivalent experience
+- Proficiency in either R or Python
+- Proficiency in data sourcing/manipulation in SQL
+- Bachelor or Master's degree in highly quantitative field (computer science, mathematics, machine learning, statistics) or equivalent experience
+- Experience applying various machine learning techniques, specifically neural networks and gradient boosted machines, and understanding the key parameters that affect their performance
+- Strong data visualization skills using open source tools (plotly, ggplot2, shiny)
+- Experience with both supervised and unsupervised modeling techniques
+
+This is, simply, exactly what I have experience in. But I haven't done anything in the field recently.
+
+## What I Want to Do
+I am terrified of applying to jobs. So I am going to compromise between not applying and applying right now. I'd say I have to prep my past work on the subject
+- [ ] [[portfolio cleanup|Combine my work]] from Mazidi's NLP and ML classes into one portfolio in markdown
+- [ ] Host it on my URL with a home page summary that links to my linkedin and github
+- [ ] Clean up my github
+- [ ] Clean up my Resume to be focused on Data Science
+- [ ] Review focus points on the list of requirements:
+	- [ ] Python basic programming questions
+	- [ ] Profeciency with ggplot2 and pandas, matplot, pytorch, and scikit
+	- [ ] Mild R review
+	- [ ] Neural network review, and how to use dimensionality reduction and gradient descent to better ML models
+
+
+

docs/portfolio cleanup.md

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+---
+share: true
+category: obsidian
+---
+# Portfolio Cleanup
+Like dusting off books on a shelf, there is knowledge waiting to be shared
+
+## MKDocs and Obsidian Markdown
+I'm using Mara Li's excellent [Obsidian Publish](https://github.com/ObsidianPublisher/obsidian-github-publisher) plugin to push notes from my obsidian vault to a github pages repo. I had tried this before, but only in testing. Now it is time to take existing data science projects I have and push to this static site. This means I just ripped the pre-existing notes off the website and restarted! Spring cleaning feels great
+
+## What am I making?
+The only real decision is structure, of which there are a couple things to note:
+- Obsidian Notes (like this basic one!) can be pushed to an obsidian sub directory to act as an archive
+- The ML and NLP portfolios can have their own folders, with home pages for each section to act as summaries built from what I have already written
+- The homepage of the size can just be an introduction to me, and service as the about page.
+
+## Aesthetics
+For now! There shouldn't be any!
+
+> [!note]
+> Although I realized now I would like to set up automatic backlinks... (and to test [callouts](https://docs.readme.com/rdmd/docs/callouts))