Teaching and Learning Resources / Workflows

Here is a list of things I think are important and want to eventually learn or write out for other to learn.

Teaching Ideas

• Teach how to create datasets with small scale experiments
• Run analysis using R or Pandas in Python
• Put analysis in report via RBlogdown or RBookdown
• Need own servers, small physical lab that generates data, data visualization team, app/comp sci team

Stuff to Learn and Put into Notebooks Later

• Branch and Bound
• Common MIP Cuts
• Q Learning
• SARSA
• R
  • Tidyverse
  • GGPlot
  • Plotly
  • GGAnimate and other Animation packages
  • Gurobi
  • Machine Learning Workflows
  • Functions
  • Shiny – Difference between Plotly and Shiny?
• Python
  • Pandas
  • Plotly
  • Machine Learning
  • Fucntions
  • Matlabplot/Seaborn
• Ask Bard difference between decomp methods and CP, MCP and Bilevel optimization
• Raspberry Pi Machines
• Basic Hacking

New Artistic Skills

• Learn Piano
• Introduction to Painting
• Guitar after Piano

Pages of Learned Info

• Basic Engineering
  • -Calculus
  • -Physics
  • -Statics and Dynamics
  • -Circuits
  • -Thermo
  • -Fluids
  • Chemical Engineering
    • -Mass
    • -Kinetics
    • -Controls
    • -Thermo 2

•  
  • ORIE

• Optimization
  • -Stochastic Processes
  • Probability
  • Statistics
  • Data Collection 
  • Time Series
  • Data Processing
    • ML, regression, etc.

• System Analysis
  • -optimization, simulation, logistics, PIC, systems modeling, MDP
  • -Visualization
    • -graphs, system modeling
  • -Operations Design
    • -simulation, systems modeling
  • -Operations Management
    • -PIC, logistics, simulation, queuing

Workflow

R:

• Workflow
  • https://r-norberg.blogspot.com/2013/06/generating-tables-using-pander-knitr.html
• Production quality Figures and Reports
  • https://rafalab.github.io/dsbook/index.html
• Tidyverse
  • https://rafalab.github.io/dsbook/index.html
  • https://bookdown.org/roy_schumacher/r4ds/
• R Markdown
  • https://bookdown.org/yihui/rmarkdown/
    • Bookdown:
    • Blogdown:
• Visualization and Animation
  • Ggplot
  • Ggplot Extensions (e.g. gganimate)
    • https://www.ggplot2-exts.org/
  • Plotly
  • Animation
    • https://towardsdatascience.com/animating-your-data-visualizations-like-a-boss-using-r-f94ae20843e3
• Geocomputation
  • https://geocompr.robinlovelace.net/index.html
• Machine Learning
  • Tensor Flow
  • Keras
• Optimization
  • Gurobi
• Loops, Functions, and Functionals
  • Apply Family
    • https://nicercode.github.io/guides/repeating-things/
  • RcPP
    • https://adv-r.hadley.nz/rcpp.html
  • Purr
    • https://r4ds.had.co.nz/iteration.html#introduction-14
  • Parrellization
    • https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html
    • https://psu-psychology.github.io/r-bootcamp-2018/talks/parallel_r.html#conceptual_overview_of_parallel_computing
• Simulation
  • Monte Carlo
    • https://stackoverflow.com/questions/22001977/monte-carlo-integration-in-r-getting-the-wrong-answer-using-hit-or-miss
  • Markov Chains
    • https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50
    • https://www.datacamp.com/community/tutorials/markov-chain-analysis-r
    • https://cran.r-project.org/web/packages/markovchain/vignettes/an_introduction_to_markovchain_package.pdf
  • Statistics
    • https://daviddalpiaz.github.io/r4sl/index.html

Python

• Pandas
  • https://towardsdatascience.com/python-for-data-science-basics-of-pandas-5f8d9680617e
  • https://towardsdatascience.com/python-for-data-science-what-to-do-before-performing-machine-learning-a30f62465632
  • https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/?utm_source=statistics-101-introduction-central-limit-theorem
• Machine Learning
  • Keras
  • Tensorflow
• Visualization
  • Matplotlab/Seaborn
  • Plotly
    • https://towardsdatascience.com/python-for-data-science-a-guide-to-data-visualization-with-plotly-969a59997d0c
    • https://towardsdatascience.com/python-for-data-science-advance-guide-to-data-visualization-with-plotly-8dbeaedb9724
• APIs
  • Apps
  • Development programs
    • https://towardsdatascience.com/learn-metaflow-in-10-mins-netflixs-python-r-framework-for-data-scientists-2ef124c716e4       

Writing / Presentations

• Blogs
  • https://bookdown.org/yihui/blogdown/
• Journals / Conference Papers
  • https://bookdown.org/yihui/rmarkdown/journals.html
• Presentations
  • Slidy, ioslides, powerpoint
    • https://bookdown.org/yihui/rmarkdown/presentations.html
• Writing and Editing
  • Writing Thesis Statement, Topic Sentence, Introduction, Conclusion, Subheadings
    • https://pro-academic-writers.com/blog/academic-paper-writing
  • Read first sentence and last sentence of every paragraph
    • https://www.constant-content.com/content-writing-service/2017/08/25-tips-for-editing-your-first-draft/
    • https://www.constant-content.com/content-writing-service/2017/10/11-tips-for-editing-your-own-writing/

Workflow for writing

• When writing paragraphs, put up a heading saying what that paragraph is supposed to do
  • Remove the headings later, but use them as notes
• Write introduction to get idea of what you want to show in the paper
  • Do small lit review to find novel contributions
  • Go through the main points you want to explore
• Then make charts for the results and update the intro
  • See if the results match your hypothesizes
  • Comment on peculiarities
• Approve those sections
• Then write conclusion, methods, background, etc
  • Conclusion should match the thesis questions of the introduction
  • Should also have an implications and future research part
• Note Results and Discussion are one section
  • Write about each figure
    • How to interpret the figure
    • What it says
    • What it means
  • Then talk about the common thread between a few figures
• Conclusion is a few paragraphs concluded not discussing