- What is Data Science, Big data and the Data Science process
- The origin of R, why use R, R vs Python and resources to learn
- Version Control, Git & GitHub and best practices for sharing code
- The 6 types of data analysis
- The ability to design experiments to answer your Ds questions
- P-value & P-hacking
- Big Data, it's benefits, challenges, and future
- Everything You Need to Learn R for Data Science
- 21 R books
- R profilers
- Lexical Scoping Vs Dynamic Scoping explained
- R Data Structures
- apply family in R
- parallel computing
- simulating data in R
- What is Tidy Data?
- TidyR functions
- dplyr
- Lubridate in R
- Data Reshaping in R
- The 6 Principles of Effective Data Visualization
- hierarchical clustering
- k-means clustering
- PCA
- SVD
- ggplot2 all graphs examples
- Adding color to plots in R
- Structure of Data Analysis
- Checklist for reproducible data analysis
- what is stats inference
- An intro to probability
- Conditional probability
- expected values
- variation
- 3 common distributions
- Asymptopics
- t confidence intervals
- Hypothesis testing
- p-values
- power
- Multiple Testing
- bootstrap and resampling
- Influence measures (dffits, dfbetas, rstudent, rstandard, Cook's distance)
- What is regression
- Stats learning vs machine learning
- interpreting regression coefficients
- residuals
- regression inference
- multivariate regression
- Model selection
- GLMs
- What are link functions
- Logistic regression
- Poisson regression
- Prediction study design
- In sample and out of sample errors
- Overfitting
- Receiver Operating Characteristic (ROC) curves
- The caret package in R
- Preprocessing and feature creation
- Prediction with regression
- Prediction with decision trees
- Prediction with random forests
- Boosting
- Prediction blending
- Plotly animation
- Leaflet map tutorial
- How are R packages made
- How to dockerize a shiny app
- R presentations
- How to uninstall R