Course 2 - R Programming
Week 1
- Data types
- Reading in data
- Subsetting
Week 2
- Control structures
- Functions
- Scoping
- Dates and times
Week 3
- Loop functions
- Debugging tools
Course 3 - Getting and Cleaning Data
Week 1
- Components of tidy data
- Reading Excel files
- Reading XML
- Reading JSON
- The data.table package
Week 2
- Reading from MySQL
- Reading from HDF5
- Reading from The Web
- Reading from APIs
- Reading from other sources
Week 3
- Subsetting and sorting
- Reshaping data
- managing data with dplyr
Week 4
- Regular expressions
- Working with dates
- Data resources
Course 4 - Exploratory Data Analysis
Week 1
- Principles of analytic graphics
- Exploratory graphs
- Base plotting system
Week 2
- Lattice plotting system
- ggplot2
Week 3
- Hierarchical clustering
- K-means clustering
- Dimension reduction
- Working with colour in R plots
Week 4
- Clustering case study
- Air pollution case study
Course 5
Week 1
- Concepts and ideas
- Structuring a data analysis
Week 3
- RPubs
- Reproducible research checklist
- Evidence-based data analysis
Week 4
- Caching computations
- Case study: air pollution
- Case study: high throughput biology
- Commentaries on data analysis
Course 6 - Statistical Inference
Week 1
- Probability
- PMFs
- PDFs
- Baye’s rules
- Expected values
Week 2
- Variability
- Variance simulation example
- Standard error of the mean
- Binomial distribution
- Normal distribution
- Poisson distribution
- Asymptotics and LLN
- Asymptotics and the CLT
- Asymptotics and confidence intervals
Week 3
- Confidence intervals
- T tests
- Hypothesis testing
- P values
Week 4
- Power
- Multiple comparisons
- Bootstrapping
Course 7 - Regression Models
Week 2
- Linear regression
- Residuals
- Multivariable regression
Week 3
- Multivariable regression
- Residuals and diagnostics
- Model selection
Week 4
- Logistic regression
- Poisson regression
Course 8 - Practical Machine Learning
Week 1
- Prediction
- Types of errors
- Cross-validation
Week 2
- Training
- Preprocessing
- Prediction with regression
- Prediction with regression multiple covariates
Week 3
- Prediction with trees
- Bagging
- Random forests
- Boosting
- Model based prediction
Week 4
- Regularised regression
- Combining predictors
- Forecasting
- Unsupervised prediction
Course 9 - Developing Data Products
Week 1
- Shiny
- Manipulate
- rCharts
- GoogleVis
Week 2
- Writing a data report
- Slidify
- RStudio Presenter