Course 2 - The R Language
- Week 1 Notes - Overview of R, data types, data frames, attributes, subsetting.
- Week 2 Notes - R control structures, for and while loops, functions, scoping rules, dates and times.
- Week 3 Notes - Loop function, lapply, sapply, apply, tapply, mapply, split, debugging.
- Week 4 Notes - str() function, simulation, permutation, profiling.
Course 3 - Getting and Cleaning Data
- Week 1 Notes - motivations and goals, raw and processed data, subset, joins.
- Week 2 Notes - reading from MySQL, reading HDFS, webscraping, APIs.
- Week 3 Notes - summarisation, new variables, reshaping, merging.
- Week 4 Notes - Fixing character vectors, working with dates.
Course 4 - Exploratory Data Analysis
- Week 1 Notes - Principles, exploratory graphs, plotting systems, graphics devices.
- Week 2 Notes - Latice plotting system, ggplot2.
- Week 3 Notes - Hierarchical clustering, K-means clustering, dimension reduction, colours and palletes.
Course 5 - Reproducible Research
- Week 1 Notes - Concepts and ideas, structure of data analysis
- Week 2 Notes - Coding standards, RMarkdown, knitr
- Week 3 Notes - Communicating results, rpubs, reproducible research checklist, evidence based data analysis.
- Week 4 Notes - Caching computations
Course 6 - Statistical Inference
- Week 1 Notes - Probability, CDF and survival functions, quantiles, conditional probability, expected values, sample means.
- Week 2 Notes - Variability, standard error, distributions, central limit theorem, confidence intervals.
- Week 3 Notes - T-confidence intervals, hypothesis testing, t-tests, p-values.
- Week 4 Notes - Power, t-test power, multiple testing, resampling.
Course 7 - Regression Models
- Week 1 Notes - Least Squares, Covariance, Correlation, Regression to the Mean
- Week 2 Notes - Interpreting coefficients, residuals, heteroskedacticity, slope and intercept variance, prediction vs slope intervals.
- Week 3 Notes - Multivariable linear models, influence measures, variance inflation, nested models.
- Week 4 Notes - Generalised linear models, logistic regressions, Poisson regressions.
Course 8 - Practical Machine Learning
- Week 1 Notes - Prediction, Cross-Validation, Types of Errors
- Week 2 Notes - Data Slicing, Training, Standardising, Imputation, Covariate Creation, PCA
- Week 3 Notes - Prediction with Trees, Bagging, Random Forests, Boosting, LDA, Naive Bayes
- Week 4 Notes - Thresholding, Ridge Regression, Lasso, Forecasting, Unsupervised Prediction
Course 9 - Developing Data Products