a. The data science process

The roles in a data science project

Stages of a data science project

Setting expectations

b. Loading data into R

Working with data from files

Working with relational databases

c. Exploring data

Using summary statistics to spot problems

Spotting problems using graphics and visualization

d. Managing data

Cleaning data

Sampling for modeling and validation

a. Choosing and evaluating models

Mapping problems to machine learning tasks

Evaluating models

Validating models

b. Memorization methods

KDD and KDD Cup 2009

Building single-variable models

Building models using many variables

c. Linear and logistic regression

Using linear regression

Using logistic regression

d. Unsupervised methods

Cluster analysis

Association rules

e. Exploring advanced methods

Using bagging and random forests to reduce training variance

Using generalized additive models (GAMs) to learn nonmonotone relationships

Using kernel methods to increase data separation

Using SVMs to model complicated decision boundaries

a. Documentation and deployment

The buzz dataset

Using knitr to produce milestone documentation

Using comments and version control for running documentation

Deploying models

b. Producing effective presentations

Presenting your results to the project sponsor

Presenting your model to end users

Presenting your work to other data scientists