Data analysis attempts to solve problems by gathering relevant data and crafting statistical analysis algorithms whose output is as close as possible to the problem solution. The central challenge one faces here is twofold:

1) What data should I collect?

2) What data analysis should I perform?

To answer these ...

The first dimension of a data analysis problem is the population dimension. The statistical algorithms corresponding to the analysis of that dimension are called clustering algorithms. When fed a data frame containing the values of selected variables for a given population sample, these algorithms spit out clusters of individuals that ...

The second dimension of a data analysis problem is the variable dimension. The main algorithms for that dimension are the regression and classification algorithms ("supervised learning," in machine learning terms). As we saw, the variables we choose to consider for our target population allow us to carry out a "population ...

The last dimension of the data analysis problem is the time dimension. Namely, values collected on a population for a collection of relevant variables have a natural time stamp: the time and date of collection.

In certain cases, the time of collection is neither known nor relevant to the problem ...

After having 1) set up the data analysis problem and 2) forged a good idea of the ideal data set (and corresponding analysis) for the problem, the first stage of the data analysis process is to start gathering relevant data in order to build the best possible approximation of your ...

Once collected and saved into your local file system, the raw but regular data will need to be unified into an appropriate collection of numerical arrays for further analysis.

This is the second stage of the data analysis process. It involves operations on arrays such as merging, transforming, and aggregating ...