The Data Exploration Process

Stacia Varga takes a step back from analyzing NHL data to explore it a little more:

As I mentioned in my last post, I am currently in an exploratory phase with my data analytics project. Although I would love to dive in and do some cool predictive analytics or machine learning projects, I really need to continue learning as much about my data as possible before diving into more advanced techniques.

My data exploration process has the following four steps:

  1. Assess the data that I have at a high level

  2. Determine how this data is relevant to the analytics project I want to undertake

  3. Get a general overview of the data characteristics by calculating simple statistics

  4. Understand the “middles” and the “ends” of your numeric data points

There’s some good stuff in here.  I particularly appreciate Stacia’s consideration of data exploration as an iterative process.

Related Posts

Biases in Tree-Based Models

Nina Zumel looks at tree-based ensembling models like random forest and gradient boost and shows that they can be biased: In our previous article , we showed that generalized linear models are unbiased, or calibrated: they preserve the conditional expectations and rollups of the training data. A calibrated model is important in many applications, particularly when financial data […]

Read More

Comparing Poisson Regression to Regressing Against Logs

Nina Zumel compares a pair of methods for performing regression when income is the dependent variable: Regressing against the log of the outcome will not be calibrated; however it has the advantage that the resulting model will have lower relative error than a Poisson regression against income. Minimizing relative error is appropriate in situations when […]

Read More

Categories

March 2018
MTWTFSS
« Feb Apr »
 1234
567891011
12131415161718
19202122232425
262728293031