Step 1: Answering the question¶

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The first step to any data analysis project is to define the question or problem we're looking to solve, and to define a measure (or set of measures) for our success at solving that task. The data analysis checklist has us answer a handful of questions to accomplish that, so let's work through those questions.

Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?

We're trying to classify the species (i.e., class) of the flower based on four measurements that we're provided: sepal length, sepal width, petal length, and petal width.

Did you define the metric for success before beginning?

Let's do that now. Since we're performing classification, we can use accuracy — the fraction of correctly classified flowers — to quantify how well our model is performing. Our company's Head of Data has told us that we should achieve at least 90% accuracy.

Did you understand the context for the question and the scientific or business application?

We're building part of a data analysis pipeline for a smartphone app that will be able to classify the species of flowers from pictures taken on the smartphone. In the future, this pipeline will be connected to another pipeline that automatically measures from pictures the traits we're using to perform this classification.

Did you record the experimental design?

Our company's Head of Data has told us that the field researchers are hand-measuring 50 randomly-sampled flowers of each species using a standardized methodology. The field researchers take pictures of each flower they sample from pre-defined angles so the measurements and species can be confirmed by the other field researchers at a later point. At the end of each day, the data is compiled and stored on a private company GitHub repository.

Did you consider whether the question could be answered with the available data?

The data set we currently have is only for three types of Iris flowers. The model built off of this data set will only work for those Iris flowers, so we will need more data to create a general flower classifier.

Notice that we've spent a fair amount of time working on the problem without writing a line of code or even looking at the data.

Thinking about and documenting the problem we're working on is an important step to performing effective data analysis that often goes overlooked. Don't skip it.