Flatiron Data Science Blog

Recently, I worked on a project where one of the main goals was to create a series of highly interactive graphs in Python.  Since matplotlib, which is my usual go-to for graphing, has limited capabilities in this regard, I decided that I would need to try a different library in order to get the results that I wanted.  The solution I ended up using was the Plotly graphing library.  I was so impressed with my results and the capabilities of Plotly that I’ve decided this blog post will be dedicated to explaining a bit about the library and looking at some of its more interesting features.   First, I’ll give a broad overview of Plotly and how it is structured.  Like many other Python modules, the main purpose of Plotly is to create, manipulate, and render graphical figures such as charts, plots, diagrams and even maps.  These figures are rendered using the Plotly.js JavaScript library and can be represented in Python as either dictionaries or as instances of the Plotly Figure class.  These

Today I’m going to be writing a quick guide on how to get started on your first data science project.  I will be assuming the reader has very little experience in data science and giving an overview of the entire project process from start to finish, with particular attention paid to the early stages.   The first and second steps to follow, choosing a topic and dataset, can be done in either order depending on your preference.  In some cases one might be better suited than the other.  For example, choosing a topic first doesn’t do much good if there is no data available or it can’t be easily gathered.  Likewise, a dataset by itself isn’t very helpful if you aren’t able to think of any interesting topics to explore with it.     Choosing a Topic My personal preference when getting started on a project is to choose a topic first and then look for a dataset that is applicable.  For example, on my most recent project I decided on a topic related to the stock market.  With stock data so easi