1 minute read

Docsify, Main Duties of Data Scientists.

Docsify is a neat way to quickly build a documentation site for your github project. I used it to quickly make something for my PyPayroll Project. All you have to do is make a “_docs” folder in your project’s repo and add in 3 files ( .nojekyll, README.md, index.html). The index.html should have some code that looks like this:

<!-- index.html -->

<!DOCTYPE html>
<html>

<head>
  <title>PyPayroll</title>
  <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
  <meta name="viewport" content="width=device-width,initial-scale=1">
  <meta charset="UTF-8">
  <link rel="stylesheet" href="//unpkg.com/docsify/themes/vue.css">
</head>
<body>
  <div id="app"></div>
  <script>
    window.$docsify = {
      //...
      coverpage: true
    }
  </script>
  <script src="//unpkg.com/docsify/lib/docsify.min.js"></script>
</body>
</html>

The main Duties of a data scientist are:

  1. Problem Definition
    This consists of asking the right question. Most stakeholders do not know what they want. They want you to solve their problems with their Data. It is an important skill to understand how to formulate good Questions that can help in defining what problems we can solve with our data.
  2. Solution Measurement
    Once we have formulated a good question and we have used the data to come up with a model or a solution. We need a good metric to determine the quality or validity of our solution. This too is a difficult task. How do we really know how good our solution is? Sure we can model the data well and check our model’s accuracy but the model isn’t our solution. So we need to understand how to measure the actual quality of our solution.