A Beginner’s Guide to Data Analysis in Python

A Beginner’s Guide to Data Analysis in Python

A step by step guide to get started with data analysis in Python. In this article, I am going to walk you through the end-to-end data analysis process with Python.

The Role of a Data Analyst

A data analyst uses programming tools to mine large amounts of complex data, and find relevant information from this data.

In short, an analyst is someone who derives meaning from messy data. A data analyst needs to have skills in the following areas, in order to be useful in the workplace:

  • Domain Expertise — In order to mine data and come up with insights that are relevant to their workplace, an analyst needs to have domain expertise.
  • *Programming Skills *—As a data analyst, you will need to know the right libraries to use in order to clean data, mine, and gain insights from it.
  • Statistics — An analyst might need to use some statistical tools to derive meaning from data.
  • Visualization Skills — A data analyst needs to have great data visualization skills, in order to summarize and present data to a third party.
  • *Storytelling — *Finally, an analyst needs to communicate their findings to a stakeholder or client. This means that they will need to create a data story, and have the ability to narrate it.

In this article, I am going to walk you through the end-to-end data analysis process with Python.

If you follow along to this tutorial and code everything out the way I did, you can then use these codes and tools for future data analytic projects.

We will start with downloading and cleaning the dataset, and then move on to the analysis and visualization. Finally, we will tell a story around our data findings.

I will be using a dataset from Kaggle called Pima Indian Diabetes Database, which you can download to perform the analysis.

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