GithubHelp home page GithubHelp logo

chanronnie / resume_survey_tableau Goto Github PK

View Code? Open in Web Editor NEW
0.0 1.0 0.0 53 KB

Perform web scraping with Python to collect data and create an effective and engaging visualization using Tableau, along with some tweaks.

License: MIT License

Jupyter Notebook 100.00%
python tableau

resume_survey_tableau's Introduction

Most Common Mistakes

About this project

Objective

The goal of this project is to create my own dataset by collecting data from an online survey that studied the most common resume mistakes, and to use this data to build an effective and engaging visualization. The key steps involved in this process include:

  1. Data collection using web scraping techniques with Python in Jupyter Notebook
  2. Data visualization creation using Tableau
  3. Data visualization makeover.

Data Source

The data is collected from the CareerBuilder website using Python code. It consists of a survey conducted in 2018 by The Harris Poll on behalf of CareerBuilder regarding the most common mistakes found in the resumes.

Tools Used

Python Jupyter Notebook Tableau

File Contents

Approach

The Data Extraction

The data survey are represented on the CareerBuilder website in both short paragraphs and bullet points forms. The data needed for our specific visualization are in list form, and there are only 7 records to extract. Using Python code and Python libraries (such as BeautifulSoup and requests), the data is easily extracted from the website.

๐Ÿš€ Please click here for more details about the Python code.

data_survey.csv

View Table Here is the collected data.
mistakes percent
Typos or bad grammar 77
Unprofessional email address 35
Resume without quantifiable results 34
Resume with long paragraphs of text 25
Resume is generic, not customized to company 18
Resume is more than two pages 17
No cover letter with resume 10

comparision

The Original Visualization

Resume mistakes are categorical data. A horizontal bar chart is an effective method to visualize this particular data. However, applying the default Tableau design to the bar chart may introduce unnecessary visual noise. The categorical labels have lengthy text spanning over two lines, making the chart less appealing to read. The presence of the gridlines and the axis labels makes the visualization appear clustered... Hence, the bar chart can be somehow more effective with fewer elements.

The Makeover

I wanted to keep to one visualization that effectively communicates the survey's message. To achieve this result, here are the modifications I made:

  • Removed the gridlines and axis labels from the chart to minimize visual noise.
  • Labeled each bar with its corresponding value to make the visualization more informative.
  • Shortened categorical labels for extensive data by renaming variable aliases in Tableau.
  • Transferred lengthy categorical text to the right side of the chart using a dummy variable created through a calculated field.
  • Added a header and a description on top of the visualization to provide more context to the audience.

๐Ÿ“ To view my visualization on Tableau, please click here

resume_survey_tableau's People

Contributors

chanronnie avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.