Publishing Your Jupyter Notebooks, Get Your Insights Standout

Wed, Apr 24, 2024
3-minute read

Table of Contents ▼

Introduction：

Fortunately, I get an admission of master of Biostatistics from ASU. To gain hands-on experience on data analyses, I’ve spent some time working on resume or side projects; thus, this article comes out.

Although I only got an interview for data analyst position, this experience motivated me to write this article; also, the article is a pro tips to myself, which help me to write a better resume.

some bullet points helps resume better

I merely modified my resumes from my previous job, Java backend engineer; additionally, experience dose not change overall. That’s why interviewer can not identify reasons why I can be standout.

After the interview, I summarized some bullet points to strengthen my resume.

python skills
present your data analysis outcomes

In next section, I will introduce how to use jupyter notebook to present your work on portofolio.

Why Use Jupyter Notebook:

Choosing Jupyter Notebook for a data analysis project presentation has several advantages:

Jupyter Notebook can be published on the web, making it understandable to HR or PMs without a technical background.
Besides supporting markdown documents, Jupyter Notebook also supports Python and R code blocks, showcasing coding skills more intuitively.
GitHub repositories only contain code, making it difficult for interviewers to quickly assess a candidate’s proficiency.

How to Start a Jupyter Notebook Project:

Pre-requisites:

Install Anaconda or Miniconda (a lightweight version of Anaconda). Use the conda command in the command line.

Open the command line and enter the following commands to install Jupyter Notebook and related packages in your environment:

Update Conda packages
```
conda update conda
```

Create a virtual environment:

conda create -n yourenvname python=${your python ver.}

Activate the virtual environment:
```
conda activate yourenvname
```
Install the necessary packages in the virtual environment:
```
conda install -c conda-forge jupyter-book jupyter
```

Creating a Jupyter Notebook Project:

What is Jupyter Notebook?

An interactive web interface where you can write and execute Python code directly within the notebook.
It combines IPython (interactive Python execution) and Notebook.

Here’s how to create such a project:

Add files to an existing Jupyter Notebook project

Files to add:

Basic Jupyter Notebook configuration information (_config.yml)
Table of Content settings for Jupyter Notebook (_toc.yml)

Use Jupyter Book commands to create a new project After setting up the environment, use the jupyter-book command to create a template:
```
jupyter-book create your_folder/
```

After creating the Jupyter Notebook project, the folder structure is as follows:

P.S. You can use the tree command to view the structure.

$ tree your_folder
your_folder/
├── _config.yml
├── _toc.yml
├── intro.md
├── logo.png
├── markdown-notebooks.md
├── markdown.md
├── notebooks.ipynb
├── references.bib
└── requirements.txt

Make sure to include requirements.txt for installing dependencies during deployment.

Simple _config.yml settings:

# In _config.yml
title: My sample book
author: The Jupyter Book Community
logo: logo.png
execute:
  execute_notebooks: force

# Add a bibtex file for citations
bibtex_bibfiles:
  - references.bib

Three main settings:

title: Your Jupyter Notebook title (customizable)
author: Author name (customizable)
logo: Your logo image name For more details, refer to here

Simple _toc.yml settings:

# In _toc.yml
format: jb-book
root: intro
chapters:
- file: markdown
- file: notebooks
- file: markdown-notebooks

Main settings:

format: Jupyter Book display style (jb-article or jb-book)
root: The homepage of your Jupyter Notebook
chapters: Add multiple filenames as chapters in the yml file, each representing a chapter.

Publishing Your Jupyter Notebook on the Web:

Currently, the jupyter notebook’s development team recommends using Netlify as the main solution for deployment. This section outlines the general process for completing the deployment.

Steps:

Register an account (it’s recommended to use GitHub)
Link the project you want to deploy from GitHub. After linking, an authorization window will appear, simply click “Yes.”
Set the build command and Publish directory options in the deployment settings to ensure successful deployment. For more detailed settings, check here

Sources:

Job Tech jupyter notebook Data Analysis Python Resume