The US job market is filled with exciting opportunities for aspiring Data Analysts. However, landing your first job can be challenging due to the diverse range of requirements employers are looking for. In this article, we analyze data from 15,963 Data Analyst job listings. We build a Jupyter Notebook with data analysis and visualization, and serve it as an interactive web app. For example, we search for the most needed skills and show their dependency on the average yearly salary. Let’s check what the most needed skills for Data Analyst are!

Jupyter Notebook is a popular open-source tool for development and exploration in a data world. It started from IPython Notebook developed by Fernando Pérez and Brian Granger. Currently, the Jupyter Notebook is available as 4 different web applications: Classic Jupyter Notebook, Jupyter Lab, Jupyter RetroLab, and Jupyter Lite. Let’s look closer for differences between those Jupyter versions.

Jupyter Notebook saves files in .ipynb format. It is a JSON with code, Markdown, and outputs. There are many cases in which we would like to convert Jupyter Notebook to plain Python script. For example, you would like to keep Python code in the repository or would like to turn your notebook into a standalone package. I will show you 3 ways to export the Jupyter Notebook file to Python script.

Presentation created with Jupyter Notebook is exported to an HTML file. It is interactive, thanks to the Reveal.js library. There are several options to publish HTML presentations in the cloud.

You created an analysis in Jupyter Notebook and would like to share it online with selected persons. In this article, I will show you how to add password protection to the selected Jupyter Notebook and share it only with authorized people.

Jupyter Notebook can be used to create data-rich presentations. They can be presented as interactive slideshow thanks to Reveal.js library. What if you would like to export it to PDF slides? I will show you two approaches that you can use to export Jupyter Notebook presentation into PDF slides.

Jupyter Notebook is a perfect tool for data analysis and quick prototyping. You can easily mix Python (or R, Julia, Scala …) code with Markdown. What to do if you would like to run your .ipynb file in the command line? There are several approaches to execute notebook in the terminal. There is no one-size fits all, it all depends on your needs. Do you need to execute notebook and share the output as PDF or HTML file? Would you like to upload executed notebook to the cloud storage? Would you like to add parameters to your notebook? I hope you will find solution for yourself in our list.

Jupyter notebooks are a convenient way of prototyping our Machine Learning models. They are also very easy to use and provide a convenient way of teaching data science concepts while simultaneously showing the working of the code using the code cells. If we want to test several examples for some functionality, then we need to repeatedly change the code and run the cells to get the output. Also, anyone who doesn’t have a good programming background may face difficulty handling the code.

Creating a Jupyter Notebook presentation is a great way to share your data-rich results. All your plots and results are already in slides. There is no need to copy results between Jupyter Notebook and software for presentation manually. The slideshow is a preferred way to share results with non-technical stakeholders. The code in the slides might scare them. How to hide code in a presentation generated from Jupyter Notebook? We will show you 3 approaches to hiding code in the slides.

Presentation created with Jupyter Notebook is a great way to show your results computed based on data. Wouldn’t it be amazing if there were a way to parameterize presentation with interactive widgets? There is an open-source framework called Mercury that can serve Jupyter Notebook presentations with interactive widgets. Users can recompute charts and values in slides during watching the presentation.

Presentation is a great way to share your results and findings with a nontechnical audience. The data-rich presentation with charts, tables, and code may be tedious to create. You need to copy-paste each image into traditional presentation software manually. What if you need to update all charts because your supervisor or manager asked for them? Ugh… again, a lot of work … Don’t worry, there is a solution for your pain! You can create a presentation directly from Jupyter Notebook. The presentation slides can be constructed from single, multi-code or Markdown cells. The cell’s code can be hidden in the presentation (to not scare nontechnical viewers). The final slides can be shared as a website or converted to a PDF file. Some frameworks allow you to parameterize the presentation with widgets making it interactive.

You have just created an excellent report in Jupyter Notebook and would like to run it daily. Are you looking for a scheduler solution that fulfills your requirements? It would be great to send executed notebook as a PDF by email. You would like to serve a website with an updated HTML view of the notebook and restricted access to only selected users. It would be fantastic to have an option to parametrize the notebook and execute it with customized values. There are several schedulers for Jupyter Notebook available. However, not all fulfill those requirements. This post will show you how to schedule your notebook to run daily with the open-source Mercury framework.

Jupyter Notebook is an excellent tool for creating computational documents. The mix of code and markdown makes it perfect for creating data-rich reports, dashboards, and slides. There is often a requirement to update the notebook’s results at a selected time interval and publish it to the rest of the team. For example, you must create a daily report from Jupyter Notebook and send it as a PDF file to team members by email.

The HTML representation of the Jupyter Notebook is very useful for sharing with non-technical users. The HTML can be served as a static website or even send by email. It is an universl format. You can open HTML file on any device with web browser application. What is more, the HTML that is constructed from executed Jupyter Notebook is packed in a standalone file, so you don’t need to send additional files with assets. I would like to show you 3 ways to convert Jupyter Notebook to HTML.

Jupyter Notebook is a powerful tool that can mix code and Markdown. It provides fast feedback for code snippets execution which makes it perfect for data analysis and experimenting. It is a development environment of choice for many data scientists, machine learning practitioners, and software developers. In this article, we will check different ways how to install Jupyter Notebook on your local machine.

Do you need to monitor data from multiple sources and act based on their values? If you answered yes, then it means that you need a dashboard. I will show you how to create a dashboard in Python. It will automatically update the values in the dashboard and send email notifications based on monitored values. The dashboard will be deployed to the Amazon AWS cloud service.

You are building your notebook and would like to make it more informative and attractive with the inserted image. How to do this? In this post, I will show different approaches to inserting an image into Jupyter Notebook.

Your analysis is ready. You would like to share results from Jupyter Notebook with non-technical users. You have to hide the Python code to not scare them. There are several ways in which it can be achieved.

Have you ever wanted to export your Jupyter Notebook to PDF file? The PDF is an abbreviation for Portable Document Format. It can be displayed on any operating system. That makes it format of choice for many who wants to share their results. In this post I will show you 4 different ways how to export Jupyter Notebook as PDF file.

Your analysis is ready. You found very important insights. Congratulations! What’s next? You need to share your Jupyter Notebook with non-programmers. How to do this? You can’t send them the ipynb file in the email attachement or just push it to the GitHub repository. Your peers don’t understand Python and can’t reproduce your work on their own computers. You can create a presentation for them. This can be tedious manual work that might require many updates if data and final results will changes. What to do? Don’t worry! In this post, I will show you how to share Jupyter Notebook with non-programmers.

The Jupyter Notebook offers a great way to mix code with Markdown. It allows create computational documents and is a step forward to literate programming proposed by Donald Knuth. Have you ever need to style a Markdown text in the Jupyter Notebook? I will show you how it can be done with Markdown or HTML syntax.

The Jupyter Notebook is a great tool for experimentation with code. It provides the REPL (read-eval-print loop) with a visual interface for plots, tables and many more. You can mix Markdown and selected programming language (usually Python). It is a default choice of development and experimentation environment for data scientists and machine learning practitioners. Have you heard about other ways to use the Jupyter Notebook? Let’s explore 8 alternative ways of how to use Jupyter Notebook that might surprise you!

Setting up a local environment to run Jupyter Notebook can be cumbersome. You need to install Python and tons of packages to make it works. The convenient alternative can be running notebooks in the cloud. There are many services that allow you to create, edit and run Python notebooks with premade data science environment. I would like to show you some popular services that offer Jupyter Notebooks online. What is more, I will also show you two new alternative approaches for running notebooks online.

The keyboard shortcuts make you fast as ninja. The faster you can provide the software to analyze data or solve engineering problem the better. In this article we show you Jupyter Notebooks keyboard shortcuts that you need to know. Learning shortcuts might take a while. We created a PDF ready to print with shortcuts for Mac, Windows and Linux users. It is available at the end of the article.

The *.ipynb file extension is used for computational notebooks that can be open with Jupyter Notebook. The Jupyter Notebook was formerly named IPython Notebook. The extension *.ipynb is from letters IPython Notebook. The IPython was developed as a command shell for interactive computing in Python programming language. It offers introspection, rich media, shell syntax, tab completion, and history.

The Jupyter notebooks provide great flexibility for developing Python programs. The code is organised in cells, and execution results are presented just below the cell. This approach gains huge trackion among programmers, especially data scientists, around the globe. To make the notebooks more accessible to non-programmers there is possible to add an interactive widgets to the notebook. What is more, such notebook can be shared as standalone web application, which greatly simplifies the sharing. In this article I will present two alternative approaches for adding widgets into Jupyter Notebook, namely ipywidgets+Voila and Mercury.

Wouldn’t it be fantastic if you could share a Jupyter Notebook with your coworkers and allow them to upload a new file that is processed by your notebook? I will show you how to add a file upload widget to your Jupyter Notebook and share it as a web app with Mercury framework.

Have you ever wanted to share your Jupyter notebook with non-programming friends? Would you like to hide the code, to not scare them? Give them the option to change the input with interactive widgets. In this post, I will show you how to easily share a notebook as an interactive web app with the Mercury framework.