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Four tips to solve harder data science problems with Jupyter Notebooks

Jupyter notebooks are versatile tools that data scientists can use for a variety of purposes. In this article, we will explore four ways that Jupyter notebooks can be used to improve your data science workflow. We will discuss how Jupyter notebooks can be used to learn new programming languages, document your code, debug code, and create reproducible workflows. We will also provide tips on how to get the most out of Jupyter notebooks.

Use Jupyter Notebooks learn a new language

Jupyter notebooks are a helpful tool. They can be used for learning new languages. They support many languages, including Python, R, Julia and Scala! This is good because it means that they will work for you no matter what language you want to learn. You can use them to write or test code in any of these languages.

Use Jupyter Notebooks to Document code

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Comments can explain what is happening in your program and why you chose a certain method. It may be helpful to have comments when you share your code with others. You can use a markdown cell in Jupyter notebooks to write text with formatting. The text is saved with an HTML or Markdown extension.

Debug code in Jupyter Notebooks

You can write and execute the code in the notebook, which might help you see where it’s breaking down. To make sure your code is running without problems, add a separate output cell for each command that is run. Remove all the debug statements from the code and only leave the parts you want to use.

Monitor your code execution environment

The “Kernel” menu option in the notebook allows you to view shell activity, such as I/O requests and system commands. This can help you see if your code is getting too slow or using too much memory.

In this article, we explored the many ways that Jupyter notebooks can be used to improve your data science workflow. We hope these tips will help you get started thinking about how you might use them in your own work or research. If all of this sounds intimidating and you want some more direction on where to begin, our team is ready and waiting to partner with you!

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David Callaghan, Solutions Architect

As a solutions architect with Perficient, I bring twenty years of development experience and I'm currently hands-on with Hadoop/Spark, blockchain and cloud, coding in Java, Scala and Go. I'm certified in and work extensively with Hadoop, Cassandra, Spark, AWS, MongoDB and Pentaho. Most recently, I've been bringing integrated blockchain (particularly Hyperledger and Ethereum) and big data solutions to the cloud with an emphasis on integrating Modern Data produces such as HBase, Cassandra and Neo4J as the off-blockchain repository.

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