# Tag: Jupyter

**Jupyter Notebook Best Practices for Data Science**- Oct 20, 2016.

Check out this overview of Jupyter notebook best practices as pertains to data science. Novice or expert, you may find something of use here.**Top KDnuggets tweets, Aug 17-23: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language rule them all?**- Aug 24, 2016.

In Search of #Database Nirvana - can one query language rule them all? Google Cloud Datalab: #Jupyter meets #TensorFlow, #cloud meets local deployment; Approaching (Almost) Any #MachineLearning Problem; The Gentlest Introduction to Tensorflow Part 1.**Visualizing 1 Billion Points of Data: Doing It Right – Aug 18 Webinar**- Aug 11, 2016.

Join Continuum Analytics on August 18 for a webinar on Big Data visualization with the datashader library. Save your spot today!**Top KDnuggets tweets, Jul 13 – Jul 19: Bayesian #MachineLearning, Explained; Introducing JupyterLab**- Jul 20, 2016.

Bayesian #MachineLearning, Explained; JupyterLab: the next generation of the #Jupyter Notebook; On the importance of democratizing #ArtificialIntelligence**Statistical Data Analysis in Python**- Jul 18, 2016.

This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.**Top KDnuggets tweets, Jul 6 – Jul 12: Statistical Data Analysis #Python #Jupyter Notebooks; Modern Pandas Notebooks**- Jul 13, 2016.

Statistical Data Analysis in #Python (#Jupyter Notebooks); Modern Pandas: idiomatic Pandas notebook collection; New (free) book by @rdpeng: #rstats Programming for #DataScience**Jupyter+Spark+Mesos: An “Opinionated” Docker Image**- May 31, 2016.

Check "opinionated" Docker-based stacks for Jupyter, including one to combine Jupyter and Spark right out of the gate.**R or Python? Consider learning both**- Mar 8, 2016.

The key to become a data science professional is in understanding the underlying data science concepts and work towards expanding your programming toolbox as much as you can. Hence, one should understand when to use Python and when to pick R, rather mastering just one language.**Using Python and R together: 3 main approaches**- Dec 10, 2015.

Well if Data Science and Data Scientists can not decide on what data to choose to help them decide which language to use, here is an article to use BOTH.**Building and Sharing R packages made easy with Anaconda**- Sep 15, 2015.

"R Essentials" bundle comes with Jupyter, IRKernel, and over 80 of the most used R packages and dependencies for data science, and get Conda, the leading package manager for data science. Free Anaconda download.