# Tag: Jupyter (25)

**Top KDnuggets tweets, Jan 3-9: A collection of Jupyter notebooks NumPy, Pandas, matplotlib, basic #Python #MachineLearning**- Jan 10, 2018.

Artificial General Intelligence (AGI) in less than 50 years; Top KDnuggets tweets: 10 Free Must-Read Books for #MachineLearning and #DataScience; The Art of Learning #DataScience; Supercharging Visualization with Apache Arrow; Docker for #DataScience**Introducing R-Brain: A New Data Science Platform**- Oct 11, 2017.

R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.**Top KDnuggets tweets, Sep 27 – Oct 03: Introduction to #Blockchains & What It Means to #BigData; 7 More Steps to Mastering #MachineLearning With #Python**- Oct 4, 2017.

Also Jupyter Notebooks are Breathtakingly Featureless - Use Jupyter Lab; The 4 Types of Data #Analytics; Aspiring Data Scientists! Learn the basics with these 7 books.**From Notebooks to JupyterLab – The Evolution of Data Science IDEs**- Aug 16, 2017.

This live webinar (Aug 22) will discuss the impact that the notebook experience has had on data science, and how JupyterLab - the next generation data science IDE - has evolved from the classic notebooks.**JupyterCon – Collaborative Data Science, New York, August 22-25**- Jul 10, 2017.

Bloomberg, Microsoft, Netflix and others found how Jupyter Notebook - the new front end for collaborative data science - make data a competitive advantage. Save an extra 20% with code PCKDNG.**Exploratory Data Analysis in Python**- Jul 7, 2017.

We view EDA very much like a tree: there is a basic series of steps you perform every time you perform EDA (the main trunk of the tree) but at each step, observations will lead you down other avenues (branches) of exploration by raising questions you want to answer or hypotheses you want to test.**Getting Started with Python for Data Analysis**- Jul 5, 2017.

A guide for beginners to Python for getting started with data analysis.

**How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 3**- Jul 4, 2017.

In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%)**How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 2**- Jun 27, 2017.

In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles.**How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part I**- Jun 8, 2017.

As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!**Data Science for Newbies: An Introductory Tutorial Series for Software Engineers**- May 31, 2017.

This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.**KDnuggets™ News 17:n13, Apr 5: What makes a great data scientist? Best R Packages for Machine Learning**- Apr 5, 2017.

Also Best R Packages for Machine Learning; Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Learning; A Short Guide to Navigating the Jupyter Ecosystem.**A Short Guide to Navigating the Jupyter Ecosystem**- Mar 31, 2017.

This post presents a no-nonsense overview of the Jupyter ecosystem, and a few tips, tricks and concepts you may find useful for navigating it.**Top KDnuggets tweets, Mar 22-28: Big #DataScience: Expectation vs. Reality**- Mar 29, 2017.

Also A Gentle Introduction To Graph Theory; An Overview of #Python #DeepLearning Frameworks; The Great Algorithm Tutorial Roundup.**Moving from R to Python: The Libraries You Need to Know**- Feb 24, 2017.

Are you considering making a move from R to Python? Here are the libraries you need to know, how they stack up to their R contemporaries, and why you should learn them.**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.