https://www.datacamp.com/community/podcast/kaggle-future-data-science

<p>Anthony Goldbloom, CEO of Kaggle, speaks with Hugo about Kaggle, data science communities, reproducible data science, machine learning competitions and the future of data science in the cloud.</p>

Anthony Goldbloom, CEO of Kaggle, speaks with Hugo about Kaggle, data science communities, reproducible data science, machine learning competitions and the future of data science in the cloud.

Kaggle and the Future of Data Science

https://www.datacamp.com/community/news/my-first-kaggle-contest-th9roqvvaz9

My First Kaggle Contest

https://www.datacamp.com/community/news/conducting-an-internal-data-science-contest-at-appnexus-tsbj5q96dwa

The AppNexus Data Science team is always looking for ways to spread data science knowledge across AppNexus. We’re not the first company to recognize that scaling Machine Learning (ML) means improving…

Conducting an Internal Data Science Contest at AppNexus

https://www.datacamp.com/community/news/is-data-science-the-right-fit-for-you-khd6m0xdp5d

EDA of the Kaggle Survey from 2017.  See how you measure up!

Is Data Science the right fit for you?

https://www.datacamp.com/community/news/my-first-kaggle-contest-and-r-package-48kmh6hkl1m

Within an R project, leadr maintains a personal leaderboard for every model built. The package also handles model saving and organization. By the time I unit tested, documented, and built a pkgdown website leadr cost me days in the competition and my score no doubt suffered. Totally worth it.

My First Kaggle Contest and R Package

<p>We&#39;ve had an amazing turn out for the second FB live-coding event! Thanks to all that joined Hugo Bowne-Anderson in December in submitting several submissions to Kaggle&#39;s infamous <a href="https://www.kaggle.com/c/titanic">Titanic Machine Learning Competition!</a>. For those that missed it, here is a recap to find all the useful links and notebooks to take you from zero to one with machine learning in Python.</p>
<p>This live code-along session covers how to build an algorithm that predicts whether any given passenger on the Titanic survived or not, given data on them such as the fare they paid, where they embarked and their age. Hugo shows you how to do so using the Python programming language, Jupyter notebooks and state-of-the-art packages such as <code>pandas</code>, <code>scikit-learn</code> and <code>seaborn</code>. Dive into this rich dataset and build your chops in exploratory data analysis, data munging and cleaning, and machine learning. No previous experience with machine learning necessary, just a will to learn!</p>
<p>This code along session is meant for beginners and intermediates alike. Some programming fundamentals and Python basics will help though (e.g., variables, for loops). Hugo uses Jupyter Notebooks and the terminal. However, if you’re not super familiar with these tools, never fear! We&#39;ll link all the resources you need to complete this project.</p>
<p>You&#39;ll learn how to put all of these tools together to produce informative figures such as this:</p>
<p><a href="https://github.com/datacamp/datacamp_facebook_live_titanic"><img src="http://res.cloudinary.com/dyd911kmh/image/upload/q_auto:best/v1515431047/output_65_0_knd6e9_dsokhg.png" alt=""></a></p>
<p>Feel free to code-along or just watch Hugo do his thing! For those that do wish to code please follow the <a href="https://github.com/datacamp/datacamp_facebook_live_titanic">instructions to set up</a> before starting the video. You’ll need, amongst other things, to clone <a href="https://github.com/datacamp/datacamp_facebook_live_titanic">the repo</a> and download the <a href="https://www.anaconda.com/download/">Anaconda Distribution</a> for Python 3.6 if you haven’t already.</p>
<p>Now you are ready to watch Hugo and follow along. The video lasts about 1 hour and 40 minutes. You can skip to the 10th minute mark where Hugo comes on and the session actually starts.</p>
<p><center><iframe src="https://www.facebook.com/plugins/video.php?href=https%3A%2F%2Fwww.facebook.com%2F726282547396228%2Fvideos%2F1889371314420673%2F&width=560&show_text=false&height=315&appId" width="560" height="520" style="border:none;overflow:hidden" scrolling="no" frameborder="0" allowTransparency="true" allowFullScreen="true"></iframe></center></p>
<p>You can find 3 detailed tutorials showing the various submissions with the solutions here:</p>
<ul>
<li><a href="https://www.datacamp.com/community/tutorials/kaggle-machine-learning-eda">EDA &amp; Machine Learning</a></li>
<li><a href="https://www.datacamp.com/community/tutorials/kaggle-tutorial-machine-learning">Your First Machine Learning Model</a></li>
<li>3rd one coming January 10!</li>
</ul>
<p>Can you improve on Hugo&#39;s submissions? Let us know how high you can rank on Kaggle!</p>
<p>Help us make these sessions better by sharing feedback on Twitter to <a href="http://twitter.com/DataCamp">@DataCamp</a> &amp; <a href="http://twitter.com/hugobowne">@hugobowne</a>.</p>
<p>See you in the next live coding session!</p>


https://www.datacamp.com/community/blog/kaggle-live-coding-recap

Recap post outlining all the resources from the second live-coding session!

Live-Coding Recap: Kaggle Competition with Machine Learning

<h2>Getting Started with Python</h2><p>In this chapter we will go trough the essential steps that you will need to take before beginning to build predictive models.</p><h2>Predicting with Decision Trees</h2><p>After making your first predictions in the previous chapter, it's time to bring you to the next level. In chapter 2 you</p><h2>Improving your predictions through Random Forests</h2><p>What techniques can you use to improve your predictions even more? One possible way is by making use of the machine learning method Random Forest. Namely, a forest is just a collection of trees...</p>

https://www.datacamp.com/community/open-courses/kaggle-python-tutorial-on-machine-learning

Always wanted to compete in a Kaggle competition but not sure you have the right skill set? In this course, you'll learn to predict survival rate for Kaggle's Titanic competition.

Kaggle Python Tutorial on Machine Learning

<h2>Analyzing the 2013 American Community Survey</h2><p>In this first chapter you will use data from the <a href="https://www.kaggle.com/census/2013-american-community-survey" target="_blank" >2013 American Community Survey</a> to figure out whether it makes sense to pursue a PhD or not. The end result of this chapter will be <a href="https://www.kaggle.com/martijn/d/census/2013-american-community-survey/data-exploration-with-kaggle" target="_blank">your own Kaggle script</a> that you can add to your Kaggle account.</p><h2>Pigeons, Chopsticks, Spanish Silver and Marijuana</h2><p>Analyze pigeon racing data, discover the optimal size for your chopsticks, plot Spanish silver production in the 18th century and map the average price of marijuana in the different states of the US.</p>

https://www.datacamp.com/community/open-courses/data-exploration-with-kaggle-scripts

In this course, you'll begin learning the art and science of data exploration. You'll also become familiar with some of the most widely used visualization packages in data science.

Data Exploration With Kaggle Scripts

https://www.datacamp.com/community/tutorials/feature-engineering-kaggle

Learn how feature engineering can help you to up your game when building machine learning models in Kaggle: create new columns, transform variables and more!

Feature Engineering with Kaggle Tutorial

Machine Learning with Kaggle: Feature Engineering

https://www.datacamp.com/community/tutorials/kaggle-tutorial-machine-learning

Learn how to build your first machine learning model, a decision tree classifier, with the Python scikit-learn package, submit it to Kaggle and see how it performs! 

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