Slides for the Data Science Survival Kit Workshop.
The fields of machine learning and data science can appear intractable and overwhelming, often leaving newcomers at a loss for knowing where to begin. However, once you learn basic principles and workflows, you will see many opportunities where these techniques can help you in your projects. You can better understand your users, embed a recommendation engine into your application, or easily ship dashboards including statistical summaries and stunning visualizations.
The rise of Node.js and Electron are powering a new era where JavaScript continues to expand beyond the browser and becomes a critical component of server and desktop applications. With JavaScript being everywhere, one of the emerging next frontiers for JavaScript world domination is data science.
In this workshop, you will learn how to utilize the JavaScript open-source library stdlib for various data science tasks. Through a series of brief exercises, attendees will get a hands-on introduction, ranging from analytics to machine learning to exploratory data analysis and visualization. For example, you will investigate the accuracy of the native JavaScript Math functions, build a spam classifier, and generate synthetic texts using Markov chains.
After completing this workshop, you will have a solid understanding of what kind of problems they can approach by which techniques. Furthermore, you will have experience in conducting a full analysis from start to end, i.e., exploring, cleaning, transforming, and analyzing data with state-of-the-art techniques.
The workshop will close with an outline of future steps for data science in JavaScript and opportunities for community development of next-generation tools.
$ git clone https://github.com/Planeshifter/data-science-survival-kit.gitand
$ npm installFrom the top-level directory,
$ python -m SimpleHTTPServer 9000and, in your browser, navigate to
http://127.0.0.1:9000
Copyright © 2017. Philipp Burckhardt, Athan Reines.