Nobusoeklczpxb5j4lli
SkillsCast

Workshop: The JavaScript Data Science Survival Kit - Intermediate

12th July 2017 in London at CodeNode

There are 79 other SkillsCasts available from FullStack 2017 - the conference on JavaScript, Node & Internet of Things

This session was not filmed.

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.

YOU MAY ALSO LIKE:

Workshop: The JavaScript Data Science Survival Kit - Intermediate

Philipp Burckhardt

Philipp Burckhardt is a PhD Student in the joint Statistics & Public Policy program of the Department of Statistics and the Heinz College at Carnegie Mellon University. He hold a Master's degree in Applied Statistics from the University of Oxford and a Bachelor's degree in Economics from Humboldt-University. Some of his interests are the development of statistical methods for analyzing unstructured textual data, specifically from the health-care domain and to develop platform-independent and browser-enabled statistical tools, to aid decision-making and make it easier to turn insights into action. Recently, he has become a major contributor to the Node.js JavaScript ecosystem, having (co)-authored more than 200 npm packages in the areas of numerical computing, statistical tools and text mining, among others. For his dissertation, he is working on an e-learning platform for statistics education built on state-of-the-art web technologies. Together with Athan Reines, he is engaged in the development of a standard library for JavaScript called stdlib. He has spoken at various international conferences on topics ranging from political science, health-care informatics to machine learning and software engineering.

SkillsCast

This session was not filmed.

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.

YOU MAY ALSO LIKE:

About the Speaker

Workshop: The JavaScript Data Science Survival Kit - Intermediate

Philipp Burckhardt

Philipp Burckhardt is a PhD Student in the joint Statistics & Public Policy program of the Department of Statistics and the Heinz College at Carnegie Mellon University. He hold a Master's degree in Applied Statistics from the University of Oxford and a Bachelor's degree in Economics from Humboldt-University. Some of his interests are the development of statistical methods for analyzing unstructured textual data, specifically from the health-care domain and to develop platform-independent and browser-enabled statistical tools, to aid decision-making and make it easier to turn insights into action. Recently, he has become a major contributor to the Node.js JavaScript ecosystem, having (co)-authored more than 200 npm packages in the areas of numerical computing, statistical tools and text mining, among others. For his dissertation, he is working on an e-learning platform for statistics education built on state-of-the-art web technologies. Together with Athan Reines, he is engaged in the development of a standard library for JavaScript called stdlib. He has spoken at various international conferences on topics ranging from political science, health-care informatics to machine learning and software engineering.

Photos