Actions and Detail Panel
Big Data Visualization Camp
Tue, Nov 14, 2017, 9:00 AM – Thu, Nov 16, 2017, 5:00 PM EST
A 3 days camp focused on methodologies, frameworks & libraries for interactive data visualization & analytics.
Learn to build scalable & real-time data-driven applications: recommendation engines, dashboards, control charts.
Stacks: MongoDB, Express, Angular, Node, React, D3, ArcGIS, Python, Spark, Scala, Kafka, R, Kinesis, AWS & Azure.
Click here to contact us if you have any questions, or want to sponsor us or get involved.
Track 1: Building Real-Time Recommendation Engines with Spark
A recommendation engine or recommender system is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, news, research articles, search queries, social tags, and products in general. In this presentation, we will build real-time recommender systems with Apache Spark from scratch. You will discover and implement the tools needed to build real-time recommendation engines with Spark and familiarize yourselves with various techniques of real-time recommender systems in Spark such as collaborative filtering, model based recommender system, and MLlib recommendation engine. Finally, you will familiarize yourself with machine learning algorithms using Spark and learn to create efficient decision-making systems that will ease your work.
Track 2: Building a Recommendation Engine with Scala
With an increase of data in online e-commerce systems, the challenges in assisting users with narrowing down their search have grown dramatically. The various tools available in the Scala ecosystem enable developers to build a processing pipeline to meet those challenges and create a recommendation system to accelerate business growth and leverage brand advocacy for your clients. During this bootcamp, you'll be introduced to Scala and other related tools to set the stage for your project and familiarize yourself with the different stages in the data processing pipeline. You'll discover different machine learning algorithms using MLLib, build different versions of recommendation engines from practical code examples, gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore other methods systems such as content-based, and cross-recommendations to improve users’ recommendation. You will also understand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engine.
Track 3: Building a Recommendation System with R
A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R is one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. This presentation will help you understand how to build recommender systems using R. You will learn the basics of data mining and machine learning, Then you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the presentation to build a recommender system.