Data Science, Analytics, Machine Learning using R, Apache Spark and Mllib

About the course

Data scientists build information platforms to provide deep insight and answer previously unimaginable questions. Spark and Hadoop are transforming how data scientists work by allowing interactive and iterative data analysis at scale. Learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities. This course helps participants understand what data scientists do, the problems they solve, and the tools and techniques they use. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field.

Objectives

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, and develop concrete skills such as:
1. How to identify potential business use cases where data science can provide impactful results
2. How to obtain, clean and combine disparate data sources to create a coherent picture for analysis
3. What statistical methods to leverage for data exploration that will provide critical insight into your data
4. Where and when to leverage Hadoop streaming and Apache Spark for data science pipelines
5. What machine learning technique to use for a particular data science project
6. How to implement and manage recommenders using Spark’s MLlib, and how to set up and evaluate data experiments
7. What are the pitfalls of deploying new analytics projects to production, at scale

Pre-requisites

This course is suitable for students, developers, data analysts, and statisticians with basic knowledge of Computer Science.

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