How to identify potential business use cases where data science can provide impactful results.
How to obtain, clean and combine disparate data sources to create a coherent picture for analysis.
What statistical methods to leverage for data exploration that will provide critical insight into your data.
Where and when to leverage Hadoop streaming and Apache Spark for data science pipelines.
What machine learning technique to use for a particular data science project.
How to implement and manage recommenders using Spark’s MLlib, and how to set up and evaluate data experiments.
What are the pitfalls of deploying new analytics projects to production, at scale.
Are you ready? Let’s get started!