Ultimate Data Analysis Course
“Ultimate Data” is a 2 day class for any developer who has some experience with Go and wants to learn how to work with data to make better decisions. We believe these classes are perfect for data analysts/scientists/engineers interested in working in Go or Go programmers interested in doing data analysis. This class provides an intensive, comprehensive and idiomatic view on building predictive models, analytics engines, components of data pipelines, and more using Go. It is, in our opinion, perfect for anyone wishing to build data-driven applications that produce valuable insights, have reproducible behavior, and can be deployed within modern architectures.
- Design, mechanics and philosophy
- Data gathering, cleaning and organization
- Matrices and linear algebra
- Statistics and aggregation
- Evaluation and validation
- Distributed data processing
Minimal Qualified Student:
- Has attended Ultimate Go, a similar class, or has a good understanding of the Go programming language
- Has a fully working Go environment and can build a "basic" Go application without the need for documentation and other "guides".
- Has a foundational understanding of statistics, probability, and mathematics.
- Has worked on the command line.
- Knows how to maneuver around the file system.
What a student is expected to gain:
- Confidence in retrieving, organizing, and cleaning data from various sources and formats.
- An understanding of how to perform numerical/statistical operations in Go.
- Exposure to and experience with Go-based predictive modeling packages.
- A toolkit of patterns and techniques for solving common data science problems in a reproducible and deployable manner.
- Light snacks and refreshments provided.
- No refunds are available within 7 days of the event.
Daniel Whitenack (@dwhitena) - a PhD trained data scientist/engineer with industry experience developing data science applications for large and small companies. Daniel has spoken at conferences around the world (Gopherfest, GopherCon, Datapalooza, DevFest Siberia, and more), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to other open source data science projects.