R-Programming

R-ProgrammingSQLMaestros Admin2020-08-25T17:16:18+05:30

Getting R into production (Online)

This Master Class will be delivered by Steph Locke

Friendly Title	R-Programming
Class Title	Getting R into production
Trainer	Steph Locke (UK)
Brochure	Coming Soon
Delivery Type	Online
Delivery Format	Total 8 hours Two consecutive days 5 hours each day with one-hour break in between
Date	Coming Soon
Time	Coming Soon
Price	Coming Soon * Conversion rate of INR 70 per USD has been taken into consideration * For Indian Credit Cards, 18% GST will be charged extra * Group discounts available
Level	Intermediate
Pre-requisites	Have some prior exposure to R, at minimum from reading / training on R basics e.g. geni.us/rfundamentals
Key Highlights	Fast and effective data manipulation with R and databases Paralellisation and scaling processes Operationalising R code Best-practice package development
Takeaways	Be able to reduce time to code and the number of lines needed to code data manipulation queries Be able to scale out R processes to leverage additional processing power Understand architectural considerations for deploying R code and be able to implement some key methods Write and manage reusable code for production in your environment
Course Outline	Querying databases with R Data manipulation with dplyr recap Database connections, standalone and pooled Writing R dbplyr code against database tables Best practices for parameterized code and database writes Scaling R code Scaling and caching core concepts Using the future package to write parallelised code Using furrr to implement parallelisation in data pipelines Reducing repeat requests and implementing caching with the memoise package Using Microsoft ML Server to execute R code remotely Operationalising R Designing an R development environment Designing an R production environment Implementing R in SQL Server fundamentals Developing R APIs with Microsoft and non-Microsoft solutions Deploying R code in Docker containers Reusable R code The R package framework Writing functions and documentation Writing unit tests Managing non-R code dependencies Defensive R programming
Demos	Querying databases with R Writing dplyr to solve data manipulation tasks that require substantial lines of code in SQL Writing R dbplyr code against database tables with a variety of requirements Scaling R code A paralellised data pipeline Reducing API requests with caching Offloading processing to a remote ML Server Operationalising R Deploying data science models to SQL Server Constructing and deploying an API in a Docker container on Azure Container Instances Reusable R code Building an R package from scratch
Trainer’s Bio	Steph is the founder of a consultancy in the UK. Her talks, blog posts, conferences, and business all have one thing in common – they help people get started with data science. Steph holds the Microsoft MVP award for her community contributions. In her spare time, Steph plays board games with her husband and takes copious pictures of her doggos.
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