Course Agenda

Agenda

Course Agenda

Course led by Nima Safaian, head of trading analytics, Cenovus Energy and Jonathan Regenstein, director of financial services, RStudio, Inc.

08:3009:00

Registration and refreshments

08:30 - 09:00

09:0010:30

Data import / wrangling: working with APIs and data providers, structuring and cleaning data

09:00 - 10:30

  • Intro with readr, readxl for csv and excel
  • API demo: interface to EIA data using rJSON
  • Dplyr for wrangling data
  • Lubridate for working with dates
  • Interface to SQL and NoSQL databases

10:3010:45

Break

10:30 - 10:45

10:4512:00

Data modelling and visualisation strategies: building and testing algorithms

10:45 - 12:00

  • Mechanics of modelling: built in and custom
  • Rolling models
  • Multiple models: fitting and managing
  • Coding algorithmic logic with dplyr
  • Ggplot2 and the art of exploratory data visualisation
  • Interactive JavaScript visualisations

12:0013:00

Lunch

12:00 - 13:00

13:0014:30

Real world applications

13:00 - 14:30

  • Time series analysis – visualising pricing relationship
  • Interactive maps using leaflet case study
  • Clustering analysis and heatmaps for risk reporting

14:3014:45

Break

10:30 - 10:45

14:4516:15

Shiny and RMarkdown

14:45 - 16:15

  • Intro to Shiny: hello world example (app.r and flexdashboard)
  • A more complex applications
  • Htmlwidgets
  • Highcharter
  • Reporting with RMarkdown: html and PDF

08:3009:00

Refreshments

08:30 - 09:00

09:0010:30

Forecasting: forecast package, anamolize, prophet, sweep

09:00 - 10:30

  • Modelling time series with forecast and anamolize
  • Forecasting with the forecast package
  • Forecasting with the prophet package
  • Tidy and visualise forecasts with sweep

10:3010:45

Break

10:30 - 10:45

10:4512:00

Introduction to machine learning

10:45 - 12:00

  • The R landscape for ML
  • Model selection
  • Cross validation and resampling with rsample
  • PCA analysis of commodity prices
  • ML time series forecasting: bagging, boosting

12:0013:00

Lunch

12:00 - 13:00

13:0014:30

Machine learning in real world

13:00 - 14:30

  • Building an automated insight generator
  • Price prediction using logistics regression
  • Sentiment analysis using natural language processing and classification methods

14:3014:45

Break

10:30 - 10:45

14:4516:15

Deep learning and high level intro to TensorFlow

14:45 - 16:15

  • What is deep learning?
  • What is tensorflow?
  • Building models using greta package
  • A time series forecasting example

16:1523:59

End of course

16:15 - 23:59