Course Agenda

Agenda

Course Agenda

 

Day One | November 6th

09:00

Registration and refreshments

09:30

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

  • 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

11:00

Morning Break

11:30

Data modeling and visualization strategies: building and testing algorithms

  • mechanics of modeling: built in and custom
  • rolling models
  • multiple models: fitting and managing
  • coding algorithmic logic with dplyr
  • ggplot2 and the art of exploratory data visualization
  • Interactive JavaScript visualizations

13:00

Lunch

14:00

Real world applications

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

15:30

Afternoon break

16:00

Shiny and Rmarkdown

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

17:30

End of day one

Day Two | November 7th

09:00

Refreshments

09:30

Forecasting: forecast package, anamolize, prophet, sweep

  • Modeling time series with forecast and anamolize
  • Forecasting with the forecast package
  • Forecasting with the prophet package
  • tidy and visualize forecasts with sweep

11:00

Morning break

11:30

Intro to Machine Learning

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

13:00

Lunch

14:00

Machine Learning in real world

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

15:30

Afternoon break

15:30

Deep Learning and high level intro to TensorFlow 

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

17:30

End of course