kwartler / harvard_datamining_business_student

For students of Harvard CSCI E-96 Data Mining for Business

Harvard_DataMining_Business_Student

For students of Harvard CSCI E-96 Data Mining for Business

Syllabus

Fall 2024 University syllabus or PDF here

Spring 2024 University syllabus or PDF here

Working with R

If you are new to R, please take an online course to get familarity prior to the first session. We will still cover R basics but students have been aided by spending a few hours taking a free online course at Youtube or DataCamp. The code below should be run in the console to install packages needed for the semester.

Please install the following packages with this R code.

If you encounter any errors don't worry we will find time to work through them. The qdap library is usually the trickiest because it requires Java and rJava and does not work on Mac. So if you get any errors, try removing that from the code below and rerunning. This will take a long time if you don't already have the packages, so please run prior to class, and at a time you don't need your computer ie at night.

# Individually you can use 
# install.packages('packageName') such as below:
install.packages('ggplot2')

# or 
install.packages('pacman')
pacman::p_load(ggplot2, ggthemes, ggdark, rbokeh, maps, 
               ggmap, leaflet, radiant.data, DataExplorer,
               vtreat, dplyr, ModelMetrics, pROC,
               MLmetrics, caret, e1071, plyr, 
               rpart.plot, randomForest, forecast, dygraphs,
               lubridate, jsonlite, tseries, ggseas,
               arules,fst, recommenderlab,reshape2,
               TTR,quantmod, htmltools,
               PerformanceAnalytics,rpart, data.table,
               pbapply, rbokeh, stringi, tm, qdap, readr,
               dendextend, wordcloud, RColorBrewer,
               tidytext, radarchart, RCurl, openNLP, xml2, stringr,
               devtools, flexdashboard, rmarkdown, httr)

Class Schedule

This is tentative and subject to change to maximize learning

Date	Topic
Sept 9	Intro to R, R-studio & git
Sept 16	Intro to Data Mining
Sept 23	More R Practice & EDA
Sept 30	Data mining workflows
Oct 7	Regression & Log Regression
Oct 14	no class (university holiday)
Oct 21	Decision Tree & Random Forest
Oct 28	Time Series Data
Nov 4	Equities
Nov 11	Predicting Risk & non-traditional investing
Nov 18	Text analysis & NLP
Nov 25	Text analysis & NLP, continued
Dec 2	Chat GPT basics
Dec 9	Responsible AI & tech ethics
Dec 16	Optional Class Lab