Publications by Daniel Lee
Leveraging Data Analytics to Address Seasonality in the Tourism Industry: Insights from the Ski Business (Part2)
Table of contents Data Mapping Amenity Index Analysis Nearby Attractions and Restaurants Tours: Quantity and Quality Tour Types: Opportunities for Differentiation and Collaboration Summarizing all in the Amenity Score Actionable Insights Conclusion Leveraging Data Analytics to Address Seasonality in the Tourism Industry: Insights from the Ski B...
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Leveraging Data Analysis to Address Seasonality in the Tourism Industry: Insights from the Ski Business
The tourism industry is highly susceptible to fluctuations in demand, often driven by seasonality. Businesses operating in popular tourist destinations face the challenge of managing varying customer demands throughout the year. As a solution, many businesses are turning to data analysis techniques to gain valuable insights that can inform stra...
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ATV Tour Customer Review Analysis
Table of contents Data Analysis map of ATV tours and their characteristics common characteristics of ATV tours price and ratings tour duration and ratings ratings and common ATV tour characteristics customer review analysis Tying these findings to business metrics Summary ATV Tour Customer Review Analysis based on data scraped from tripadvisor....
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# Load packages # Core library(tidyverse) library(tidyquant) # time series library(timetk) Goal Simulate future portfolio returns five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG” market: “SPY” from 2012-12-31 to 2017-12-31 1 Import stock prices symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG") prices <- tq_get(x = symbol...
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# Load packages # Core library(tidyverse) library(tidyquant) Goal Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset. 1 Import stock prices symbols <- c("COKE", "TSLA", "GOOG", "NVDA", "VOO") prices <- tq_get(x = symbols, get = "stock.prices",...
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# Load packages # Core library(tidyverse) library(tidyquant) library(readr) # Time series library(lubridate) library(tibbletime) # modeling library(broom) Goal Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset. five stocks: “SPY”, “EFA”, “IJS”, �...
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Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_un...
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# Load packages # Core library(tidyverse) library(tidyquant) Functions When should you write a function df <- tibble::tibble( a = rnorm(10), b = rnorm(10), c = rnorm(10), d = rnorm(10) ) # For reproducible work set.seed(1234) # Rescale Each Column df$a <- (df$a - min(df$a, na.rm = TRUE)) / (max(df$a, na.rm = TRUE) - min(df$a, na.rm ...
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# Load packages # Core library(tidyverse) library(tidyquant) Goal Collect individual returns into a portfolio by assigning a weight to each stock five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG” from 2012-12-31 to 2017-12-31 1 Import stock prices symbols <- c("COKE", "TSLA", "GOOG", "NVDA", "VOO") prices <- tq_get(x = symbols,...
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