• A Google Data Analytics Professional Certificate case study.

• Real-world data + fictional company (Cyclistic)

• Task: analyze data, answer critical business questions, and make data-driven recommendations to maximize annual memberships.

• Business Objective: Chicago bike-sharing company Cyclistic aimed to increase annual memberships based on understanding the differences in bike usage between annual members and casual riders.

• Key Questions:

  • How do annual members and casual riders use Cyclistic bikes differently?
  • Why would casual riders buy Cyclistic annual memberships?
  • How can Cyclistic use digital media to influence casual riders to become members?

comparable_summaries(df, col, groups)
# A comprehensive function build on skim() to display summaries in concise and comparable way.
# Functioning early findings in comparable scenarios.

check_column_consistenc(csv_files, std_cols)
# Check if the cols of multiple csv files are consistent.

combine_csvs_to_tibble(csv_files)
# Merge csvs into one tibble

convert_datetime(column) 
# Convert messey POSIXct formats to uniform datetime

convert_datetime(column) 
# Convert messey POSIXct formats to uniform datetime

empty_value_monthly(tb, date_col, col1, col2)
# Display distribution of empty value in cols with bar chart

The project used geographical libraries to gain insights (see below).

• Ask: Defined key business questions and objectives.
• Prepare: Gathered and cleaned the data from various sources.
• Process: Performed data manipulation and transformation for analysis.
• Analyze: Conducted in-depth analysis to uncover insights.
• Share: Communicated findings through visualizations and reports.
• Act: Presented recommendations based on data insights.

• Casual riders are over 40%, showing high business potential.
• Although the distance of trips are similar, casual trips are 2.44 times slower in speed. Which shows evidence of leisure riding.

• The popular stations for two groups are distinct.

• For casual, more riding happened on weekends.

• Member's station distributed in clusters.
• Casual user's stations are along the lake.

• Top stations in Weekend and weekdays are distinct.
• Casual riders are significantly more active in weekend.

• Further investigation: The most popular stations are located in the most popular city attractions

• Strong seasonal volatility.
• Both trips are at the same level in Summer.
• Casual trips drop to significant lower level in winter.

• Trends are very similar in weekdays or in weekends respectively.
• In weekend, Overlapping lines reveal same behavior between two groups.
• In weekdays, the riding time is distinct.
• The peaks appear around 15 pm in weekends, and 17 pm in weekdays.
• No peak in weekday mornings for casual riding. But small peaks appear in evening rush hours.

• Casual riders still make up a large percentage
  • More than 40% ridings are made by casual riders.
  • The portation of casual are stable and the number are slightly growing in the last 3 years.
• Casual riders show clear evidence to perform leisure tourism.
  • They ride the similar distance in much longer time.
  • Their favorite stations spread along the beach.
  • They strongly prefer riding in Weekend, and have the same time pattern with members.
  • On weekday, they tend to ride around evening rush hours.
  • Their number of rides is at the same or even higher level in Summer. But in winter, they ride much less.

• R
  • RStudio
  • R Markdown
• SpreadSheet
• Kaggle
  • Kaggle Notebook
• Git
  • GitHub Repository