Baselight
Sign In

Cyclistic Bike-Share Data Analysis Case Study

Google Data Analytics Capstone: Case Study 1

@kaggle.mregoyau_google_case_study_1

Loading...
Loading...

About this Dataset

Cyclistic Bike-Share Data Analysis Case Study

This dataset is collected by Lyft Bikes and Scooters, LLC (“Bikeshare”) operates the City of Chicago’s (“City”) Divvy bicycle sharing service and this dataset is a part of the Google Data Analytics Professional Certificate capstone project on Coursera. Project Name: Case Study 1 Case Study: How Does a Bike-Share Navigate Speedy Success? This is the Data License Agreement of dataset.

Characters and teams
● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.

● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them.

● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

This dataset cover from January 2022 to December 2022.

Tables

N 202201 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202201_divvy_tripdata
  • 5.54 MB
  • 103,770 rows
  • 13 columns
Loading... 
CREATE TABLE n_202201_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202202 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202202_divvy_tripdata
  • 6.11 MB
  • 115,609 rows
  • 13 columns
Loading... 
CREATE TABLE n_202202_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202203 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202203_divvy_tripdata
  • 14.24 MB
  • 284,042 rows
  • 13 columns
Loading... 
CREATE TABLE n_202203_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202204 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202204_divvy_tripdata
  • 18.67 MB
  • 371,249 rows
  • 13 columns
Loading... 
CREATE TABLE n_202204_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202205 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202205_divvy_tripdata
  • 31.33 MB
  • 634,858 rows
  • 13 columns
Loading... 
CREATE TABLE n_202205_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202206 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202206_divvy_tripdata
  • 37.52 MB
  • 769,204 rows
  • 13 columns
Loading... 
CREATE TABLE n_202206_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202207 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202207_divvy_tripdata
  • 41.31 MB
  • 823,488 rows
  • 13 columns
Loading... 
CREATE TABLE n_202207_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202208 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202208_divvy_tripdata
  • 39.81 MB
  • 785,932 rows
  • 13 columns
Loading... 
CREATE TABLE n_202208_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202209 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202209_divvy_tripdata
  • 35.42 MB
  • 701,339 rows
  • 13 columns
Loading... 
CREATE TABLE n_202209_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202210 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202210_divvy_tripdata
  • 28.36 MB
  • 558,685 rows
  • 13 columns
Loading... 
CREATE TABLE n_202210_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202211 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202211_divvy_tripdata
  • 17.49 MB
  • 337,735 rows
  • 13 columns
Loading... 
CREATE TABLE n_202211_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202212 Divvy Tripdata

@kaggle.mregoyau_google_case_study_1.n_202212_divvy_tripdata
  • 9.55 MB
  • 181,806 rows
  • 13 columns
Loading... 
CREATE TABLE n_202212_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

Share link

Anyone who has the link will be able to view this.