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Cyclistic Bike Trip Data

Uncovered insights into rider behaviour, usage patterns, and trends from dataset

@kaggle.devtaadhikari_cyclistic_bike_trip_data

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About this Dataset

Cyclistic Bike Trip Data

The datasets provided for this case study are part of the capstone project in the Google Data Analytics course on Coursera. They are essential for completing the project tasks and gaining insights into real-world business scenarios. Although the datasets are named differently to reflect the fictional nature of Cyclistic, they are curated to facilitate comprehensive analysis and address key business questions.

These datasets are made available by Motivate International Inc. under a suitable license for educational and analytical purposes. They offer a valuable opportunity to explore the usage patterns of different customer segments utilizing Cyclistic bikes. However, it's important to note that privacy regulations prohibit using personally identifiable information of riders. Therefore, analyses cannot involve linking pass purchases to credit card details, and conclusions cannot be drawn about individual riders' residency or purchasing behaviour.

By adhering to data privacy guidelines and focusing on aggregated insights, analysts can still derive meaningful conclusions about user behaviour, preferences, and trends within the Cyclistic bike share system.

Tables

N 2023 April

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_april
  • 22.05 MB
  • 426,590 rows
  • 13 columns
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CREATE TABLE n_2023_april (
  "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 2023 August

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_august
  • 39.18 MB
  • 771,693 rows
  • 13 columns
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CREATE TABLE n_2023_august (
  "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 2023 December

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_december
  • 11.7 MB
  • 224,073 rows
  • 13 columns
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CREATE TABLE n_2023_december (
  "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 2023 February

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_february
  • 9.7 MB
  • 190,445 rows
  • 13 columns
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CREATE TABLE n_2023_february (
  "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 2023 July

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_july
  • 39.07 MB
  • 767,650 rows
  • 13 columns
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CREATE TABLE n_2023_july (
  "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 2023 June

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_june
  • 37.14 MB
  • 719,618 rows
  • 13 columns
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CREATE TABLE n_2023_june (
  "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 2023 March

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_march
  • 13.54 MB
  • 258,678 rows
  • 13 columns
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CREATE TABLE n_2023_march (
  "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 2023 May

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_may
  • 31.47 MB
  • 604,827 rows
  • 13 columns
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CREATE TABLE n_2023_may (
  "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 2023 November

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_november
  • 18.72 MB
  • 362,518 rows
  • 13 columns
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CREATE TABLE n_2023_november (
  "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 2023 October

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_october
  • 26.97 MB
  • 537,113 rows
  • 13 columns
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CREATE TABLE n_2023_october (
  "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 2023 September

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2023_september
  • 34.01 MB
  • 666,371 rows
  • 13 columns
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CREATE TABLE n_2023_september (
  "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 2024 January

@kaggle.devtaadhikari_cyclistic_bike_trip_data.n_2024_january
  • 7.57 MB
  • 144,873 rows
  • 13 columns
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CREATE TABLE n_2024_january (
  "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
);

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