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Case Study: Divvy-tripdata

How does a bike-share navigate speedy success?

@kaggle.elenaracila_case_study_divvy_tripdata

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About this Dataset

Case Study: Divvy-tripdata

Introduction
Welcome to the Cyclistic bike-share analysis case study! In this case study, I work for a fictional company, Cyclistic, along with some key team members. In order to answer the business questions, I have to follow the steps of the data analysis process: Ask, Prepare, Process, Analyze, Share, and Act.

Scenario
I am a junior data analyst working on the marketing analyst team at Cyclistic, a bike-share company in Chicago. As the director of marketing concluded, that the company’s future success depends on maximizing the number of annual memberships. My team have to understand how casual riders and annual members use Cyclistic bikes differently.
From these insights, the marketing team will design a new marketing strategy to convert casual riders into annual members.

About the company
In 2016, Cyclistic launched a successful bike-share offering.
The company has 2 types of customers:

  1. Casual riders Customers who purchase single-ride or full-day passes
  2. Annual members Customers who purchase annual memberships are Cyclistic members.
    Cyclistic’s finance analysts have determined that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers. Rather than creating a marketing campaign that targets all-new customers, the director of marketing believes that there is a solid opportunity to convert casual riders into members.
    The goal of the analysis is to analyze the historical bike trip data, to identify trends and understand how these 2 types of customers use the service.

Tables

N 202303 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202303_divvy_tripdata
  • 12.91 MB
  • 258678 rows
  • 13 columns
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CREATE TABLE n_202303_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 202304 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202304_divvy_tripdata
  • 21.02 MB
  • 426590 rows
  • 13 columns
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CREATE TABLE n_202304_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 202305 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202305_divvy_tripdata
  • 30.02 MB
  • 604827 rows
  • 13 columns
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CREATE TABLE n_202305_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 202306 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202306_divvy_tripdata
  • 35.42 MB
  • 719618 rows
  • 13 columns
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CREATE TABLE n_202306_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 202307 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202307_divvy_tripdata
  • 37.26 MB
  • 767650 rows
  • 13 columns
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CREATE TABLE n_202307_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 202308 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202308_divvy_tripdata
  • 37.37 MB
  • 771693 rows
  • 13 columns
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CREATE TABLE n_202308_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 202309 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202309_divvy_tripdata
  • 32.43 MB
  • 666371 rows
  • 13 columns
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CREATE TABLE n_202309_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 202310 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202310_divvy_tripdata
  • 25.72 MB
  • 537113 rows
  • 13 columns
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CREATE TABLE n_202310_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 202311 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202311_divvy_tripdata
  • 17.86 MB
  • 362518 rows
  • 13 columns
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CREATE TABLE n_202311_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 202312 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202312_divvy_tripdata
  • 11.16 MB
  • 224073 rows
  • 13 columns
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CREATE TABLE n_202312_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 202401 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202401_divvy_tripdata
  • 7.22 MB
  • 144873 rows
  • 13 columns
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CREATE TABLE n_202401_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 202402 Divvy Tripdata

@kaggle.elenaracila_case_study_divvy_tripdata.n_202402_divvy_tripdata
  • 10.76 MB
  • 223164 rows
  • 13 columns
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CREATE TABLE n_202402_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
);

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