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Google Data Analytics Professional Certification

Pedal Power Unleashed: Unveiling Insights from the Cyclistic Dataset!

@kaggle.sachinpillai_google_data_analytics_professional_certification

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About this Dataset

Google Data Analytics Professional Certification

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act.

About the company
In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.
Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.
Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs.
Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Ask
Three questions will guide the future marketing program:

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

All CSV files have been merged and is readily available for analysis

Tables

N 202004 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202004_divvy_tripdata
  • 3.89 MB
  • 84776 rows
  • 13 columns
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CREATE TABLE n_202004_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" BIGINT,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202005 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202005_divvy_tripdata
  • 8.77 MB
  • 200274 rows
  • 13 columns
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CREATE TABLE n_202005_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" BIGINT,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202006 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202006_divvy_tripdata
  • 14.79 MB
  • 343005 rows
  • 13 columns
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CREATE TABLE n_202006_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" BIGINT,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202007 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202007_divvy_tripdata
  • 24.46 MB
  • 551480 rows
  • 13 columns
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CREATE TABLE n_202007_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" DOUBLE,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202008 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202008_divvy_tripdata
  • 30.02 MB
  • 622361 rows
  • 13 columns
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CREATE TABLE n_202008_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" DOUBLE,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202009 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202009_divvy_tripdata
  • 27.6 MB
  • 532958 rows
  • 13 columns
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CREATE TABLE n_202009_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" DOUBLE,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202010 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202010_divvy_tripdata
  • 21.11 MB
  • 388653 rows
  • 13 columns
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CREATE TABLE n_202010_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" DOUBLE,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202011 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202011_divvy_tripdata
  • 14.26 MB
  • 259716 rows
  • 13 columns
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CREATE TABLE n_202011_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" DOUBLE,
  "end_station_name" VARCHAR,
  "end_station_id" DOUBLE,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202012 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202012_divvy_tripdata
  • 6.92 MB
  • 131573 rows
  • 13 columns
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CREATE TABLE n_202012_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 202101 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202101_divvy_tripdata
  • 5.19 MB
  • 96834 rows
  • 13 columns
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CREATE TABLE n_202101_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 202102 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202102_divvy_tripdata
  • 2.64 MB
  • 49622 rows
  • 13 columns
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CREATE TABLE n_202102_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 202103 Divvy Tripdata

@kaggle.sachinpillai_google_data_analytics_professional_certification.n_202103_divvy_tripdata
  • 11.05 MB
  • 228496 rows
  • 13 columns
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CREATE TABLE n_202103_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
);

Merged File

@kaggle.sachinpillai_google_data_analytics_professional_certification.merged_file
  • 170.09 MB
  • 3489748 rows
  • 13 columns
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CREATE TABLE merged_file (
  "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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