These datasets are used for the case study as the capstone project in Google Data Analytics course on Coursera

The datasets have a different name because Cyclistic is a fictional company. For the purposes of this case study, the datasets are appropriate and will enable you to answer the business questions. The data has been made available by Motivate International Inc. under this license.

This is public data that you can use to explore how dierent customer types are using Cyclistic bikes. But note that data-privacy issues prohibit you from using riders’ personally identifiable information. This means that you won’t be able to connect pass purchases to credit card numbers to determine if casual riders live in the Cyclistic service area or if they have purchased multiple single passes.

This table contains information on private households owning passenger cars and motor-two-wheelers/motorcycles as at 1 January of the year. If someone within the household has a lease car that is also used for private purposes, this is also regarded as car ownership. A household can own both a car and a motorcycle. In that case, this household is included in both households with a car and households with a motorcycle. In addition to the absolute number of households with cars/motorcycles, the number of households with cars/motorcycles is also presented as a percentage of the total number of private households. The percentage is calculated within the group of households with the same background characteristic. Example: in 2010, 71.6 percent of all private households owned at least one passenger car: 49.1 percent had one car, 18.4 percent had two cars and 4.0 percent had three or more cars. Data available for 2010 - 2015 Status of the figures: The figures in this table are provisional Changes as of 9 March 2017 The provisional figures for 2015 have been added. When will new figures be available? This table was discontinued on 20 August 2018. However, it is possible to have tables with more up-to-date or other figures on vehicle ownership by individuals and households customized by Statistics Netherlands. To do so, please contact the Infoservice.

Cyclistic Bike-Share Data

This dataset is based on the Cyclistic Bike-Share case study, which is the part of Google Data Analytics Professional Certificate capestone project.

About 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 the bikes to commute to work each day.

Data Source

Data in this dataset is collected from thedivvy-tripdata.s3.amazonaws.com . The data has been made available by Motivate International Inc. under thislicense. This is public data that we can use to explore how different customer types are using Cyclistic bikes. But note that data-privacy issues prohibit us from using riders’ personally identifiable information.

Bike Share Figures 2025 2028 FCC. Published by Fingal County Council. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This data set contains the details of Bike Rental Scheme within Fingal County Council from 2025-2028 inclusive. See older data set Bike Shares 2022-2024_FCC.You don’t need to own a bike to get cycling you can sign up for Bleeper or Tier and unlock a bike or e bike to use whenever you need one.These bikes are ideal for daily trips to work or training or the shops as well as leisure too. So whatever you are thinking about cycling to the park or pedal to the shops you can sign up by downloading the apps for relevant bike rental scheme you choose to use without the storage issues of owning a bike, you can still pedal away !!! Enjoy...

Cyclistic bike-share analysis case study

License

The dataset contains Cyclistic’s historical trip data for the past 12 months to analyze and identify trends. The data has been made available by Motivate International Inc. The data provides the following attributes: - Ride ID - Rideable type - Electric / Classic bike - Start and End Date of the trip - Start and End Station Name with Id - Start and End Latitude and Longitute - Rider Type - Member / Casual

This case study is a part of the Google data analytics Certificate course. The analysis is for a fictional company, Cyclistic, A bike-share program that features more than 5,800 bicycles and 600 docking stations.

The annual bike and pedestrian count is a volunteer data collection effort each fall that helps the City understand where and how many people are biking and walking in Somerville, and how those numbers are changing over time. This program has been taking place each year since 2010. Counts are collected Tuesday, Wednesday, or Thursday for one hour in the morning and evening using a “screen line” method, whereby cyclists and pedestrians are counted as they pass by an imaginary line across the street and sidewalks. Morning count sessions begin between 7:15 and 7:45 am, and evening count sessions begin between 4:45 and 5:15 pm. Bike counts capture the number of people riding bicycles, so an adult and child riding on the same bike would be counted as two counts even though it is only one bike. Pedestrian counts capture people walking or jogging, people using a wheelchair or assistive device, children in strollers, and people using other micro-mobility devices, such as skateboards, scooters, or roller skates. While the City and its amazing volunteers do their best to collect accurate and complete data each year and the City does quality control to catch clear errors, it is not possible to ensure 100% accuracy of the data and not all locations have been counted every year of the program. There are also several external factors impacting counts that are not consistent year-to-year, such as nearby construction and weather. For these reasons, the counts are intended to be used to observe high-level trends across the city and at count locations, and not to extrapolate that biking and walking in Somerville has changed by a specific percentage or number. Data in this dataset are available at the _location count level. To request data at the movement level, please contact transportation@somervillema.gov.

Safety is the biggest concern people have with riding a bike. Research shows that 78 percent of people are interested in using a bike to get around but are only comfortable riding in protected lanes. BikeSpot provides the opportunity for all Australians to share their experiences of cycling safety to generate new data and insights for prioritising cycling infrastructure improvements.

San Francisco Ford GoBike , managed by Motivate, provides the Bay Area’s bike share system. Bike share is a convenient, healthy, affordable, and fun form of transportation. It involves a fleet of specially designed bikes that are locked into a network of docking stations. Bikes can be unlocked from one station and returned to any other station in the system. People use bike share to commute to work or school, run errands, get to appointments, and more. The dataset contains trip data from 2013-2018, including start time, end time, start station, end station, and latitude/longitude for each station. See detailed metadata for historical and real-time data . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

ABSTRACT CONTEXT AND OBJECTIVE: Traffic accidents have gained prominence as one of the modern epidemics that plague the world. The objective of this study was to identify the spatial distribution of potential years of life lost (PYLL) due to accidents involving motorcycles in the state of São Paulo, Brazil. DESIGN AND SETTING: Ecological and exploratory study conducted in São Paulo. METHODS: Data on deaths among individuals aged 20-39 years due to motorcycle accidents (V20-V29 in the International Classification of Diseases, 10th revision) in the state of São Paulo in the years 2007-2011 were obtained from DATASUS. These data were stratified into a database for the 63 microregions of this state, according to where the motorcyclist lived. PYLL rates per 100,000 inhabitants were calculated. Spatial autocorrelations were estimated using the Global Moran index (IM). Thematic, Moran and Kernel maps were constructed using PYLL rates for the age groups of 20-29 and 30-39 years. The Terraview 4.2.2 software was used for the analysis. RESULTS: The PYLL rates were 486.9 for the ages of 20-29 years and 199.5 for 30-39 years. Seventeen microregions with high PYLL rates for the age group of 20-29 years were identified. There was higher density of these rates on the Kernel map of the southeastern region (covering the metropolitan region of São Paulo). There were no spatial autocorrelations between rates. CONCLUSIONS: The data presented in this study identified microregions with high accident rates involving motorcycles and microregions that deserve special attention from regional managers and traffic experts.

Report forms concerning the deaths of 120 motorcycle riders in traffic crashes in New South Wales have been studied. Most of those killed were young men. Deaths were particularly common after 6:00pm and at the weekend. In many cases a colliding vehicle had not been aware of the motorcyclist’s presence. Any measure which would make motorcycles more easily visible, such as the constant burning of headlights, should have a beneficial effect. Head injury was very common. If 100 per cent of motorcyclist in New South Wales wore helmets, rather than the 75 per cent as at present, the death rate would be cut by about 35 percent.

Daily vehicle miles traveled (VMT) is a distance- and volume-based measure of driving on roadways for all motorized vehicle types—car, bus, motorcycle, and truck—on an average day. Per capita VMT is the same measure divided by the same area's population for the same year. Per vehicle VMT divides VMT by the number of household vehicles available by residents of that geography in the same year. These three value types can be selected in the dropdown in the first chart below. Use the legend items to explore various geographies. The second chart below shows per capita and total personal vehicles available to the region’s households from the American Community Survey.

Normalizing VMT by a county or region's population, or household vehicles, is helpful for context, but does not have complete parity with what is measured in VMT estimates. People and vehicles come into the region from other places, just as people and vehicles leave the region to visit other places. VMT per capita compares all miles traveled on the region's roads to the region's population (for all ages) from the U.S. Census Bureau's latest population estimates. Vehicle counts for VMT are classified by vehicle types, but not by vehicle ownership. In 2017, statewide estimates for VMT by motorcycles, passenger cars, and two-axle single-unit trucks with four wheels made up 88% of Pennsylvania's VMT, and 95% of New Jersey's. These vehicle types are highly likely to be personal vehicles, owned by households, but a small percent could be fleet vehicles of companies or governments. The remaining VMT is made up of vehicle types like school and commercial buses and trucks with more than two axles so they are highly likely to be commercial vehicles.

It is a Case study For a Cyclistic company. Scenario: You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

In financial year 2025, two-wheeler sales in India saw an increase from the previous year's to *****million units. The sales reached an all-time high as of 2019, when India's auto industry sold some ***million units. This figure is almost double the 2011 sales, when just ******million two-wheeler units were sold in India. Two-wheeler industry in India In 2024, two-wheeler vehicles also make up the majority of vehicles in production in India, followed by passenger vehicles. There are many kinds of two-wheeler vehicles available in India, including scooters, motorcycles, and mopeds. As of 2025, the leading two-wheeler manufacturer in India was *************. The manufacturer of the very popular Splendor motorcycle model is headquartered in India's capital, New Delhi. Major automotive brands in India Motor vehicle sales in India have doubled between 2008 and 2018, and saw a dip in 2019. Among passenger cars, Maruti Suzuki, followed by Hyundai Motor of India, holds a majority of the market. India’s best-selling cars in 2024 included models such as Wagon R, Ertiga and Brezza, to name a few, which were all from Maruti Suzuki.

The dataset is publicly available at and has been made available by Motivate International Inc. under this License. Each file contains a table with 13 columns and hundreds of thousands of observations. Each column represents the following fields; • ride_id • rideable_type • started_at • ended_at • start_station_name • start_station_id • end_station_name • end_station_id • start_lat • start_lng • end_lat • end_lng • member casual

This annual release provides a snapshot of the number of active vehicle registration counts of light-duty vehicles and medium-duty vehicles by type of vehicle and fuel type, heavy-duty vehicles, buses, and motorcycles and mopeds. Data are obtained from the administrative files from provincial and territorial governments.

To demonstrate my skills gained from Google Data Analytics Professional Certificate, this case study is part of the last course (Google Data Analytics Capstone: Complete a Case study). I have used the Six Steps in Data Analytics (Ask, Prepare, Process, Analyze, Share, and Act) to complete this project.

Context

** One analysis Done in spreadsheets with 202004 and 202005 data **

Content

To adjust for outlier Ride lengths like the max and min below: Max RL =MAX(N:N)978:40:02 minimum RL =MIN(N:N)-0:02:56

TRIMMean to shave off the top and bottom of a dataset. TRIMMEAN =TRIMMEAN(N:N,5%)0:20:20 =TRIMMEAN(N:N,2%)0:21:27

Otherwise the Ride length for 202004 is Average RL 0:35:51

The most common day of the week is Sunday. There are 61,148 members and 23,628 casual riders. mode of DOW 1 CountIf member of MC 61148 CountIf casual of MC 23628

Pivot table 1 2020-04 member_casual AVERAGE of ride_length

Same calculations for 2020-05 Average RL 0:33:23 Max RL 481:36:53 minimum RL -0:01:48 mode of DOW 7 CountIf member of MC 113365 CountIf casual of MC 86909 TRIMMEAN 0:25:22 0:26:59

There are 4 pivot tables included in seperate sheets for other comparisons.

Acknowledgements

I gathered this data using the sources provided by the Google Data Analytics course. All work seen is done by myself.

Inspiration

I want to further use the data in SQL, and Tableau.