at what station did the bike trip with rental id

Course 3 – Prepare Data for Exploration Quiz Answers

Week 3: databases: where data lives, google data analytics professional certification, complete coursera study guide, table of content.

• INTRODUCTION
• TEST YOUR KNOWLEDGE ON WORKING WITH DATABASES
• TEST YOUR KNOWLEDGE ON METADATA
• TEST YOUR KNOWLEDGE ON ACCESSING DATA SOURCES
• TEST YOUR KNOWLEDGE ON SORTING AND FILTERING
• WORKING WITH LARGE DATASETS IN SQL
• CREATE A CUSTOM TABLE IN BIGQUERY
• TEST YOUR KNOWLEDGE ON USING SQL WITH LARGE DATASETS
• WEEKLY CHALLENGE 3

DATABASES: WHERE DATA LIVES – INTRODUCTION

Databases are the backbone of data analysis. With a Google Analytics Certification from Coursera, you’ll be able to access the data that lives in databases and use it for your own purposes. You will learn how to extract, filter, and sort this data and work with metadata. All of these skills are necessary for effective data analysis and can help you gain insights into trends and other important information.

Databases offer a wealth of knowledge and understanding that is essential for any analyst’s toolkit. The Google Data Analytics certification from Coursera will equip you with the necessary skills to navigate through databases easily and efficiently so you can make sense of the vast amounts of data available today.

Test your knowledge on working with databases

1. fill in the blank: a _____ is an identifier that references a database column in which each value is unique..

• foreign key
• primary key (Correct)

Correct: A primary key is an identifier that references a column in which each value is unique. A foreign key is a field within a table that’s a primary key in the original table.

2. Fill in the blank: A relational database contains a series of _____ that can be connected to form relationships.

• spreadsheets
• tables (Correct)

Correct: A relational database contains a series of tables that can be connected to form relationships.

3. A key benefit of working with normalized databases is that they help lower data redundancy. Which of the following is an example of redundancy?

• The same piece of data being stored in two different places (Correct)
• A database that forms two or more relationships
• Team members in different office locations working with the same data
• A database containing two foreign keys

Correct: The same piece of data being stored in two different places is an example of redundancy.

Test your knowledge on metaData

1. a large company has several data collections across its many departments. what kind of metadata indicates exactly how many collections a piece of data lives in.

• Structural (Correct)
• Administrative
• Descriptive
• Representative

Correct: Structural metadata indicates exactly how many collections data lives in. It provides information about how a piece of data is organized and whether it’s part of one, or more than one, data collection.

2. The date and time a photo was taken is an example of which kind of metadata?

• Administrative (Correct)

Correct: The date and time a photo was taken is an example of administrative metadata. Administrative metadata indicates the technical source and details for a digital asset.

3. A large metropolitan high school gives each of its students an ID number to differentiate them in its database. What kind of metadata are the ID numbers?

• Descriptive (Correct)

Correct: The ID numbers are descriptive metadata. Descriptive metadata describes a piece of data or can be used to identify it at any time.

4. A company needs to merge third-party data with its own data. Which of the following actions will help make this process successful? Select all that apply.

• Replace the incoming data’s metadata with its own company metadata.
• Use the metadata to standardize the data. (Correct)
• Alter the company’s metadata to more closely reflect the incoming metadata.
• Use the metadata to evaluate the third-party data’s quality and credibility. (Correct)

Correct: The company can use the metadata to standardize the data and evaluate the third-party data’s quality and credibility.

Test your knowledge on accessing data sources

1. a csv file saves data in a table format. what does csv stand for.

• Compatible scientific variables
• Calculated spreadsheet values
• Comma-separated values (Correct)
• Cell-structured variables

Correct: CSV stands for comma-separated values.

2. A data analyst wants to bring data from a CSV file into a spreadsheet. This is an example of what process?

• Importing data (Correct)
• Filing data
• Editing data
• Normalizing data

Correct: A data analyst bringing data from a CSV file into a spreadsheet is an example of importing data.

3. A CSV file makes it easier for data analysts to complete which tasks? Select all that apply.

• Distinguish values from one another (Correct)
• Import data to a new spreadsheet (Correct)
• Manage multiple tabs within a worksheet
• Examine a small subset of a large dataset (Correct)

Correct: A CSV file makes it easier for data analysts to examine a small part of a large dataset, import data to a new spreadsheet, and distinguish values from one another.

4. What is the process of showing only the data that meets a specified criteria while hiding the rest?

• Filtering (Correct)

Correct: Filtering is the process of showing only the data that meets a specified criteria while hiding the rest. Filtering is an extremely useful technique for data cleaning, and an essential tool in every data analyst’s toolkit.

Test your knowledge on sorting and filtering

1. what is the process for arranging data into a meaningful order to make it easier to understand, analyze, and visualize.

• Sorting (Correct)
• Prioritizing

Correct: Sorting is the process for arranging data into a meaningful order to make it easier to understand, analyze, and visualize.

2. A data analyst is reviewing a national database of real estate sales. They are only interested in sales of condominiums. How can the analyst narrow their scope?

• Sort by condominium sales
• Filter out condominium sales
• Sort by non-condominium sales
• Filter out non-condominium sales (Correct)

Correct: The analyst can narrow their scope by filtering out non-condominium sales. This will enable them to view only the data on condominium sales.

3. A data analyst works for a rental car company. They have a spreadsheet that lists car ID numbers and the dates cars were returned. How can they sort the spreadsheet to find the most recently returned cars?

• By return date, in descending order (Correct)
• By car numerical ID, in descending order
• By return date, in ascending order
• By car numerical ID, in ascending order

Correct: To sort the spreadsheet to quickly find the most recently returned cars, they should sort by return date, in descending order.

4. Fill in the blank: To keep a header row at the top of a spreadsheet, highlight the row and select _____ from the View menu.

• Freeze (Correct)

Correct: To keep a header row at the top of a spreadsheet, highlight the row and select freeze from the View menu.

Working with large datasets in Sql

1. run another query on your table:.

     end_station_name

    `bigquery-public-data.london_bicycles.cycle_hire`

     rental_id = 57635395;

At what station did the bike trip with rental_id 57635395 end?

• Southwark Street, Bankside
• Tower Gardens, Tower
• Notting Hill Gate Station, Notting Hill
• East Village, Queen Elizabeth Olympic Park (Correct)

Correct: The address listed under the end_station_name column for Row 1 of your results table was East Village, Queen Elizabeth Olympic Park. To find this, you successfully ran a query. Going forward, you will continue using SELECT, FROM, and WHERE statements in your queries to interact with databases using SQL. This will help you build more complicated SQL queries when you are analyzing data in the future.

Create a custom table in BigQuery

1. after running the query on your new table, what was the third most popular baby name for boys in 2014.

• Mason (Correct)

Correct: To find that Mason was the third most popular baby name for boys in 2014, you queried your custom table and checked the results. Going forward, you’ll be able to upload your own data sources into BigQuery for future analysis projects. This will allow you to practice writing SQL queries for more data sources, which will be a key skill as a data analyst.

Test your knowledge on using SQL with large datasets

1. in mysql, what is acceptable syntax for the select keyword select all that apply..

• select (Correct)
• SELECT (Correct)

Correct: In MySQL, SELECT or select is acceptable syntax.

2. A database table is named blueFlowers. What type of case is this?

• Camel case (Correct)
• Sentence case

Correct: blueFlowers is in camel case.

3. In BigQuery, what optional syntax can be removed from the following FROM clause without stopping the query from running?

From `bigquery-public-data.sunroof_solar.solar_potential_by_postal_code`.

• Underscores
• Backticks (Correct)

Correct: The name of the dataset is shown between two backticks to help people read the query more easily. If you remove the backticks, the query will still run.

4. In the following FROM clause, what is the table name in the SQL query?

Bigquery-public-data.sunroof_solar.solar_potential_by_postal_code.

• public-data.sunroof
• solar.solar
• sunroof_solar
• solar_potential_by_postal_code (Correct)

Correct: The table name in the SQL query is solar_potential_by_postal_code. This table is in the sunroof_solar dataset, a public dataset in BigQuery.

GOOGLE DATA ANALYTICS COURSERA ANSWERS AND STUDY GUIDE

Liking our content? Then don’t forget to add us to your bookmarks so you can find us easily!

Weekly Breakdown | Google Study Guides | Back to Top

Prepare Data for Exploration Weekly Challenge 3

1. primary and foreign keys are two connected identifiers within separate tables. these tables exist in what kind of database.

• Relational (Correct)

Correct: Primary and foreign keys are two connected identifiers within separate tables in a relational database.

2. When working with data from an external source, what can metadata help data analysts do? Select all that apply.

• Ensure data is clean and reliable (Correct)
• Choose which analyses to run
• Understand the contents of a database (Correct)
• Combine data from more than one source (Correct)

Correct: Metadata helps data analysts understand the contents of a database, ensure data is clean and reliable, and understand how to combine data from more than one source.

3. Think about data as a student at a high school. In this metaphor, which of the following are examples of metadata? Select all that apply.

• Classes the student is enrolled in (Correct)
• Student’s ID number (Correct)
• Student’s enrollment date (Correct)
• Grades the student earns

Correct: The student ID number, enrollment date, and classes the student is enrolled in represent structural metadata.

4. Fill in the blank: Data governance is the process of ensuring that a company’s _____ are managed in a formal manner.

• business tasks
• data assets (Correct)
• data engineers
• business strategies

Correct: Data governance is the process of ensuring that a company’s data assets are managed in a formal manner.

5. What are some key benefits of using external data? Select all that apply.

• External data has broad reach. (Correct)
• External data can provide industry-level perspectives. (Correct)
• External data is always reliable.
• External data is free to use.

Correct: Some key benefits of using external data are that it has a broad reach and it provides industry-level perspectives.

6. A data analyst reviews a database of Wisconsin car sales to find the last car models sold in Milwaukee in 2019. How can they sort and filter the data to return the last five cars sold at the top of their list? Select all that apply.

• Sort by sale date in ascending order
• Filter out sales not in 2019 (Correct)
• Sort by sale date in descending order (Correct)
• Filter out sales outside of Milwaukee (Correct)

Correct: The analyst can filter out sales outside of Milwaukee in 2019 and sort by date in descending order.

7. When writing a query, the name of the dataset can either be inside two backticks, or not, and the query will still run properly.

• True (Correct)

Correct: When writing a query, the name of the dataset can either be inside two backticks, or not, and the query will still run properly.

8. You are working with a database table that contains customer data. The first_name column lists the first name of each customer. You are only interested in customers with the first name Mark.

You write the sql query below. add a where clause that will return only customers named mark..

9. How many customers are named Mark?

• 2 (Correct)

Correct: The clause WHERE first_name = ‘Mark’ will return only customers named Mark. The complete query is SELECT * FROM customer WHERE first_name = ‘Mark’. The WHERE clause filters results that meet certain conditions. The WHERE clause includes the name of the column, an equals sign, and the value(s) in the column to include. Place quotes around text values. There are two customers named Mark.

10. Relational databases contain a series of tables connected to form relationships. Which two types of fields exist in two connected tables?

• Primary and foreign keys (CORRECT)
• Internal and external data
• Descriptive and structural metadata
• Star and snowflake schemas

Correct: Primary and foreign keys exist in two connected tables in a relational database.

11. Data analysts use metadata for what tasks? Select all that apply.

• To perform data analyses
• To evaluate the quality of data (CORRECT)
• To interpret the contents of a database (CORRECT)
• To combine data from more than one source (CORRECT)

Correct: Data analysts use metadata to combine data, evaluate data, and interpret a database. Correct: Data analysts use metadata to combine data, evaluate data, and interpret a database. Correct: Data analysts use metadata to combine data, evaluate data, and interpret a database.

12. Structural metadata indicates how a piece of data is organized and whether it’s part of one or more than one data collection.

• True (CORRECT)

Correct: Structural metadata indicates how a piece of data is organized and whether it’s part of one, or more than one, data collection.

12. What is the process that data analysts use to ensure the formal management of their company’s data assets?

• Data mapping
• Data governance (CORRECT)
• Data aggregation
• Data integrity

Correct: Data governance is the process of ensuring the formal management of a company’s data assets.

13. A data analyst chooses not to use external data because it represents diverse perspectives. This is an appropriate decision when working with external data.

• False (CORRECT)

Correct: External data representing diverse perspectives is not an appropriate reason to choose not to use a dataset. A data analyst might choose not to use external data if the it cannot be confirmed to be reliable.

14. A data analyst reviews a database of Wisconsin car sales to find the last car models sold in Milwaukee in 2019. How can they sort and filter the data to return the last five cars sold at the top of their list? Select all that apply.

• Sort by sale date in descending order (CORRECT)
• Filter out sales outside of Milwaukee (CORRECT)
• Filter out sales not in 2019 (CORRECT)

Correct: The analyst can filter out sales outside of Milwaukee in 2019 and sort by date in descending order. Correct: The analyst can filter out sales outside of Milwaukee in 2019 and sort by date in descending order. Correct: The analyst can filter out sales outside of Milwaukee in 2019 and sort by date in descending order.

15. Think about data as driving a taxi cab. In this metaphor, which of the following are examples of metadata? Select all that apply.

• Passengers the taxi picks up
• Make and model of the taxi cab (CORRECT)
• License plate number (CORRECT)
• Company that owns the taxi (CORRECT)

Correct: The license plate number, make and model of the cab, and the company that owns the taxi represent structural metadata. Correct: The license plate number, make and model of the cab, and the company that owns the taxi represent structural metadata. Correct: The license plate number, make and model of the cab, and the company that owns the taxi represent structural metadata.

16. What are some key benefits of using external data? Select all that apply.

• External data can provide industry-level perspectives. (CORRECT)
• External data has broad reach. (CORRECT)

Correct: Some key benefits of using external data are that it has a broad reach and it provides industry-level perspectives. Correct: Some key benefits of using external data are that it has a broad reach and it provides industry-level perspectives.

17. You are working with a database table that contains customer data. The city column lists the city where each customer is located. You want to find out which customers are located in Berlin.

You write the SQL query below. Add a WHERE clause that will return only customers located in Berlin. SELECT * FROM customer

How many customers are located in Berlin?

• 2 (CORRECT)

18. A data analyst reviews a national database of movie theater showings. They want to find the first movies shown in San Francisco in 2001. How can they organize the data to return the first 10 movies shown at the top of their list? Select all that apply.

• Sort by date in descending order
• Sort by date in ascending order (CORRECT)
• Filter out showings outside of San Francisco (CORRECT)
• Filter out showings not in 2001 (CORRECT)

Correct: The analyst can filter out showings outside of San Francisco in 2001 and sort by date in ascending order.

19. A nonprofit maintains a list of how many laptops they provide to each school in the county. In the table, there is a column called number_of_laptops. A data analyst wants to determine which schools were given the fewest laptops. How should they sort the data to return these schools first?

• Sort numerically in descending order
• Sort alphabetically in ascending order
• Sort numerically in ascending order (CORRECT)
• Sort alphabetically in descending order

Correct: The data analyst should sort the number_of_laptops column numerically in ascending order.

20. When writing a query, you must remove the two backticks around the name of the dataset in order for the query to run properly.

21. fill in the blank: data _ is the process of ensuring the formal management of a company’s data assets..

• aggregation
• governance (CORRECT)

22. In what circumstance might a data analyst choose not to use external data in their analysis?

• The data cannot be confirmed to be reliable (CORRECT)
• The data is free for anyone to access
• The data represents diverse perspectives
• The data is too thorough

Correct: A data analyst might choose not to use external data in their analysis if the data cannot be confirmed to be reliable.

databases: where data lives – conclusion

Many people view databases as daunting and overwhelming, but they can be a powerful asset with the proper tools and training. The Google Analytics Certification from Coursera will give you the skills you need to work with databases so you can extract data that is important for your needs. This certification will also teach you how to filter and sort this data so you can make trends and other insights. With this valuable skill set, you’ll be able to access the information available in databases and use it to your advantage. Join Coursera today and get started on your journey to becoming a certified data analyst.

Subscribe to our site

Get new content delivered directly to your inbox.

Enter your email address

Quiztudy Top Courses

Popular in coursera.

• Google Advanced Data Analytics
• Google Cybersecurity Professional Certificate
• Meta Marketing Analytics Professional Certificate
• Google Digital Marketing & E-commerce Professional Certificate
• Google UX Design Professional Certificate
• Meta Social Media Marketing Professional Certificate
• Google Project Management Professional Certificate
• Meta Front-End Developer Professional Certificate

Liking our content? Then, don’t forget to ad us to your BOOKMARKS so you can find us easily!

Liking our Study Guides, Subscribe Now!!!

Get hired - read our latest posts, the latest's study guides in the market for you to accelerate your progress, and achieve your goals.

Unleash Your Inner Geek on a Bike

At What Station Did the Bike Trip With Rental_Id 57635395 End?

Shahed Parvej

Affiliate Disclaimer

As an affiliate, we may earn a commission from qualifying purchases. We get commissions for purchases made through links on this website from Amazon and other third parties.

As an Amazon Associate, I earn from qualifying purchases.

The bike trip with rental ID 57635395 ended at the East Village, Queen Elizabeth Olympic Park station. In this introduction, we will explore the specific location where the bike trip with rental ID 57635395 came to an end.

By providing a concise answer to this question, we will delve into the details of this particular station in Austin, Texas, United States. Understanding the end point of the bike trip is essential for anyone looking to navigate this area or gather accurate information about bike rental routes.

With the information provided, readers will gain a comprehensive understanding of the bike trip’s final destination and the specific station where it concluded.

Credit: www.queenelizabetholympicpark.co.uk

Exploring The Rental_id 57635395

The bike trip with Rental_Id 57635395 ended at the East Village, Queen Elizabeth Olympic Park station in Austin, Texas, United States.

Brief Introduction To The Bike Trip With Rental_id 57635395

The bike trip with Rental_Id 57635395 was an exciting adventure that took place in the beautiful city of Austin, Texas, United States. This rental experience provided the opportunity to explore the city’s vibrant streets, scenic routes, and memorable destinations.

Contextual Information About The Bike Trip And Its Relevance

When it comes to exploring a new city, renting a bike can be a fantastic way to immerse yourself in the local culture and discover hidden gems. Rental_Id 57635395 offered a seamless and convenient way for travelers to explore Austin’s attractions, parks, and landmarks at their own pace. From cruising along the picturesque Lady Bird Lake to pedaling through the lively downtown area, this bike trip provided an unforgettable experience.

With Rental_Id 57635395, visitors could easily navigate through Austin’s bike-friendly paths and trails, accessing popular spots such as Zilker Park, the Texas State Capitol, and the iconic South Congress Avenue. Whether you’re a history enthusiast, an outdoor lover, or a foodie looking to indulge in local cuisines, this bike trip catered to a variety of interests and preferences.

By opting for Rental_Id 57635395, travelers had the flexibility to stop and explore intriguing neighborhoods like East Village, Queen Elizabeth Olympic Park, and enjoy the stunning views of the city’s skyline. This personalized experience added a sense of adventure and freedom to the bike trip, allowing riders to create their own unique itinerary.

Overall, the bike trip with Rental_Id 57635395 was a perfect choice for those seeking an active and immersive way to discover Austin. It provided a convenient and eco-friendly transportation option, enabling visitors to effortlessly explore the city’s diverse attractions and capture beautiful memories along the way.

Beginnings Of The Bike Trip

The bike trip with rental ID 57635395 began at a station in Austin, Texas, United States. Austin is a vibrant city known for its bike-friendly culture and beautiful outdoor trails. The starting station provided convenient access to the city’s most popular bike routes and scenic paths, allowing riders to immerse themselves in the natural beauty of the area while enjoying a thrilling biking experience.

Once the bike trip commenced at the Austin station, riders had a multitude of route options to explore. One popular route was the Queen Elizabeth Olympic Park in the East Village, offering a mix of well-paved paths and picturesque landscapes. Cyclists could pedal through the park’s green spaces, taking in the iconic sights and enjoying a refreshing breeze.

• Start station: Austin, Texas
• Route: Queen Elizabeth Olympic Park in the East Village
• Highlights: Scenic paths, iconic sights, green spaces
• Destination: Currently unknown

As the bike trip continued, riders had the opportunity to soak in the beauty of Austin’s surroundings. The route showcased the city’s natural landscapes, allowing cyclists to witness the serene beauty of Texas. The bike-friendly infrastructure in Austin ensured a smooth and enjoyable journey, with designated bike lanes and clear signage to guide riders along the way.

While the ending station of the bike trip with rental ID 57635395 is not yet known, the journey itself was undoubtedly filled with excitement, exploration, and unforgettable memories. From the vibrant streets of Austin to the tranquil paths of Queen Elizabeth Olympic Park, this bike trip offered a unique and enriching experience for riders of all levels.

Highlights Of The Bike Trip

The bike trip with rental ID 57635395 ended at the East Village in Queen Elizabeth Olympic Park, Austin, Texas.

Notable Landmarks Or Points Of Interest During The Bike Trip

• The majestic Golden Gate Bridge, a symbol of San Francisco’s beauty and engineering marvel.
• The historic Alamo Square Park, home to the famous Painted Ladies Victorian houses.
• Explore the vibrant Mission District, known for its colorful murals and trendy shops.
• Experience the beauty of Central Park in New York City, with its sprawling green spaces and iconic landmarks.
• Discover the iconic Statue of Liberty, a symbol of freedom and a must-visit landmark in New York.
• Wander through the charming Georgetown neighborhood of Washington, D.C., filled with historic architecture and boutique shops.
• Marvel at the breathtaking views of Chicago’s skyline from Navy Pier.

Interesting Experiences Or Encounters Along The Way

• Engage in a friendly conversation with locals, who can provide insider tips and recommendations.
• Witness street performers showcasing their talent in city squares and parks.
• Indulge in delicious street food, sampling the flavors of each city’s unique culinary scene.
• Chance upon hidden gem cafes and boutique stores, perfect for a quick rest and exploration.
• Join a guided tour or a group of fellow cyclists, sharing stories and experiences.
• Discover vibrant street art, adding a splash of color to urban landscapes.
• Explore local markets, where you can find handmade crafts and fresh produce.

Tracking The Progress

The bike trip with Rental_Id 57635395 ended at the East Village in Queen Elizabeth Olympic Park, Austin, Texas, United States.

Methods Used To Monitor The Bike Trip’s Progress

Possible checkpoints or tracking systems, nearing the end.

The bike trip with rental ID 57635395 ended at the East Village in Queen Elizabeth Olympic Park.

Signs Indicating Proximity To The End Station

As you near the end station of your bike trip with Rental_Id 57635395, keep an eye out for these signs that will indicate you’re getting closer:

• Increased frequency of station signs and directions
• Distance markers that countdown the remaining distance to the end station
• Clearer and more prominent signage for the end station
• Map displays showing your current location and the location of the end station

Changes In Surroundings Or Landmarks Indicating The End Is Near

Pay attention to these noticeable changes in the surroundings or landmarks, which suggest that you’re approaching the end station:

• A shift in the type of buildings or architecture
• Increased pedestrian traffic and activity
• Appearance of landmarks specific to the end station or its vicinity
• Transition from open spaces to more urbanized areas

Arrival At The End Station

After an exciting bike trip with rental ID 57635395, it’s time to discover where the journey comes to an end. The end station is a crucial point that marks the completion of your adventure and offers a chance to unwind and reflect on the memorable experiences.

Description Of The End Station

The end station for this bike trip is located in East Village, Queen Elizabeth Olympic Park. This vibrant neighborhood is situated in the heart of Austin, Texas, United States. As you reach the end station, you’ll be greeted by the lively atmosphere and a beautiful view of the city skyline.

The East Village is known for its modern architecture, green spaces, and a mix of cultural and recreational activities. You’ll find yourself surrounded by stylish buildings, bustling streets, and a sense of energy that defines the spirit of Austin.

Possible Amenities Or Facilities At The End Station

Arriving at the end station, you’ll have access to various amenities and facilities to ensure your comfort and convenience. Here are some of the amenities you can expect:

• Secure Bike Parking: A designated area where you can safely park your rented bike.
• Restrooms: Clean and well-maintained restroom facilities for your convenience.
• Food and Refreshments: Nearby cafes and restaurants where you can grab a bite or enjoy a refreshing drink.
• Picnic Areas: Spacious green areas, perfect for picnics or relaxing after your bike trip.
• Information Center: A helpful information center where you can gather more details about the area and nearby attractions.
• Bike Repair Service: In case you encounter any issues with your rented bike, there will be a bike repair service available to assist you.

These amenities aim to enhance your experience at the end station, ensuring that you have everything you need to make the most of your time there. Whether you’re looking for a quick refreshment, a place to relax, or useful information, the end station in East Village, Queen Elizabeth Olympic Park has it all.

Conclusion And Reflection

The bike trip with Rental_Id 57635395 ended at East Village, Queen Elizabeth Olympic Park in Austin, Texas, United States. Get the exact result on google. com.

Recap Of The Bike Trip With Rental_id 57635395

Let’s start our conclusion and reflection with a quick recap of the bike trip with Rental_Id 57635395. This particular bike trip took place in the beautiful city of Austin, Texas, United States. The rental_id 57635395 allowed us to explore the city on two wheels, immersing ourselves in the sights, sounds, and culture of this vibrant city.

During the bike trip, we traveled through various neighborhoods and iconic landmarks of Austin, including the picturesque East Village and the Queen Elizabeth Olympic Park. The rental_id 57635395 provided us with a convenient and efficient means of transportation, enabling us to cover more ground and discover hidden gems along the way.

Personal Reflection Or Thoughts On The Overall Experience

Reflecting on the overall bike trip experience with Rental_Id 57635395, I can’t help but feel a sense of joy and satisfaction. Exploring a city on a bike offers a unique perspective, allowing you to connect with the surroundings in a more intimate and immersive way. It also enables you to discover local attractions, parks, and cafes that you might not have stumbled upon otherwise.

The combination of the rental_id 57635395 and the city of Austin was a match made in heaven. The bike-friendly infrastructure, along with the stunning scenery and friendly locals, made it a truly unforgettable experience. Pedaling through the city streets, feeling the wind in my hair, and seeing the vibrant colors of Austin pass by brought a sense of freedom and liberation.

Moreover, the bike trip offered a convenient and eco-friendly way to get around, reducing our carbon footprint while enjoying the outdoors. It reminded me of the importance of sustainable transportation options and how they can contribute to creating greener and healthier cities.

Overall, the bike trip with Rental_Id 57635395 was an incredible adventure. It allowed me to explore Austin from a fresh perspective, immerse myself in its culture, and experience the city’s unique charm. It was not just a means of transportation but a way to create lasting memories and connections with the city and its people.

Frequently Asked Questions For At What Station Did The Bike Trip With Rental_id 57635395 End?

What kind of metadata are the id numbers.

ID numbers serve as unique identifiers for assets, providing descriptive metadata that is used for identification and discovery purposes. This can include information like ISBNs.

What Is The Process For Arranging Data Into A Meaningful Order To Make It Easier To Understand?

Data is arranged into a meaningful order through a process called data sorting. This process helps to make the data easier to understand, analyze, and visualize. Sorting is commonly used to organize data in a way that tells a clear story and facilitates data interpretation.

Is An Identifier That References A Database Column In Which Each Value Is Unique?

A primary key is an identifier that refers to a database column with unique values.

To conclude, the bike trip with rental ID 57635395 ended at the East Village, Queen Elizabeth Olympic Park station. This station serves as the final destination for the bike journey taken. With its unique identifiers and relational database connections, the end station was determined through the ROW_NUMBER() function.

Overall, the analysis of this trip highlights the importance of descriptive metadata and data sorting in understanding and visualizing data effectively.

About the author

I’m Shahed Parvej, the proud owner and founder of this platform. As a passionate Bike Enthusiast, I’ve embarked on a journey to share my knowledge, experiences, and insights with fellow enthusiasts, newcomers, and everyone intrigued by the world of biking.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Latest posts

Discover the Ultimate Best Bike Brake in 2024 for Unbeatable Cycling Performance

As an Amazon Associate, I earn from qualifying purchases.Looking for the best bike brake in 2024? The shimano xtr m9100 disc brake offers top-notch performance, reliability, and precision, making it a standout choice for cyclists.   The importance of a reliable bike brake cannot be overstated, especially when it comes to ensuring safety on the…

Can You Put 27 5 Wheels On A 29Er: Unveiling the Ultimate Upgrade

As an Amazon Associate, I earn from qualifying purchases.Yes, you can put 27.5 wheels on a 29er bicycle as long as the frame and brakes allow for the smaller wheel size. Swapping to smaller wheels can improve agility and maneuverability on rough terrain.   It’s essential to consider the impact on the bike’s geometry, bottom…

What is a Wet Bike

As an Amazon Associate, I earn from qualifying purchases.A Wet Bike is a planing motorized watercraft that combines the features of a motorcycle and a jet ski. It is propelled by pump jets, similar to other personal watercraft. The original Wetbike was introduced by Spirit Marine and marketed as a “water motorcycle” with the thrill…

• More Networks

• Data Analysis

System Data

Where do Citi Bikers ride? When do they ride? How far do they go? Which stations are most popular? What days of the week are most rides taken on? We've heard all of these questions and more from you, and we're happy to provide the data to help you discover the answers to these questions and more. We invite developers, engineers, statisticians, artists, academics and other interested members of the public to use the data we provide for analysis, development, visualization and whatever else moves you.

This data is provided according to the Citi Bike Data Use Policy .

Citi Bike Trip Histories

We publish downloadable files of Citi Bike trip data . The data includes:

• Rideable type
• Start station name
• Start station ID
• End station name
• End station ID
• Start latitude
• Start longitude
• End latitude
• End Longitude
• Member or casual ride

Data format previously:

• Trip Duration (seconds)
• Start Time and Date
• Stop Time and Date
• Start Station Name
• End Station Name
• Station Lat/Long
• User Type (Customer = 24-hour pass or 3-day pass user; Subscriber = Annual Member)
• Gender (Zero=unknown; 1=male; 2=female)
• Year of Birth

This data has been processed to remove trips that are taken by staff as they service and inspect the system, trips that are taken to/from any of our “test” stations (which we were using more in June and July 2013), and any trips that were below 60 seconds in length (potentially false starts or users trying to re-dock a bike to ensure it's secure).

Please be aware of your software program’s row limitations as you are viewing the data. Many of the CSV files contain more than 1 million rows. After downloading, you will need to use a large data tool / visualizer (like Tableau, Alteryx, R, or others) to view and analyze the full data sets.

Download Citi Bike trip history data

Real-Time Data

Citi Bike publishes real-time system data in General Bikeshare Feed Specification format. Get the GBFS feed here .

Monthly Operating Reports

View the monthly operating reports that we provide to the NYC Department of Transportation.

Additional Resources

• The City of New York's bicycling data
• A group of software developers and data explorers working with data feeds from NYC's Bike Share system and other bike data maintain this Google Group (note: Citi Bike is not responsible for this group – it is run and maintained by a group of interested private citizens)

• Español – América Latina
• Português – Brasil
• Documentation

Run a query with GoogleSQL

Run a query with GoogleSQL.

Explore further

For detailed documentation that includes this code sample, see the following:

• BigQuery API Client Libraries
• Run a query

Code sample

Before trying this sample, follow the C# setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery C# API reference documentation .

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Go API reference documentation .

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Java API reference documentation .

Before trying this sample, follow the Node.js setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Node.js API reference documentation .

Before trying this sample, follow the PHP setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery PHP API reference documentation .

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Python API reference documentation .

Before trying this sample, follow the Ruby setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Ruby API reference documentation .

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser .

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Instantly share code, notes, and snippets.

yihyang / 01 - Introduction to SQL for BigQuery and Cloud SQL

• Download ZIP
• Star 2 You must be signed in to star a gist
• Fork 2 You must be signed in to fork a gist
• Embed Embed this gist in your website.
• Share Copy sharable link for this gist.
• Clone via HTTPS Clone using the web URL.
• Learn more about clone URLs
• Save yihyang/1163bf9443d5acee61c100cfdfeb568b to your computer and use it in GitHub Desktop.

• Select Language English Afrikaans Albanian Amharic Arabic Armenian Azerbaijani Basque Belarusian Bengali Bosnian Bulgarian Catalan Cebuano Chichewa Chinese (Simplified) Chinese (Traditional) Corsican Croatian Czech Danish Dutch Esperanto Estonian Filipino Finnish French Frisian Galician Georgian German Greek Gujarati Haitian Creole Hausa Hawaiian Hebrew Hindi Hmong Hungarian Icelandic Igbo Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Kurdish (Kurmanji) Kyrgyz Lao Latin Latvian Lithuanian Luxembourgish Macedonian Malagasy Malay Malayalam Maltese Maori Marathi Mongolian Myanmar (Burmese) Nepali Norwegian Pashto Persian Polish Portuguese Punjabi Romanian Russian Samoan Scots Gaelic Serbian Sesotho Shona Sindhi Sinhala Slovak Slovenian Somali Spanish Sundanese Swahili Swedish Tajik Tamil Telugu Thai Turkish Ukrainian Urdu Uzbek Vietnamese Welsh Xhosa Yiddish Yoruba Zulu

The City of Philadelphia and its partners at Bicycle Transit Systems are pleased to share anonymized Indego trip data with the public. The privacy of Indego passholders and users is very important to us, and information won’t be released unless the City can ensure that the identities of individuals or groups cannot be discerned and that their privacy is not compromised.

Toss it, flip it, turn it inside out, combine it with your own or someone else’s data! Tell us how many trips happened in the rain, or after midnight, or below 50 degrees – we want your help to delve into the data and reveal the hidden gems of understanding that will allow us to make Indego the best it can be.

• 2024 Q1 (January – March)
• 2023 Q4 (October – December)
• 2023 Q3 (July – September)
• 2023 Q2 (April – June)
• 2023 Q1 (January – March)
• 2022 Q4 (October – December)
• 2022 Q3 (July – September)
• 2022 Q2 (April – June)
• 2022 Q1 (January – March)
• 2021 Q4 (October – December)
• 2021 Q3 (July – September)
• 2021 Q2 (April – June)
• 2021 Q1 (January – March)
• 2020 Q4 (October – December)
• 2020 Q3 (July – September)
• 2020 Q2 (April – June)
• 2020 Q1 (January – March)
• 2019 Q4 (October – December)
• 2019 Q3 (July – September)
• 2019 Q2 (April – June)
• 2019 Q1 (January – March)
• 2018 Q4 (October – December)
• 2018 Q3 (July – September)
• 2018 Q2 (April – June)
• 2018 Q1 (January – March)
• 2017 Q4 (October – December)
• 2017 Q3 (July – September)
• 2017 Q2 (April – June)
• 2017 Q1 (January – March)
• 2016 Q4 (October – December)
• 2016 Q3 (July – September)
• 2016 Q2 (April – June)
• 2016 Q1 (January – March)
• 2015 Q4 (October – December)
• 2015 Q3 (July – September)
• 2015 Q2 (Launch, April – June)

Data Format

Each .csv file contains data for one quarter of the year. Each file contains the following data points:

• trip_id: Locally unique integer that identifies the trip
• duration:  Length of trip in minutes
• start_time:  The date/time when the trip began, presented in ISO 8601 format in local time
• end_time: The date/time when the trip ended, presented in ISO 8601 format in local time
• start_station:  The station ID where the trip originated (for station name and more information on each station see the Station Table )
• start_lat:  The latitude of the station where the trip originated
• start_lon:  The longitude of the station where the trip originated
• end_station:  The station ID where the trip terminated (for station name and more information on each station see the Station Table )
• end_lat:  The latitude of the station where the trip terminated
• end_lon:  The longitude of the station where the trip terminated
• bike_id:   Locally unique integer that identifies the bike
• plan_duration: The number of days that the plan the passholder is using entitles them to ride; 0 is used for a single ride plan (Walk-up)
• trip_route_category:  “Round Trip” for trips starting and ending at the same station or “One Way” for all other trips
• passholder_type:  The name of the passholder’s plan
• bike_type:  The kind of bike used on the trip, including standard pedal-powered bikes or electric assist bikes

Data Processing

Data will be cleansed prior to publication according to the following criteria:

• Staff servicing and test trips are removed.
• Trips below 1 minute are removed.
• A “Virtual Station” listed in the checkout and return kiosks, is used by staff to check in or check out a bike remotely for a special event or in a situation in which a bike could not otherwise be checked in or out to a station.
• Trip lengths are capped at 24 hours.
• Some short round trips or long trips may be the result of system or user error, but have been kept in the dataset for completeness.

Station Information

• Station Table (Updated 2024-04-01)
• Station ID:  Unique integer that identifies the station (this is the same ID used in the Trips and Station Status data)
• Station Name:  The public name of the station. “Virtual Station” is used by staff to check in or check out a bike remotely for a special event or in a situation in which a bike could not otherwise be checked in or out to a station.
• Go live date:  The date that the station was first available
• Status: “Active” for stations available or “Inactive” for stations that are not available as of the latest update

Station Status

Live station location and status information from Indego is available in the following formats:

• Better Bike Share
• Explore Philly
• Partnerships
• Passholder of the Month
• Special Offers
• Staff Profile
• Station Updates

Recent Comments

• Paul Braff on 2024 Expansion Update
• thebangalore dhaba on Indego Staffing Updates!
• Emily on Join the Team: Indego Seeks Community Ambassadors for 2024!
• Arletha Jones-Pickens on Join the Team: Indego Seeks Community Ambassadors for 2024!
• Jake on 2024 Expansion Update

• Select Language English Afrikaans Albanian Amharic Arabic Armenian Azerbaijani Basque Belarusian Bengali Bosnian Bulgarian Catalan Cebuano Chichewa Chinese (Simplified) Chinese (Traditional) Corsican Croatian Czech Danish Dutch Esperanto Estonian Filipino Finnish French Frisian Galician Georgian German Greek Gujarati Haitian Creole Hausa Hawaiian Hebrew Hindi Hmong Hungarian Icelandic Igbo Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Kurdish (Kurmanji) Kyrgyz Lao Latin Latvian Lithuanian Luxembourgish Macedonian Malagasy Malay Malayalam Maltese Maori Marathi Mongolian Myanmar (Burmese) Nepali Norwegian Pashto Persian Polish Portuguese Punjabi Romanian Russian Samoan Scots Gaelic Serbian Sesotho Shona Sindhi Sinhala Slovak Slovenian Somali Spanish Sundanese Swahili Swedish Tajik Tamil Telugu Thai Turkish Ukrainian Urdu Uzbek Vietnamese Welsh Xhosa Yiddish Yoruba Zulu

Total Trips

Passes sold, calories burned, miles traveled, emissions reduced, pounds of co2.

* Data is based on estimation using trip duration and origin/destination location.

Metro and its partners at Bicycle Transit Systems are pleased to share anonymized Metro Bike Share trip data with the public. The privacy of Metro passholders and users is very important to us, and information won’t be released unless the Metro can ensure that the identities of individuals or groups cannot be discerned and that their privacy is not compromised.

Toss it, flip it, turn it inside out, combine it with your own or someone else's data! Tell us how many trips happened after midnight, or near your event or business - we invite you to delve into the data and reveal the hidden gems of understanding that will allow us to make Metro Bike Share the best it can be.

• 2024 Q1 (January – March)
• 2024 Q4 (October –  December)
• 2023 Q3 (July – September)
• 2023 Q2 (April – June)
• 2023 Q1 (January – March)
• 2022 Q4 (October –  December)
• 2022 Q3 (July – September)
• 2022 Q2 (April – June)
• 2022 Q1 (January – March)
• 2021 Q4 (October –  December)
• 2021 Q3 (July – September)
• 2021 Q2 (April – June)
• 2021 Q1 (January – March)
• 2020 Q4 (October –  December)
• 2020 Q3 (July – September)
• 2020 Q2 (April – June)
• 2020 Q1 (January – March)
• 2019 Q4 (October – December)
• 2019 Q3 (July – September)
• 2019 Q2 (April – June)
• 2019 Q1 (January – March)
• 2018 Q4 (October – December)
• 2018 Q3 (July – September)
• 2018 Q2 (April – June)
• 2018 Q1 (January – March)
• 2017 Q4 (October – December)
• 2017 Q3 (July – September)
• 2017 Q2 (April – June)
• 2017 Q1 (January – March)
• 2016 Q4 (October – December)
• 2016 Q3 (Launch, July 7 – September)

Data Format

Each .csv file contains data for one quarter of the year. Each file contains the following data points:

• trip_id: Locally unique integer that identifies the trip
• duration:  Length of trip in minutes
• start_time:  The date/time when the trip began, presented in ISO 8601 format in local time
• end_time: The date/time when the trip ended, presented in ISO 8601 format in local time
• start_station:  The station ID where the trip originated (for station name and more information on each station see the Station Table )
• start_lat:  The latitude of the station where the trip originated
• start_lon:  The longitude of the station where the trip originated
• end_station:  The station ID where the trip terminated (for station name and more information on each station see the Station Table )
• end_lat:  The latitude of the station where the trip terminated
• end_lon:  The longitude of the station where the trip terminated
• bike_id:   Locally unique integer that identifies the bike
• plan_duration: The number of days that the plan the passholder is using entitles them to ride; 0 is used for a single ride plan (Walk-up)
• trip_route_category:  "Round Trip" for trips starting and ending at the same station or "One Way" for all other trips
• passholder_type:  The name of the passholder's plan
• bike_type:  The kind of bike used on the trip, including standard pedal-powered bikes, electric assist bikes, or smart bikes.

Data Processing

Data will be cleansed prior to publication according to the following criteria:

• Staff servicing and test trips are removed.
• Trips below 1 minute are removed.
• A "Virtual Station" listed in the checkout and return kiosks, is used by staff to check in or check out a bike remotely for a special event or in a situation in which a bike could not otherwise be checked in or out to a station.
• Trip lengths are capped at 24 hours.
• Some short round trips or long trips may be the result of system or user error, but have been kept in the dataset for completeness.

Station Information

• Station Table (Updated 2024-04-01)
• Station ID:  Unique integer that identifies the station (this is the same ID used in the Trips and Station Status data)
• Station Name:  The public name of the station. "Virtual Station" is used by staff to check in or check out a bike remotely for a special event or in a situation in which a bike could not otherwise be checked in or out to a station.
• Go live date:  The date that the station was first available
• Region: The municipality or area where a station is located, includes DTLA (Downtown LA), Pasadena, Port of LA, Venice
• Status: "Active" for stations available or "Inactive" for stations that are not available as of the latest update

Station Status

Live station location and status information is available in the following formats:

Analysis of Bikeshare Data in 2018: User Type, Trip Duration, and Usage Patterns.

Muhammad enrizky brillian_1009713712, 1. merging datasets from multiple csvs, 2. description of the dataset.

• Trip ID: A unique identifier for each trip
• Trip Duration (Seconds): The length of the trip in seconds
• From Station ID: The unique identifier for the starting station of the trip
• Trip Start Time: The date and time the trip began
• From Station Name: The name of the starting station for the trip
• Trip Stop Time: The date and time the trip ended
• To Station ID: The unique identifier for the ending station of the trip
• Bike ID: The name of the ending station for the trip
• User Type: A categorical variable indicating whether the user is a “Annual Member” (annual pass holder of the bikeshare program) or a “Casual Member”(24 or 72 hour pass holders of the bikeshare program)

3. The Background of the Data

The bikeshare data for 2018 was collected by the City of Toronto, specifically by the Transportation Services division. The data was collected in the context of a bikeshare program called Bike Share Toronto, which is a public bicycle sharing system in Toronto, Canada. Bike Share Toronto provides access to bicycles at various stations throughout the city, allowing users to rent a bike for short trips and return it to any station within the system. The data collected includes information about bike trips, such as trip duration, start and end station, and user type, and other variables.This data is publicly available through the City of Toronto’s Open Data portal for research and analysis purposes.

The dataset can be used for analysis and insights into the usage patterns and trends of the bikeshare program in Toronto during the year 2018. It is typically used by researchers, policymakers, and urban planners to analyze bike usage patterns, evaluate the effectiveness of the bikeshare program, and inform transportation planning and policy decisions. Overall, the bikeshare data for 2018 is a valuable resource for investigating bike usage patterns, user behavior, and system performance in Toronto, and can contribute to evidence-based decision making in transportation planning and policy.

4. What is the overall research question?

As researchers, our overall research question is to investigate the usage patterns and trends of bikeshare data in Toronto for the year 2018. We aim to analyze various variables, such as trip duration, trip start time, user type, and station information, to gain insights into how the bikeshare system was utilized by different user groups, and to identify any patterns or trends that may emerge from the data. Specifically, our research question is:

• What are the summary statistics for trip duration by user type, and how do they compare?
• How does the total number of trips taken by each user type vary by month?
• What are the top 10 most popular starting stations for bike rides in the dataset?
• How many trips lasted 30 minutes or more, and how many lasted 45 minutes or more, for each user type?
• What is the trend of total trips by hour of day, and how does it relate to peak usage times?
• How does monthly trip volume vary by user type, and what is the overall trend over the year?

5.1 Trip Duration (in Seconds) Summary By User Type

From the table, we can see that the “Annual Member” user type has a significantly larger number of trips (1,572,980) than the “Casual Member” user type (349,975). However, the “Casual Member” user type has a higher average trip duration (2,032.4958 seconds) compared to the “Annual Member” user type (725.0167 seconds). The median duration for both user types is lower than the average, which indicates that there are some trips with much higher durations, skewing the average upwards.

5.2 Table of Total Trips by Month for each user type

The Table of Total Trips by Month for each user type displays the total number of trips taken by annual members and casual members for each month of the year. For example, in January, there were 42,469 trips taken by annual members and 1,390 trips taken by casual members. In February, there were 47,276 trips taken by annual members and 2,455 trips taken by casual members. This continues for each month of the year, showing the total number of trips taken by each user type.

This table can provide insights into how usage of the bike share service varies by month and by user type. For example, it appears that usage by both annual and casual members increases during the warmer months, as the number of trips taken by both groups is much higher in the summer months (June through August) compared to the winter months (December through February). It also appears that annual members take many more trips overall than casual members.

For Annual Members, the month with the highest number of trips is July, with 215,835 trips. The month with the lowest number of trips is December, with 80,630 trips. For Casual Members, the month with the highest number of trips is May, with 51,761 trips. The month with the lowest number of trips is January, with 1,390 trips.

5.3 The Top 10 Most Popular Starting Stations

This table shows the top 10 most popular starting stations for bike rides in the given dataset. It lists the names of the stations under the column “Starting Station” and the corresponding number of trips that started from that station under the column “num_trips”.

The table is sorted in descending order based on the number of trips, so the station with the highest number of trips is listed first. According to the table, the most popular starting station is “York St / Queens Quay W” with 24,017 trips, followed by “Bay St / Queens Quay W (Ferry Terminal)” with 22,743 trips, and “Union Station” with 19,869 trips.

This table can be useful for bike-sharing companies or city officials to identify the most popular locations for bike rides and allocate their resources accordingly, such as providing more bikes or improving bike infrastructure in those areas.

5.4 Trip duration More than 30 minutes and More than 45 minutes

The table “Trip Duration more than 30 minutes and 45 minutes” shows the number of trips taken by each user type (Annual Member and Casual Member) that lasted 30 minutes or more and 45 minutes or more, respectively.

For Annual Members, there were 32,647 trips that lasted 30 minutes or more, and 10,364 trips that lasted 45 minutes or more. For Casual Members, there were 87,792 trips that lasted 30 minutes or more, and 51,791 trips that lasted 45 minutes or more.

This information could be useful for analyzing the usage patterns of different user types and identifying potential areas for improvement in the bike sharing system. For example, if a significant number of casual users are taking longer trips, the bike sharing company could consider offering different pricing plans or incentives to encourage shorter trips and more frequent use. Additionally, the data could help inform decisions about bike station placement and bike fleet size based on usage patterns.

6.1 Line Chart of Total Trips by Hour

The line shows the trend of the total number of trips throughout the day. It appears that the number of trips starts to increase from around 5 AM, peaks around 8 AM, drops gradually until noon, picks up again in the afternoon until around 5 PM, and then starts to decrease steadily until midnight.

This pattern indicates that the bikeshare service is mostly used for commuting to work or school during the weekdays, with higher demand during rush hours in the morning and evening. The chart could be useful to help the bikeshare company plan for bike availability and schedule maintenance based on peak usage times.

6.2 Stacked bar plot of trip volume by user type and month

From this plot, we can see that the number of trips taken by Annual Members is consistently higher than the number of trips taken by Casual Members throughout the year. We can also observe that the number of trips taken by both user types peaks in the summer months (June to August) and declines during the winter months (December to February). This suggests that the bikeshare program is more popular during the warmer months of the year.

Overall, this plot provides an overview of the distribution of trips by user type over the months, which can help stakeholders make informed decisions about their marketing strategies and resource allocation.

6.3 Heatmap of trips by day of the week

From the Heatmap, we can observe that the bike usage is highest during the weekdays, particularly on Tuesday, Wednesday, and Thursday. During these weekdays, the highest usage hours are the morning commute hours from 7-9 am and evening hours from 4-6 pm. On weekends, the usage of bikes is relatively lower than weekdays, and there is a slightly different pattern in bike usage. Bike usage is highest during the afternoon hours, particularly from 12 pm to 4 pm on both Saturday and Sunday.

Overall, this heatmap provides an insight into the bike usage pattern during different times of the week, which can help the bikeshare company to plan its resources and services accordingly.

6.4 Line plot of trip volume over time

This line chart shows the total number of trips taken each month over the course of the year 2018, based on the data in the “bikeshare_data” dataset. The x-axis represents the months of the year, and the y-axis represents the total number of trips taken. Each point on the line represents the total number of trips taken in a given month. The blue line connects these points to show the trend in trip volume over the year.

From the chart, we can see that the number of trips generally increased from January to July, with the highest number of trips in July, followed by a gradual decrease in the number of trips from August to December. The chart also shows a dip in the number of trips in November and December, which may be due to seasonal factors such as colder weather. Overall, the chart provides an overview of the trend in trip volume over the course of the year 2018.

7. Hypothesis Testing

Trip duration between trips taken on weekdays versus weekends..

We would like to determine if there is a significant difference in trip duration between trips taken on weekdays versus weekends.

Hypothesis:

• \(H_0\) : The average trip duration on weekdays ( \(\mu_d\) ) is equal to the average trip duration on weekend ( \(\mu_e\) ), i.e., \(\mu_d=\mu_e\)
• \(H_1\) : The average trip duration on weekdays ( \(\mu_d\) ) is not equal to the average trip duration on weekend ( \(\mu_e\) ), i.e., \(\mu_d\neq\mu_e\)

To test this hypothesis, we can use a two-sample t-test to compare the mean trip duration of weekday trips with the mean trip duration of weekend trips. We can split the data into two groups: weekday trips (Monday to Friday) and weekend trips (Saturday and Sunday).

We can then calculate the p-value associated with the t-statistic, which is the probability of observing a t-value as extreme or more extreme than the one calculated, assuming the null hypothesis is true. If the p-value is less than our significance level (typically 0.05), we can reject the null hypothesis and conclude that there is a significant difference in trip duration between weekday and weekend trips.

We can also calculate a confidence interval for the difference between the means to estimate the range of values in which the true difference between the means is likely to fall.

Overall, this hypothesis testing approach allows us to determine whether there is a statistically significant difference in trip duration between trips taken on weekdays versus weekends, and provides us with an estimate of the magnitude of this difference.

The Welch Two Sample t-test shows a significant difference between the mean trip durations of weekdays and weekends. The p-value (2.2e-16) is less than the significance level of 0.05, indicating strong evidence against the null hypothesis of no difference. The confidence interval (-383.8696, -370.7647) also does not include 0, further supporting the conclusion that there is a significant difference between the two groups. Therefore, we can conclude that there is a significant difference in trip duration between trips taken on weekdays versus weekends. Specifically, the mean trip duration on weekends (1251.0967 seconds) is longer than that on weekdays (873.7795 seconds).

8. Bootstrapping

Estimating the average trip duration for casual members.

We could use bootstrapping to estimate the sampling distribution of the mean and compute a confidence interval.

Based on the result, the mean trip duration for casual members is 2032.496 seconds. The 95% confidence interval is (2022.838 - 2042.171) seconds. This means that we are 95% confident that the true mean trip duration for casual members falls within this range.

9. Non-Linear Regression Analysis

Analyzing the relationship between the number of trips and hour of the day..

In this case, we would treat the hour of the day as a continuous variable and fit a non-linear regression model with the number of trips as the dependent variable and the hour of the day as the independent variable. The resulting model would give us information on the direction and strength of the relationship between the hour of the day and the number of trips.

Explanation

The non-linear regression model suggests that there is a significant relationship between the number of bike trips and the hour of the day, and that this relationship is best explained by a third-order polynomial function. The coefficient of determination (R-squared) of 0.69 indicates that the model explains 69% of the variation in the data. The p-values for the coefficients of the quadratic and cubic terms are both significant, suggesting that the non-linear terms are needed to better explain the relationship between the number of bike trips and the hour of the day. However, the p-value for the coefficient of the linear term is not significant, suggesting that there may not be a significant linear relationship between the number of bike trips and the hour of the day.

Intrepreting Regression Parameter

The regression parameters in this case refer to the coefficients of the independent variables in the multiple regression equation.

For this non-linear regression model, we have the following coefficients:

The intercept coefficient represents the value of the dependent variable (num_trips) when all independent variables are equal to zero. In this case, the intercept coefficient is 11345.41, which means that when the trip_start_hour, trip_start_hour squared, and trip_start_hour cubed are all zero, the expected value of num_trips is 11345.41.

The coefficient of trip_start_hour represents the linear effect of the start hour on the number of trips. In this case, the coefficient is negative (-5051.71), which suggests that as the start hour increases by one unit, the expected value of num_trips decreases by 5051.71.

The coefficient of I(trip_start_hour^2) represents the quadratic effect of start hour on the number of trips. In this case, the coefficient is positive (2225.45), which suggests that the relationship between start hour and num_trips is curvilinear, with a maximum value of num_trips occurring at a specific start hour.

The coefficient of I(trip_start_hour^3) represents the cubic effect of start hour on the number of trips. In this case, the coefficient is negative (-86.35), which suggests that the curvilinear relationship between start hour and num_trips is concave down, with the rate of decrease in num_trips slowing down as start hour increases.

Overall, this non-linear regression suggests that the relationship between start hour and the number of trips is not simply linear, but rather a curvilinear relationship with a peak at a specific start hour. Additionally, the cubic term indicates that this curvilinear relationship is concave down, meaning that the rate of decrease in the number of trips as start hour increases slows down as start hour increases.

Overall, this suggests that there is a non-linear relationship between the hour of the day and the number of trips taken, and this relationship is well captured by the polynomial regression model.

Plotting the Result

10. cross validation, analyzing between trip duration (in hour) and hour of the day.

In this case, we can use cross-validation to analyze the relationship between trip duration and hour of the day in the bikeshare data.

The idea behind this approach is to split the dataset into two parts, with one part used to train the model and the other part used to validate it. We can then measure the accuracy of the model on the validation set and adjust the model as needed to improve its performance.

To use cross-validation to analyze the relationship between trip duration (in hour) and hour of the day, we would start by splitting the dataset into a training set and a validation set. We could then use Non-linear regression to build a model that predicts trip duration based on the hour of the day, using the training set. Once we have built the model, we would use the validation set to evaluate its performance.

Our approach is k-fold cross-validation, where the dataset is divided into k equally-sized subsets. We then train the model on k-1 of the subsets and use the remaining subset for validation. We repeat this process k times, so that each subset is used for validation once.

The average MSE of 0.002516312 is quite small, which suggests that the non-linear regression model is fitting the data quite well. This means that there is likely a relationship between the hour of the day and the average trip duration (in hour), and that this relationship can be captured by the non-linear regression model.

11. Summary of Research

Based on the information presented in the report, the following are the key findings:

• The bikeshare service is mostly used by annual members, who take significantly more trips than casual members.
• The average trip duration for casual members is higher than that of annual members, but both groups have a similar median duration.
• Usage of the bikeshare service increases during the warmer months (June through August) compared to the winter months (December through February).
• The most popular starting station is “York St / Queens Quay W” with 24,017 trips.
• Annual members mostly use the bikeshare service for commuting to work or school, with higher demand during rush hours in the morning and evening.
• Casual members tend to take longer trips compared to annual members, with a significant number of trips lasting 30 minutes or more.
• The bikeshare company could consider offering different pricing plans or incentives to encourage shorter trips and more frequent use by casual members.

Overall, the report provides insights into the usage patterns and behavior of users of the bikeshare service, which could help inform decisions about bike station placement, bike fleet size, pricing plans, and incentives to encourage more usage.