Dataset

This dataset was created by Nile Leggs

Contents

Project Name: Divvy Bikeshare Trip Data_Year2020 Date Range: April 2020 to December 2020. Analyst: Ajith Software: R Program, Microsoft Excel IDE: RStudio

The following are the basic system requirements, necessary for the project: Processor: Intel i3 or AMD Ryzen 3 and higher Internal RAM: 8 GB or higher Operating System: Windows 7 or above, MacOS

**Data Usage License: https://ride.divvybikes.com/data-license-agreement ** Introduction:

In this case, study we aim to utilize different data analysis techniques and tools, to understand the rental patterns of the divvy bike sharing company and understand the key business improvement suggestions. This case study is a mandatory project to be submitted to achieve the Google Data Analytics Certification. The data utilized in this case study was licensed based on the provided data usage license. The trips between April 2020 to December 2020 are used to analyse the data.

Scenario: Marketing team needs to 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.

Objective: The main objective of this case study, is to understand the customer usage patterns and the breakdown of customers, based on their subscription status and the average durations of the rental bike usage.

Introduction to Data: The Data provided for this project, is adhered to the data usage license, laid down by the source company. The source data was provided in the CSV files and are month and quarter breakdowns. A total of 13 columns of data was provided in each csv file.

The following are the columns, which were initially observed across the datasets.

Ride_id Ride_type Start_station_name Start_station_id End_station_name End_station_id Usertype Start_time End_time Start_lat Start_lng End_lat End_lng

Documentation, Cleaning and Preparing Data for Analysis: The total size of the datasets, for the year 2020, is approximately 450 MB, which is tiring job, when you have to upload them to the SQL database and visualize using the BI tools. I wanted to improve my skills into R environment and this is the best opportunity and optimal to use R for the data analysis.

For more insights, installation procedures for R and RStudio, please refer to the following URL, for additional information.

R Projects Document: https://www.r-project.org/other-docs.html RStudio Download: https://www.rstudio.com/products/rstudio/ Installation Guide: https://www.youtube.com/watch?v=TFGYlKvQEQ4

Case Study: How Does a Bike-Share Navigate Speedy Success?¶

Introduction

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. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path. By the end of this lesson, you will have a portfolio-ready case study. Download the packet and reference the details of this case study anytime. Then, when you begin your job hunt, your case study will be a tangible way to demonstrate your knowledge and skills to potential employers.

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. Characters and teams ● 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 them to commute to work each day. ● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. ● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them. ● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

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

Three questions will guide the future marketing program:

How do annual members and casual riders use Cyclistic bikes differently? Why would casual riders buy Cyclistic annual memberships? How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual rid...

Case study: How does a bike-share navigate speedy success?

Scenario:

As a data analyst on Cyclistic's marketing team, our focus is on enhancing annual memberships to drive the company's success. We aim to analyze the differing usage patterns between casual riders and annual members to craft a marketing strategy aimed at converting casual riders. Our recommendations, supported by data insights and professional visualizations, await Cyclistic executives' approval to proceed.

About the company

In 2016, Cyclistic launched a bike-share program in Chicago, growing to 5,824 bikes and 692 stations. Initially, their marketing aimed at broad segments with flexible pricing plans attracting both casual riders (single-ride or full-day passes) and annual members. However, recognizing that annual members are more profitable, Cyclistic is shifting focus to convert casual riders into annual members. To achieve this, they plan to analyze historical bike trip data to understand the differences and preferences between the two user groups, aiming to tailor marketing strategies that encourage casual riders to purchase annual memberships.

Project Overview:

This capstone project is a culmination of the skills and knowledge acquired through the Google Professional Data Analytics Certification. It focuses on Track 1, which is centered around Cyclistic, a fictional bike-share company modeled to reflect real-world data analytics scenarios in the transportation and service industry.

Dataset Acknowledgment:

We are grateful to Motivate Inc. for providing the dataset that serves as the foundation of this capstone project. Their contribution has enabled us to apply practical data analytics techniques to a real-world dataset, mirroring the challenges and opportunities present in the bike-sharing sector.

Objective:

The primary goal of this project is to analyze the Cyclistic dataset to uncover actionable insights that could help the company optimize its operations, improve customer satisfaction, and increase its market share. Through comprehensive data exploration, cleaning, analysis, and visualization, we aim to identify patterns and trends that inform strategic business decisions.

Methodology:

Data Collection: Utilizing the dataset provided by Motivate Inc., which includes detailed information on bike usage, customer behavior, and operational metrics. Data Cleaning and Preparation: Ensuring the dataset is accurate, complete, and ready for analysis by addressing any inconsistencies, missing values, or anomalies. Data Analysis: Applying statistical methods and data analytics techniques to extract meaningful insights from the dataset.

Visualization and Reporting:

Creating intuitive and compelling visualizations to present the findings clearly and effectively, facilitating data-driven decision-making. Findings and Recommendations:

Conclusion:

The Cyclistic Capstone Project not only demonstrates the practical application of data analytics skills in a real-world scenario but also provides valuable insights that can drive strategic improvements for Cyclistic. Through this project, showcasing the power of data analytics in transforming data into actionable knowledge, underscoring the importance of data-driven decision-making in today's competitive business landscape.

Acknowledgments:

Special thanks to Motivate Inc. for their support and for providing the dataset that made this project possible. Their contribution is immensely appreciated and has significantly enhanced the learning experience.

STRATEGIES USED

Case Study Roadmap - ASK

●What is the problem you are trying to solve? ●How can your insights drive business decisions?

Key Tasks ● Identify the business task ● Consider key stakeholders

Deliverable ● A clear statement of the business task

Case Study Roadmap - PREPARE

● Where is your data located? ● Are there any problems with the data?

Key tasks ● Download data and store it appropriately. ● Identify how it’s organized.

Deliverable ● A description of all data sources used

Case Study Roadmap - PROCESS

● What tools are you choosing and why? ● What steps have you taken to ensure that your data is clean?

Key tasks ● Choose your tools. ● Document the cleaning process.

Deliverable ● Documentation of any cleaning or manipulation of data

Case Study Roadmap - ANALYZE

● Has your data been properly formaed? ● How will these insights help answer your business questions?

Key tasks ● Perform calculations ● Formatting

Deliverable ● A summary of analysis

Case Study Roadmap - SHARE

● Were you able to answer all questions of stakeholders? ● Can Data visualization help you share findings?

Key tasks ● Present your findings ● Create effective data viz.

Deliverable ● Supporting viz and key findings

**Case Study Roadmap - A...

Dataset

This dataset was created by Shreyak Silwal

Released under Other (specified in description)

Contents

Dataset

This dataset was created by Chinazom Jennifer, Okoli

Released under CC0: Public Domain

Contents

This project uses a dataset called HR_capstone_dataset.csv. It represents 10 columns of self-reported information from employees of a fictitious multinational vehicle manufacturing corporation.

The dataset contains:

14,999 rows – each row is a different employee’s self-reported information

This dataset has as its primary data source, the Kaggle dataset:

-HR Analytics Job Prediction (CC0: Public Domain, made available by Faisal Qureshi) - Link: (https://www.kaggle.com/datasets/mfaisalqureshi/hr-analytics-and-job-prediction/data)

Introduction:

In this case study the skills that I acquired from Google Data Analytics Professional Certificate Course is demonstrated. These skills will be used to complete the imagined task which was given by Netflix. The analysis process of this task will be consisted of following steps. Ask, Prepare, Process, Analyze, Share and Act.

Scenario:

The Netflix Chief Content Officer, Bela Bajaria, believes that companies success depends on to provide the customers what they want. Bajaria stated that the goal of this task is to find most wanted contents of the movies which will be added to the portfolio. Most of the movie contracts are signed before they come to the theaters, and it is hard to know if the customers really want to watch that movie and if the movie will be successful. There for my team wants to understand what type of content a movies success depends on. From these insights my team will design an investment strategy to choose the most popular movies that are expected to be in theaters in the near future. But first, Netflix executives must approve our recommendations. To be able to do that we must provide satisfying data insights along with professional data visualizations.

About the Company:

At Netflix, we want to entertain the world. Whatever your taste, and no matter where you live, we give you access to best-in-class TV series, documentaries, feature films and games. Our members control what they want to watch, when they want it, in one simple subscription. We’re streaming in more than 30 languages and 190 countries, because great stories can come from anywhere and be loved everywhere. We are the world’s biggest fans of entertainment, and we’re always looking to help you find your next favorite story.

As a company Netflix knows that it is important to acquire or produce movies that people want to watch.

There for Bajaria has set a clear goal: Define an investment strategy that will allow Netflix to provide customers the movies what they want to watch which will maximize the Sales.

Ask:

Business Task: To find out what kind of movie customers wants to watch and if the content type really has a correlation with the movie success. Stakeholders:

Bela Bajaria: She joined Netflix in 2016 to oversee unscripted and scripted series. Bajaria also responsible from the content selection and strategy for different regions.

Netflix content analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Netflix content strategy.

Netflix executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended content program.

Prepare:

I start my preparation procedure by downloading every piece of data I'll need for the study. Top 1000 Highest-Grossing Movies of All Time.csv will be used. Additionally, 15 Lowest-Grossing Movies of All Time.csv was found during the data research and this dataset will be analyst as well. The data has been made available by IMDB and shared this two following URL addresses: https://www.imdb.com/list/ls098063263/ and https://www.imdb.com/list/ls069238222/ .

Process:

Data Cleaning:

SQL: To begin the data cleaning process, I opened both csv file in SQL and conducted following operations:

• Checked for and removed any duplicates. • Checked if there any null values. • Removed the columns that are not necessary. • Trim the Description column to have only gross profit in it. (This cleaning procedure only used for 1000 Highest-Grossing Movies of All Time.csv dataset.)

• Renamed the Description column as Gross_Profit. (This cleaning procedure only used for 1000 Highest-Grossing Movies of All Time.csv dataset.)

Follwing SQL codes were used during the data cleaning:

SQL CODE used for Highest Grossing Movies DATASET

SELECT Position, SUBSTR(Description,34,12) as Gross_Profit, Title, IMDb_Rating, Runtime_mins_, Year, Genres, Num_Votes, Release_Date FROM even-electron-400301.Highest_Gross_Movies.1

SQL CODE used for Lowest Grossing Movies DATASET

SELECT Position, Title, IMDb_Rating, Runtime_mins_, Year, Genres, Num_Votes, Release_Date FROM even-electron-400301.Lowest_Grossing_Movies.2 Order By Position

Analyze:

As a starter, I want to reemphasize the business task once again. Is content has a big impact on a movie’s success?

To answer this question, there were a few information that I projected that I could pull of and use it during my analysis.

• Average gross profit • Number of Genres • Total Gross Profit of the most popular genres • The distribution of the Gross income on Genres

I used Microsoft Excel for the bullet points above. The operations to achieve the values above are as follows:

• Average function for Average Gross profit in 1000 Highest-Grossing Movies of All Time. • Created a pivot table to work on Genres and Gross_Pr...

This dataset has as its primary data source, the Kaggle dataset:

• HR Analytics Job Prediction (CC0: Public Domain, made available by Faisal Qureshi)
• Link: (https://www.kaggle.com/datasets/mfaisalqureshi/hr-analytics-and-job-prediction/data)

The dataset was repurposed for the Google Advanced Data Analytics Professional Certicate Capstone Project.

The original dataset’s provenance refers to another Kaggle dataset:

• HR Analytics (CC0: Public Domain, made available by Giri Pujar)
• Link: https://www.kaggle.com/datasets/giripujar/hr-analytics

The dataset consists of 1 .csv file, including 15,000 observations and 10 distinct variables. The data includes information regarding the satisfaction, evaluation, project workload, working hours and tenure of employees, among other records.

The data consists of 10 variables: 6 integer variables, 2 string variables, and 2 decimal variables.

Cyclistic Trip Data

This dataset contains rows of cyclistic trip data, collected from Index of bucket "divvy-tripdata". The dataset includes 10 columns, each representing a different attribute or feature of the data.

The data has been preprocessed to remove any missing values, duplicates, or other inconsistencies. It is ready for use in a wide range of data analysis and machine learning tasks.

The dataset includes the following columns:

• rideable_type : Type of bicycle used for rides
• member_casual : Type of members performing the drive
• ride_length : Bicycle ride length in minutes
• date : Realized date of cycling ride
• day_of_week : Days of the week when the bike ride takes place
• month : Months of riding
• day : Days of the month in which the ride was carried out
• year : Year in which the ride took place
• hour : Hours when riding is carried out
• season : Seasons in which the ride takes place

This dataset contains data from the capstone project section of the google data analytics course on the coursera platform. This dataset has been cleaned and processed, ready for the user to analyze. We hope that it will help everyone who takes this course and tries to make the preparation process related to the data in the last stage.

Data source: FitBit Fitness Tracker Data (https://www.kaggle.com/datasets/arashnic/fitbit) is a public dataset available on Kaggle. This dataset contains personal fitness tracker from thirty three eligible Fitbit users. This dataset was generated by respondents to a distributed survey via Amazon Mechanical Turk between the 12th of April, 2016 and the 12th of May, 2016. This dataset has been cleaned, formatted with the date & time columns separated into 2 columns (one for date and the other for 24-hr time format) to prepare for the analysis done in SQL and visualisation in Tableau.

Introduction After completing my Google Data Analytics Professional Certificate on Coursera, I accomplished a Capstone Project, recommended by Google, to improve and highlight the technical skills of data analysis knowledge, such as R programming, SQL, and Tableau. In the Cyclistic Case Study, I performed many real-world tasks of a junior data analyst. To answer the critical business questions, I followed the steps of the data analysis process: ask, prepare, process, analyze, share, and act. **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. Characters and teams Cyclistic: A bike-share program that has grown to a fleet of 5,824 bicycles that are tracked 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 at any time. 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 assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. Stakeholders Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. Cyclistic marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals and how you, as a junior data analyst, can help Cyclistic achieve them. *Cyclistic executive team: *The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

Dataset

This dataset was created by Hussein Al Chami

Released under Apache 2.0

Contents

The data is from Divvy Bikes in Chicago. It was used to complete my capstone project. It is a .csv file that contains the rideship information from January 2023 through December 2023.

## Case Study: How Does a Bike-Share Navigate Speedy Success?

Introduction Welcome to the Cyclistic bike-share analysis case study! In this case study, you work for a fictional company, Cyclistic, along with some key team members. In order to answer the business questions, follow the steps of the data analysis process: Ask, Prepare, Process,Analyze, Share, and Act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path.

Scenario You are a junior data analyst working on 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.

Characters and teams ● 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.

● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals—as well as how you, as a junior data analyst, can help Cyclistic achieve them.

● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program. 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 solid opportunity 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 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.

This project will completed using the 6 stages of Data Analysis:

Stage 1: Ask- How Do annual members and casual riders use Cyclistic Bikes differently?

Stage 2: Prepare- Collect the data, identify how it is organized, and check for bias and determine credibility of the data.

Stage 3: Process- select the tool for cleaning and organizing the data, ensure data's integrity, check for errors and transform data to work with effectively.

Stage 4: Analyze- aggregate data so it is useful and accessible, organize and format data, perform calculation, and identify trends and relationships.

Stage 5: Share- Determine how to best share findings, create effective visualizations, and present findings.

Stage 6: Act- create portfolio, and ad...

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask:

A. Guiding Questions:
1. Who are the key stakeholders and what are their goals for the data analysis project? 2. What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: 1. Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team.

1. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare:

A. Guiding Questions: 1. Where is the data stored and organized? 2. Are there any problems with the data? 3. How does the data help answer the business question?

B. Key Tasks:

1. Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016.
   *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDay_merged.csv -dailyActivity_merged.csv

2. Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual ...

This case study is towards Capstone project requirement for the Google Data Analytics Professional Certificate. The author uses the data analysis process consisting of ask, prepare, process, analyze, share and act, and chooses spreadsheet and Tableau as tools to perform data processing, data analysis and data visualization.

This analysis is for the Google Data Analytics Professional capstone project. Though I am proficient with MS Excel with 20 years of experience at an intermediate level, the course is advancing my basic SQL and Tableau skills while providing a great foundation in R and Rstudio. I am looking forward to advancing my skillset and having fun with more datasets.

Use of fictional company Cyclistic’s historical trip data was used to analyze and identify trends. The data has been made available by Motivate International Inc. https://divvy-tripdata.s3.amazonaws.com/index.html

The link to the data set was provided by the Coursera team in the case study description. However, the dataset was originally provided by Motivate International Inc. and a data license agreement was provided. This is public data, Licensed by Lyft Bikes and Scooters, LLC (“Bikeshare”), that can be used to explore how different customer types are using Cyclistic bikes.

The data is organized quarterly and yearly with several datasets to explore, so I chose the one for Q1-Q4 of 2019, and the link to the datasets is as follows: https://divvy-tripdata.s3.amazonaws.com/index.html

Dataset

This dataset was created by Vu Khac Canh

Contents