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...

Qualitative data gathered from interviews that were conducted with case organisations. The data is analysed using a qualitative data analysis tool (AtlasTi) to code and generate network diagrams. Software such as Atlas.ti 8 Windows will be a great advantage to use in order to view these results. Interviews were conducted with four case organisations. The details of the responses from the respondents from case organisations are captured. The data gathered during the interview sessions is captured in a tabular form and graphs were also created to identify trends. Also in this study is desktop review of the case organisations that formed part of the study. The desktop study was done using published annual reports over a period of more than seven years. The analysis was done given the scope of the project and its constructs.

Snapshot img

The cloud-based project portfolio management market share is expected to increase by USD 4.83 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 18.26%.

This cloud-based project portfolio management market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers cloud-based project portfolio management market segmentations by end user (manufacturing, ICT, healthcare, BFSI, and others) and geography (North America, Europe, APAC, MEA, and South America). The cloud-based project portfolio management market report also offers information on several market vendors, including Atlassian Corp. Plc, Broadcom Inc., Mavenlink Inc., Micro Focus International Plc, Microsoft Corp., Oracle Corp., Planview Inc., SAP SE, ServiceNow Inc., and Upland Software, Inc. among others.

What will the Cloud-based Project Portfolio Management Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Cloud-based Project Portfolio Management Market Size for the Forecast Period and Other Important Statistics

Cloud-based Project Portfolio Management Market: Key Drivers, Trends, and Challenges

The increasing requirements for large-scale project portfolio management is notably driving the cloud-based project portfolio management market growth, although factors such as challenges from open-source platforms may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the cloud-based project portfolio management industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Cloud-based Project Portfolio Management Market Driver

The increasing requirements for large-scale project portfolio management is a major factor driving the global cloud-based project portfolio management market share growth. Currently, organizations are focusing on cultivating and managing the resources necessary for efficient product outputs, which increases the requirements for efficient solutions for large-scale project portfolio management. The primary purpose of the cloud-based project portfolio management software is to automate processes to ensure maximum outputs by managing resources and maintaining a regular follow-up. The main benefit of employing cloud-based project portfolio management software in large-scale project portfolio management is that automated services increase the connectivity so that organizations can handle the project-related inquiries easily and effectively. Also, automation decreases the response time and increases productivity, which ensures efficient process management. Additionally, by using cloud-based project portfolio management software, revenue possibilities can be rapidly increased by calculating conversion ratios and running reports to track the metrics detailed as per the customer demand. These features decrease the operating time. Due to such reasons, the demand for the market will grow significantly during the forecast period.

Key Cloud-based Project Portfolio Management Market Trend

The interlinking of software with project portfolio management is another factor supporting the global cloud- based project portfolio management market share growth. Since the demand for project portfolio management software is rising in the market, the stakeholders in several businesses are demanding new features in the software to increase their productivity. One of the main trends identified in the global cloud-based project portfolio management market is the interlinking of multiple software to match the requirements of the business. Currently, cloud-based project portfolio management software is deployed by several enterprises to give people access to documents, data, and reports from multiple devices at multiple locations. With all the data accessible centrally by numerous users, the accountability of the system will increase, which will provide enterprises with an instant overview of what everyone is working on. Additionally, interlinked project portfolio management software will enable the users to update data in real-time and will end the complication of sending endless email attachments of the same document. Moreover, the implementation of cloud-based project portfolio management will enhance the company's assurance for up-to-date data. Therefore, all such factor will contribute to the growth of the market.

Key Cloud-based Project Portfolio Management Market Challenge

The rising challenges from open-source platforms will be a major challenge for the global cloud-based project portfolio management market share growth during the forecast period. With the rising demand for digitalization in the current market s

Real Estate Database

This is a mock-up of a real estate company, this is based on an actual company that had a number of challenges - collection and revenue is the biggest issue. A deep dive into the available data will provide the possible reasons and is the purpose of the data analytics project.

Here's the fictional business scenario:

Ms. Aurora Sanchez, the Chief Operations Officer (COO) of Prime Estate talked to the operations data analyst team to discuss a couple of her requirements. Ms. Sanchez is responsible for sales, property and project management, customer service, collections, and several other operations departments under her umbrella. When she joined the organization in late 2018, she quickly got several escalations from buyers who were complaining about units, properties that were not turned over on time, and delays in the projects. Ms. Sanchez also noted problems with collections not meeting the targets, and inconsistent sales performance.

As the COO, Ms. Sanchez wants to identify and validate the history of these problems as well as see if there have been improvements in these pain points ever since she joined Prime Estate. Her focus points are Collections, Project Management, Customer Service, Collections, and Sales.

As the Business/Data Analyst Lead, your responsibility is to gather the performance data related to this part of operations, find trends, present findings, and provide recommendations that will help the organization improve the pain points of operations. You must work with the manager of customer service and collections, and the project and property management managers for this undertaking.

The data that is available is an inventory database that includes a listing of all projects, properties, their cost, package price, current status, and sales date. Another database provided is the project management database that tracks the construction initiation, time lapsed till the project is at 90% completion, and another date that tags it at 100% completed. Lastly, the collections database includes a listing of all units that are tagged as sold and tracks the turnover date (the date that the unit was turned over to the owner), collection date (the date that the full amount was based on the package price and all other charges) was collected from the buyer through multiple channels.

I created a web scraper to gather data from entry-level data analyst job postings from LinkedIn, as part of my data analyst portfolio project. I collected data on jobs related to Data analytics and Business Intelligence during the spring of 2023. This dataset is a small sample of the original data collected.

View a detailed description of the project and the analysis of the full data at:

https://terencicp.github.io/linkedin

Data exploration, cleaning, and arrangement with Covid Death and Covid Vaccination which is involved:

1. Data that going to be using

2. Shows the likelihood of dying if you contract covid in your country

3. Show what percentage of the population got Covid

4. Looking at Countries with the Highest Infection Rate compared to the Population

5. Showing the Country with the Highest Death Count per Population

6. Break things down by continent

7. Continents with the Highest death count per population

8. Looking at Total Population vs Vaccinations

9. Used CTE and Temp Table

10. Creating View to store data for later visualizations

Abstract This study presents a managerial tool for prioritization and portfolio selection of software development projects using the methodology Analytic Hierarchy Process (AHP). The need for better results with scarce resources is a challenge for organizations to generate competitive advantage. The tool is structured according to the analysis of articles related to project prioritization and selection, portfolio management and the AHP methodology. The research approach was quantitative through an applied case study. The case was developed in a medium-sized software company in Santa Catarina, a leader in solutions for management excellence, provider of software and services for automation and business process improvement, regulatory compliance, and corporate governance. It has more than 2000 clients, of diverse sizes and lines of action. A committee was set up with managers and analysts to define the groups and criteria, and the application of a pilot of projects. There were opportunities to use this managerial tool to minimize power play, integration, information sharing, learning, commitment among decision makers and selection of strategically aligned projects.

The dataset contains information related to supply chain operations, including orders, products, inventory, suppliers, logistics, and demand. It aims to optimize supply chain efficiency and improve performance through predictive analytics, inventory management, and logistics optimization.

This dataset provides detailed information on retrocessional reinsurance programs, including treaty types, financial structures, pricing methodologies, and market capacity. It enables analysis of risk transfer strategies among reinsurers, supports benchmarking, and facilitates regulatory compliance and risk management. The dataset is ideal for actuaries, risk managers, and insurance market analysts seeking to understand retrocession market dynamics.

This is a cleaned and structured dataset for a real-world data analytics project designed around ML Dental Clinic, a fictional but highly realistic dental clinic based in Tilak Nagar, West Delhi.

🦷 Dataset Highlights: - Covers 896 patient records from Jan 2023 to Dec 2024 - Includes demographics, visit dates, treatments, doctors, billing, discounts, and due amounts - Treatment handled by 2 doctors: Dr. Kajal (Implantologist) and Dr. Karan (Oral Surgeon) - Realistic pricing and billing logic (OPD-only charges, waived fees on treatment, free camps, etc.) - Built for data cleaning, SQL querying, Python analysis, and Power BI dashboard creation

✅ Use cases: - Healthcare analytics practice - MySQL or Power BI dashboard creation - End-to-end data analyst portfolio projects - Freelance healthcare reporting automation

🛠 Tech Stack Used in Project: - Python (Pandas, Matplotlib, Seaborn) - MySQL Workbench - Power BI - Excel

📌 GitHub Project Link:
https://github.com/kumararjunjha/ML-Dental-Clinic-Data-Analysis

👨‍💻 Created by: Arjun Jha
🔍 Aspiring Freelance Data Analyst | Healthcare Data Projects | Portfolio-ready work
📬 Reach out on LinkedIn: https://linkedin.com/in/kumararjunjha

Let me know what insights you discover with this data!

Key Features

• Interactive Dashboard: Navigate through the data seamlessly with an interactive and user-friendly dashboard.
• Sales Performance Metrics: Track and analyze sales performance metrics, including revenue, conversion rates, and customer acquisition.
• Product Analysis: Gain insights into product performance, identify best-sellers, and optimize the product portfolio.
• Customer Segmentation: Understand customer behavior through segmentation, enabling targeted marketing strategies.

Cyclistic Bike-Share Dataset (2022–2024) – Cleaned & Merged

This dataset contains three full years (2022, 2023, and 2024) of publicly available Cyclistic bike-share trip data. All yearly files have been cleaned, standardized, and merged into a single high-quality master dataset for easy analysis.

The dataset is ideal for:

• Data Analysis & Visualization
• SQL Projects
• Python (Pandas) Practice
• Power BI, Tableau Dashboards
• Machine Learning Feature Engineering

🔹 Key Cleaning & Processing Steps - Removed duplicate records - Handled missing values - Standardized column names - Converted date-time formats - Created calculated columns (ride length, day, month, etc.) - Merged yearly datasets into one master CSV file (3.17 GB)

🔹 What You Can Analyze - Member vs Casual rider behavior - Peak riding hours and days - Monthly & seasonal trends - Trip duration patterns - Station usage & demand forecasting

This dataset is especially useful for data analyst portfolio projects and technical interview preparation.

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...

The Bike Purchasing Dataset I cleaned, filtered, and visualized examined bike purchases made by customers. The dataset included details of the customers, including marital status, gender, income, age, commute distance, region and whether or not if they made a bike purchase.

Here is a link to the data source on Github: https://github.com/AlexTheAnalyst/Excel-Tutorial/blob/main/Excel%20Project%20Dataset.xlsx

****Dataset Overview – LinkedIn Survey of Data Professionals****

The dataset is derived from a LinkedIn-based survey targeting professionals in the data field, including Data Analysts, Data Scientists, Data Engineers, and others. It provides valuable insights into career trends, salary expectations, educational backgrounds, and tool preferences among respondents.

This dataset originates from Alex Freberg's Power BI tutorial project (credits and links provided in the video description). It serves as an excellent resource for beginners looking to build standalone visualization projects using Power BI or Tableau. The dataset allows users to showcase data storytelling, interactive dashboard design, and visualization skills effectively;

Skills which can be displayed;

•Data transformation using Power Query •Data cleaning using Power BI(unstandardized information,missing data,unnecessary and empty columns) •Usage of DAX formulas for Data Exploration

Key Columns in the Dataset:

Dataset contains a wide range of valuable information, some columns (such as "Email," "City," and "Referrer") are intentionally left blank or contain incomplete data, as they are either not essential for analysis or were anonymized to protect respondent privacy. These fields can typically be excluded during data cleaning and preprocessing stages without impacting the integrity of the insights drawn from the core survey questions.

Timestamp – When the response was recorded. Unique ID Email Date Taken (America/New_York) Time Taken (America/New_York) Browser OS City Country Referrer Time Spent Q1 - Which Title Best Fits your Current Role? Q2 - Did you switch careers into Data? Q2 - Did you switch careers into Data? Q3 - Current Yearly Salary (in USD) Q4 - What Industry do you work in? Q5 - Favorite Programming Language Q6 - How Happy are you in your Current Position with the following? (Salary) Q6 - How Happy are you in your Current Position with the following? (Coworkers) Q6 - How Happy are you in your Current Position with the following? (Management) Q6 - How Happy are you in your Current Position with the following? (Upward Mobility) Q6 - How Happy are you in your Current Position with the following? (Learning New Things) Q7 - How difficult was it for you to break into Data? Q8 - If you were to look for a new job today, what would be the most important thing to you? Q9 - Male/Female? Q10 - Current Age Q11 - Which Country do you live in? Q12 - Highest Level of Education Q13 - Ethnicity

Purpose of the Dataset:

To explore career dynamics and compensation trends in the data industry. To understand how skills, tools, education, and location correlate with salaries and satisfaction.

Credits: Power BI Portfolio Project by Alex The Analyst: https://www.youtube.com/watch?v=I0vQ_VLZTWg&t=6506s Alex's Github for Power BI tutorial: https://github.com/AlexTheAnalyst/PowerBI/blob/main/Power%20BI%20-%20Final%20Project.xlsx

This dataset provides a comprehensive view of retail operations, combining sales transactions, return records, and shipping cost details into one analysis-ready package. It’s ideal for data analysts, business intelligence professionals, and students looking to practice Power BI, Tableau, or SQL projects focusing on sales performance, profitability, and operational cost analysis.

Dataset Structure

Orders Table – Detailed transactional data

Row ID

Order ID

Order Date, Ship Date, Delivery Duration

Ship Mode

Customer ID, Customer Name, Segment, Country, City, State, Postal Code, Region

Product ID, Category, Sub-Category, Product Name

Sales, Quantity, Discount, Discount Value, Profit, COGS

Returns Table – Return records by Order ID

Returned (Yes/No)

Order ID

Shipping Cost Table – State-level shipping expenses

State

Shipping Cost Per Unit

Potential Use Cases

Calculate gross vs. net profit after considering returns and shipping costs.

Perform regional sales and profit analysis.

Identify high-return products and loss-making categories.

Visualize KPIs in Power BI or Tableau.

Build predictive models for returns or shipping costs.

Source & Context The dataset is designed for educational and analytical purposes. It is inspired by retail and e-commerce operations data and was prepared for data analytics portfolio projects.

License Open for use in learning, analytics projects, and data visualization practice.

I took this data from the official website globalfirepower.com and I chose some important points for this dataset. The data covers 145 countries with their respective PowerIndex. You can use this data to create a Data Analyst Portfolio Project. Hopefully helpful, and thank you :)

Well this is my first dig at creating an online portfolio on kaggle and this dataset uploaded, is part of my capstone project under the Google Data Analytics Professional Certification program offered on coursera. The dataset includes trip information collected by the City of Chicago along with Lyft Bikes and Scooters, LLC (“Bikeshare”), the popular mode of transportation preferred by the city dwellers. For educational purposes, we are using the arbitrary name Cyclistic, to represent a company that provides the bike share services in the city. The marketing team is posed with the task of converting casual riders to member riders based on their service usage metrics. As the marketing teams junior data analyst per the capstone project, I'm assigned with the task of analyzing and creating visualizations for deploying new marketing strategies, to increase the annual members count for Cyclistic.

I have used multiple tools like Microsoft Excel, R Studio, Tableau to prepare, process, analyze and visualize the dataset.

Welcome to a comprehensive dataset of Data Analyst job roles across Canada! This dataset provides a unique glimpse into the job market, capturing essential details like salary ranges, required skills, programming languages, job titles, employers, and much more.

Datasets:

- Raw_Dataset.csv:
This is the untouched, unprocessed data directly scraped from Indeed and Glassdoor. It’s the perfect starting point for those looking to demonstrate their data transformation skills by cleaning and refining messy, real-world data.

- Cleaned_Dataset.csv:
This is the refined and transformed version of the raw dataset, ready for insightful analysis and visualization. Ideal for those focusing on data storytelling and visualization.

Featured Columns in the Clean Dataset:

• Job Title: A generalized job title that encapsulates the role.
• Job Info: The exact job title as listed on the job sites.
• Position: The specific role or category the job falls under.
• Employer: The name of the hiring company.
• City: The job's location.
• Province: The abbreviated province name corresponding to the city.
• Skill: The programming languages and tools required for the job.
• Seniority: The job's seniority level (Senior, Mid, Junior, any).
• Work Type: Specifies if the job is Remote, In-person, or Hybrid.
• Industry Type: The industry to which the employer belongs.
• Min Salary: The lowest salary listed (as a float).
• Max Salary: The highest salary listed (as a float).
• Average Salary: The mean salary (as a float).

Inspiration

I recently joined the Junior Data Analyst program at NPower, and I was eager to bolster my portfolio with a project that showcases real-world data. This dataset is perfect for highlighting my data extraction, cleaning, visualization, and storytelling skills.

Some Ideas for Exploration:

• What Data Analyst job titles and roles are currently the most in demand?
• What are the must-have skills to land a Data Analyst job today?
• What salary can you expect for different Data positions, and how do work type and experience level affect it?

Acknowledgements

If you use this dataset, please support me on Github , or follow me on Kaggle.

Image by DC Studio on Freepik

This is a part of the capstone project for the professional certificate on “Google Data Analytics” offered through Coursera. This will be a great chance to apply the practices and procedures associated with the data analysis process to a given set of data. I am on the way to demonstrate my ability to handle real-life data as a junior data analyst; and this is going to be the first of my online portfolio.

Here, the case study context is, I am 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, my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve my recommendations, so they must be backed up with compelling data insights and professional data visualizations.