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.

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.

Ask

How do annual members and casual riders use Cyclistic bikes differently?

Guiding Question:

What is the problem you are trying to solve?
  How do annual members and casual riders use Cyclistic bikes differently?
How can your insights drive business decisions?
  The insight will help the marketing team to make a strategy for casual riders

Prepare

Guiding Question:

Where is your data located?
  Data located in Cyclistic organization data.

How is data organized?
  Dataset are in csv format for each month wise from Financial year 22.

Are there issues with bias or credibility in this data? Does your data ROCCC? 
  It is good it is ROCCC because data collected in from Cyclistic organization.

How are you addressing licensing, privacy, security, and accessibility?
  The company has their own license over the dataset. Dataset does not have any personal information about the riders.

How did you verify the data’s integrity?
  All the files have consistent columns and each column has the correct type of data.

How does it help you answer your questions?
  Insights always hidden in the data. We have the interpret with data to find the insights.

Are there any problems with the data?
  Yes, starting station names, ending station names have null values.

Process

Guiding Question:

What tools are you choosing and why?
  I used R studio for the cleaning and transforming the data for analysis phase because of large dataset and to gather experience in the language.

Have you ensured the data’s integrity?
 Yes, the data is consistent throughout the columns.

What steps have you taken to ensure that your data is clean?
  First duplicates, null values are removed then added new columns for analysis.

How can you verify that your data is clean and ready to analyze? 
 Make sure the column names are consistent thorough out all data sets by using the “bind row” function.

Make sure column data types are consistent throughout all the dataset by using the “compare_df_col” from the “janitor” package.
Combine the all dataset into single data frame to make consistent throught the analysis.
Removed the column start_lat, start_lng, end_lat, end_lng from the dataframe because those columns not required for analysis.
Create new columns day, date, month, year, from the started_at column this will provide additional opportunities to aggregate the data
Create the “ride_length” column from the started_at and ended_at column to find the average duration of the ride by the riders.
Removed the null rows from the dataset by using the “na.omit function”
Have you documented your cleaning process so you can review and share those results? 
  Yes, the cleaning process is documented clearly.

Analyze Phase:

Guiding Questions:

How should you organize your data to perform analysis on it? The data has been organized in one single dataframe by using the read csv function in R Has your data been properly formatted? Yes, all the columns have their correct data type.

What surprises did you discover in the data?
  Casual member ride duration is higher than the annual members
  Causal member widely uses docked bike than the annual members
What trends or relationships did you find in the data?
  Annual members are used mainly for commute purpose
  Casual member are preferred the docked bikes
  Annual members are preferred the electric or classic bikes
How will these insights help answer your business questions?
  This insights helps to build a profile for members

Share

Guiding Quesions:

Were you able to answer the question of how ...

Introduction

This is a case study called Capstone Project from the Google Data Analytics Certificate.

In this case study, I am working as a junior data analyst at a fictitious bike-share company in Chicago called Cyclistic.

Cyclistic is 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.

Scenario

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, our team will design a new marketing strategy to convert casual riders into annual members.

****Primary Stakeholders:****

1: Cyclistic Executive Team

2: Lily Moreno, Director of Marketing and Manager

ASK

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

# Prepare

The last four quarters were selected for analysis which cover April 01, 2019 - March 31, 2020. These are the datasets used:

Divvy_Trips_2019_Q2
Divvy_Trips_2019_Q3
Divvy_Trips_2019_Q4
Divvy_Trips_2020_Q1

The data is stored in CSV files. Each file contains one month data for a total of 12 .csv files.

Data appears to be reliable with no bias. It also appears to be original, current and cited.

I used Cyclistic’s historical trip data found here: https://divvy-tripdata.s3.amazonaws.com/index.html

The data has been made available by Motivate International Inc. under this license: https://ride.divvybikes.com/data-license-agreement

Limitations

Financial information is not available.

Process

Used R to analyze and clean data

• After installing the R packages, data was collected, wrangled and combined into a single file.
• Columns were renamed.
• Looked for incongruencies in the dataframes and converted some columns to character type, so they can stack correctly.
• Combined all quarters into one big data frame.
• Removed unnecessary columns

Analyze

• Inspected new data table to ensure column names were correctly assigned.
• Formatted columns to ensure proper data types were assigned (numeric, character, etc).
• Consolidated the member_casual column.
• Added day, month and year columns to aggregate data.
• Added ride-length column to the entire dataframe for consistency.
• Deleted trip duration rides that showed as negative and bikes out of circulation for quality control.
• Replaced the word "member" with "Subscriber" and also replaced the word "casual" with "Customer".
• Aggregated data, compared average rides between members and casual users.

Share

After analysis, visuals were created as shown below with R.

Act

Conclusion:

• Data appears to show that casual riders and members use bike share differently.
• Casual riders' average ride length is more than twice of that of members.
• Members use bike share for commuting, casual riders use it for leisure and mostly on the weekends.
• Unfortunately, there's no financial data available to determine which of the two (casual or member) is spending more money.

Recommendations

• Offer casual riders a membership package with promotions and discounts.

File List Wolf code.r – Source code to run wolf analysis Description This is provided for illustration only, the wolf data are not offered online. The code operates on a data frame in which rows correspond to points in space. The data frame contains a column for use (1 for a telemetry observation, 0 for a control point selected from the wolf’s home range). It also contains columns for x and y coordinates of the point, environmental covariates at that location, wolf ID and wolf pack membership. 1. Data frame preparation The data set is first thinned, for computational expediency, the covariates are standardized to improve convergence and the data frame is augmented with columns for wolf-pack-level covariate expectations (required by the GFR approach). 2. Leave-one-out validation The code allows the removal of a single wolf from the data set. Two models (one with just random effects, the second with GFR interactions) are fit to the data and predictions are made for the missing wolf. The function gof() generates goodness-of-fit diagnostics.

📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

File List community_assembly_simulations.txt

Description A set of functions in the R programming language for simulating community assembly randomly (with or without weighting species by abundance), by trait-based competition, or by trait-based habitat filtering. The linked text file contains a series of R functions, a brief demonstration of the functions, commentary on the algorithms, and references. There are three key functions, described below, and a series of smaller functions that support them. More detailed descriptions can be found as in the file itself. random_assembly(pool, final_richness, abund=NULL)

  Randomly samples final_richness number of species from a vector of species names given in pool. Sampling is occurence weighted if a vector of abundances is specified in the abund argument- default is no abundance weighting. 

  compete_until(nfinal, community)

  Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a competition algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the most similar pair of species based on trait similarity and randomly removes one. Ties are broken randomly.

 filter_until(nfinal, community, optima)

  Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a habitat filtering algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the species that is farthest from the trait optima and removes it. Ties are broken randomly. The community can have from 1 to many traits, but the optima vector must have the same number of elements as the number of traits in the community dataframe.

Young and older adult vowel categorization responses

https://doi.org/10.5061/dryad.brv15dvh0

On each trial, participants heard a stimulus and clicked a box on the computer screen to indicate whether they heard "SET" or "SAT." Responses of "SET" are coded as 0 and responses of "SAT" are coded as 1. The continuum steps, from 1-7, for duration and spectral quality cues of the stimulus on each trial are named "DurationStep" and "SpectralStep," respectively. Group (young or older adult) and listening condition (quiet or noise) information are provided for each row of the dataset.