Google Data Analytics: Bellabeat Case Study
Google Data Analytics: Bellabeat Case Study
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End of the Google Data Analytics Course I Completed a Case Study
End of the Google Data Analytics Course I Completed a Case Study
Case Study Consist of 6 phases these are: Ask, Prepare, Process, Analyze, Share, Act
Scenario
Scenario
You are a junior data analyst working on the marketing analyst team at Bellabeat, a high-tech manufacturer of health-focused products for women. Bellabeat is a successfull small company, but they have the potential to become a larger player in the global smart device market. Urska Srsen, cofounder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. You have been asked to focus on one of Bellabeat's products and analyze smart discover will then help guide marketing strategy for the company. You will present your analysis to the Bellabeat executive team along with your high-level recommendations for Bellabeat's marketing strategy.
Products
Products
Bellabeat app: The Bellabeat app provides users with health data related to their activity, sleep, stress, menstrual cycle, and mindfulness habits. This data can help users better understand their current habits and make healthy decisions. The Bellabeat app connects to their line of smart wellness products.
Leaf: Bellabeat’s classic wellness tracker can be worn as a bracelet, necklace, or clip. The Leaf tracker connects to the Bellabeat app to track activity, sleep, and stress.
Time: This wellness watch combines the timeless look of a classic timepiece with smart technology to track user activity, sleep, and stress. The Time watch connects to the Bellabeat app to provide you with insights into your daily wellness.
Spring: This is a water bottle that tracks daily water intake using smart technology to ensure that you are appropriately hydrated throughout the day. The Spring bottle connects to the Bellabeat app to track your hydration levels.
Bellabeat membership: Bellabeat also offers a subscription-based membership program for users. Membership gives users 24/7 access to fully personalized guidance on nutrition, activity, sleep, health and beauty, and mindfulness based on their lifestyle and goals.
Ask Phase
Ask Phase
Sršen asks you to analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices. She then wants you to select one Bellabeat product to apply these insights to in your presentation.
Clear Statement of Business Task : Perform data analysis on smart device usage data and make data-driven decision based on your analysis.
Prepare Phase
Prepare Phase
Sršen encourages you to use public data that explores smart device users’ daily habits. She points you to a specific data set: https://www.kaggle.com/datasets/arashnic/fitbit/: This Kaggle data set contains personal fitness tracker from thirty fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits. Sršen tells you that this data set might have some limitations, and encourages you to consider adding another data to help address those limitations as you begin to work more with this data.
In this phase i am going to do three things:
1.Download and store data properly.
2.Identify how it’s organized.
3.Sort and filter the data.
Step 1 : In this project i decided to use RStudio via Cloud for my analysis. So i upload data to cloud for use.
Step 2: There is databases on daily basis, by minute, by hour, by second.
Step 3: When i looked the datasets, i found these dataset already sorted by Id and ActivityDay columns.
Process Phase
Process Phase
In this phase we need clean or manipulate our dataset for getting rightful information from data. I already said i am going to use Rstudio.
Start with Daily Activity. Executing code below and learn column names. And getting the result shown in image.
Start with Daily Activity. Executing code below and learn column names. And getting the result shown in image.
df_daily_activity <- read_csv(daily_Activity_merged.csv)
colnames(df_daily_activity)
In this dataset i just need 4 column: Id,ActivityDate,TotalSteps,Calories. So i will create a new dataset for use. I believe this columns gives us a proper data story. Executing code chunk below showing us result in the image.
In this dataset i just need 4 column: Id,ActivityDate,TotalSteps,Calories. So i will create a new dataset for use. I believe this columns gives us a proper data story. Executing code chunk below showing us result in the image.
clean_daily_activity <- df_daily_activity %>% select(Id,ActivityDate,TotalSteps,Calories)
View(clean_daily_activity)
When i looking all the datasets and inspecting them decide to use “dailyActivity_merged, dailyCalories_merged, dailySteps_merged, sleepDay_merged, weightLogInfo_merged” Now i am going to look all datasets that i choose whether there is a null value or not.
When i looking all the datasets and inspecting them decide to use “dailyActivity_merged, dailyCalories_merged, dailySteps_merged, sleepDay_merged, weightLogInfo_merged” Now i am going to look all datasets that i choose whether there is a null value or not.
I learn there is null values inside of df_weight_log, now i am going to learn where is that null values.
When i inspecting dataset i learn “fat” named column just have 2 non-null value in all dataset. I decided to delete “fat” column and also there is other columns named “WeightPound”(because analysis continue with kilogram), “IsManualReport”, “LogId” Now our dataset ready to go, let’s move onto next phase.
Analyze and Share Phase
Analyze and Share Phase
In this phase of our process, i will deeply analyze datasets to find some relationships or trends. Then sharing these relationships and trends via visualizations.
In this phase of our process, i will deeply anaylze datasets to find some relationships or trends. Firstly i need to know how many people out there in datasets. For learn to this information i use code chunk beneath.
In this phase of our process, i will deeply anaylze datasets to find some relationships or trends. Firstly i need to know how many people out there in datasets. For learn to this information i use code chunk beneath.
nrow("Dataset_Name" %>% distinct(Id))
With these results i learned there is a data of 33 person in daily_activity, daily_calories, daily_steps datasets. 24 person in daily_sleep dataset and lastly just 8 person in weight_log dataset. Now, i want learn in first 3 datasets does have the same people and same dates.
And also i need to check if these datasets have the same dates or not.
Okay the last thing i want to check daily_activity and daily_steps datasets are have the same total step value or not
All the values matched.
With this info i learn the daily_activity dataset have calories and total steps. So after that i can go on with just three dataset: Daily_activities, Weight_log, Daily_sleep. Let’s start with the Daily Activity dataset. First i will analyze Total Steps vs Calories of all users.
With this info i learn the daily_activity dataset have calories and total steps. So after that i can go on with just three dataset: Daily_activities, Weight_log, Daily_sleep. Let’s start with the Daily Activity dataset. First i will analyze Total Steps vs Calories of all users.
There is a positive relation between Total Steps and Calories Burnt
There is a positive relation between Total Steps and Calories Burnt
I need check users separately
With the information we get from plot we can say Total Steps and Calories Burnt have positive relationship. And some of users below the healthy total step count.
With the information we get from plot we can say Total Steps and Calories Burnt have positive relationship. And some of users below the healthy total step count.
Now I want to use daily_sleep and daily_activities together. Firstly i am going to merge daily_activities and daily_sleep datasets.
Now I want to use daily_sleep and daily_activities together. Firstly i am going to merge daily_activities and daily_sleep datasets.
Results of code chunk above shown in the image.
With the visualize i create and data gathered, i can say that sleeping time and calories or total steps of users not have a relationship.
Now i want to merge weight info and daily activity dataset to analyze.
Now i want to merge weight info and daily activity dataset to analyze.
Result of the code chunk above shown in the image.
Unfortunately, I do not find any relationship or trend between Weight and Calories or Total Steps.
Act Phase
Act Phase
This is the last phase. I will share my final insights and recommendations with you. These are:
There is not enough information for deeply analysis. Especially, sleep and weight datas.
We can encourage users for using smart devices when sleeping. For example we can send alert to users said: “If you want to live healthy life you must pay attention to your sleep times. For more information please wear your smart devices when sleeping.”
And again we can encourage our users for using smart devices to get their weight.
4.And lastly based on available data we can send users alert about steps and calories in daytime. Like “You are the first %10 in all people in number of steps congratulations!” or “You are the last %20 in all people in number of steps, keep going stay healthy!”
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