Introduction

For The Google Data Analytics Capstone Project, I will be working with BellaBeat, a high-tech company that manufactures health-focused smart products for women. I am a junior data analyst working in the marketing analytics team at Bellabeat. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market.  A co-founder of Bellabeat believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. 

My project goal is to analyze smart device data to gain insight into how consumers are using their smart devices. The insights will help guide the marketing strategy for the company.  I will be using Excel to clean and analyze the data, import the data to BigQuery using SQL queries to analyze the data, and then use Tableau to create the dashboard and recommendations.

Ask

The business tasks

To analyze smart device usage data to understand how consumers use non-Bellabeat smart devices. Then select one Bellabeat product to apply these insights in the final presentation with recommendations. 

Prepare

I will use public data that explores smart device users’ daily habits. The first step of the Prepare stage is to download all of the data that I will need for the analysis. The data set FitBit Fitness Tracker Data: this Kaggle data set contains a personal fitness tracker from 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. 

Process

Data Cleaning

Excel & Google Sheets

To ensure the data was clean and ready for analysis, I checked every file from the data set and performed cleaning steps in Excel and Spreadsheet documenting the process in the Google Doc. Some cleaning steps include:

• Remove duplicates

• Time, date, number format adjustment

• Text-to-column function

• SPLIT, MID, and RIGHT functions to split/extract date and time 

Analyze

For Analysing Stage I used Google BigQuery and Pivot Tables in Excel. When the data had been prepared for analysis I uploaded three files (daily_activity, sleep_day, heart_rate) to Google BigQuery.  For SQL code please refer to the SQL file here. 

Share

Act

Main insights from the analysis of FitBit usage smart app:

• 33 distinct app users in general

• 7 distinct users logged the heartrate info

• 8 users logged their weight info

• 24 users logged sleeping records. On average users added their sleep records 19 times (1-3 records per day) during one month

• Average total minutes slept - 420 minutes or 7 hours. The average time in a bed is 458 minutes or ~ 7 hours 40 minutes

• 7637.91 - average total steps among all users

• The most steps were taken on Saturday and Tuesday, the least - on Sunday

• A sedentary lifestyle takes the most of the total activity (peak on Monday)

Conclusions and Recommendations

1. The dataset lacks information: a too small number of respondents - only 33, and we don't know the users' gender and location

2. Only 7 users have heart rate records, we can assume that many users declined to sync heart rate track information with an app (probably because the battery goes low fast). This information can be used for marketing campaigns. BellaBeat has a smart no-display bracelet - Ivy. Ivy’s battery life exceeds all screen-based trackers by ten times. 

3. Since users take few steps on Monday-Sunday, we can work on the push notifications these days to remind them about a daily walk

4. Based on the activity level information we saw that a major part of users have a sedentary lifestyle. This information also can be used in marketing aims (targeting, push notifications, call to action, and ads messages)