Author: Akech Dau Atem
Role: Junior Data Analyst | R & SQL Enthusiast
This repository showcases two complete case studies completed under the Google Data Analytics Professional Certificate.
Each follows the six-step data analysis process: Ask → Prepare → Process → Analyze → Share → Act.
Goal: Understand how casual and annual riders use Cyclistic bikes differently to design marketing strategies for membership conversion.
Tools: R (tidyverse, lubridate, ggplot2)
Data Source: Divvy Trip Data
(Provided by Motivate International Inc. under license for public use in the Google Data Analytics Capstone.)
Highlights:
- Combined and cleaned 12 months of Divvy data.
- Found casual riders take longer, weekend rides; members ride shorter weekday trips.
- Created visual insights for behavior patterns and recommendations.
🔗 Project Links:
- Kaggle Notebook: Cyclistic Case Study Code
- Tableau Dashboard: Cyclistic Interactive Dashboard
File: Cyclistic_Case_Study.md
Goal: Analyze smart device data to guide Bellabeat's marketing strategy using user activity and sleep patterns.
Tools: R (tidyverse, lubridate, skimr, ggplot2)
Data Source: FitBit Fitness Tracker Data on Kaggle
(Open dataset made available by Mobius for the Google Data Analytics Capstone.)
Highlights:
- Merged activity and sleep datasets.
- Found average 8,500 steps/day, 7 hours sleep, and long sedentary time.
- Built plots linking steps, sleep, and calories.
🔗 Project Links:
- Kaggle Notebook: Bellabeat Case Study Code
- Tableau Dashboard: Bellabeat Activity Dashboard
File: Bellabeat_Case_Study.md
R • SQL • Spreadsheets • ggplot2 • Tableau • Markdown • Git & GitHub
- Kaggle: akechatem
- Tableau Public: Akech Atem