Day 2/10
Explored the dataset today.
Two tables:
• users
• user_monthly
Initial checks in Excel:
• dataset size
• column structure
• missing values
• duplicates
Next step: move data into SQL for cleaning.
#DataAnalytics#SQL
Day 1/10
Starting a new data project:
“Why Subscribers Quit? Tackling the High Cost of Losing Users 📉”
Over the next 10 days I’ll analyze a SaaS dataset using:
Excel • SQL • Power BI
Goal: understand why users churn.
Building in public.
#DataAnalytics#BuildInPublic
Data analysis is real brainwork.
It’s not just SQL + Power BI.
It’s asking:
Is this revenue profitable?
Is it sustainable?
Currently breaking down my Superstore project 👀
Results soon.
#DataAnalytics#SQL#PowerBI
MySQL kept throwing incorrect datetime value.
Found the issue:
Two date formats in one column (m-d-Y & Y-m-d).
Used REGEXP to standardize before changing the datatype.
Lesson: clean data first. 📊
#SQL#DataCleaning#LearningInPublic
Learning data analysis taught me one thing fast:
You can’t analyze dirty data.
Spent today cleaning a layoff dataset using SQL.
Small progress, real skills.
SQL level up: Mastered CTEs today! 🚀
Used the WITH clause to clean up a complex join and analyze average salaries by gender. Much more readable than nested subqueries!
Small steps, big progress. 📈
#SQL#DataAnalytics#100DaysOfCode#DataScience
Today is a new day in my data analysis journey.
Learning data analysis isn’t just about tools;
it’s about thinking clearly, asking better questions, and communicating insight.
Progress comes from showing up daily
New chapter ✨
Officially Google Data Analytics certified.
Learning to ask better questions, turn data into insight, and make decisions with clarity.
More data thoughts coming.