-I found out that Adults aged 35-49 generated the highest revenue even more than Elderly customers despite having fewer transactions.
#Learninginpublic#LearningExcel
Day 13/30 of #LearningDataAnalytics (Excel)
Continued the customer analysis project todayπ:
-I grouped customers by how much they spend, then checked which groups those spending patterns belong to.
Day 12/30 of #LearningDataAnalytics (Excel)
Today I worked on a full analysis project π
-Explored a real customer dataset
-Derived new columns: age bands, customer value, order weekday, year-month
-Used SUMIFS to break down revenue by country, gender, and age group.
Day 12/30 of #LearningDataAnalytics (Excel)
Today I worked on a full analysis project π
-Explored a real customer dataset
-Derived new columns: age bands, customer value, order weekday, year-month
-Used SUMIFS to break down revenue by country, gender, and age group.
Day 11/30 of #LearningDataAnalytics (Excel)
Today I learnt π:
-Power Query editor Interface
- Use of Power Query
-How power query works
Power query is used for turning messy datasets into clean, structured data thatβs ready for analysis.
#LearningInPublic#LearningExcel
Day 11/30 of #LearningDataAnalytics (Excel)
Today I learnt π:
-Power Query editor Interface
- Use of Power Query
-How power query works
Power query is used for turning messy datasets into clean, structured data thatβs ready for analysis.
#LearningInPublic#LearningExcel
Day 10/30 of #LearningDataAnalytics (Excel)
Today I practiced π:
-VLOOKUP
-IFERROR
-INDEX/ MATCH
Started learning how to combine functions together. Small shift, but changing the way I think about formulas.
#LearningInPublic#LearningExcel
πππ§π ππ‘πππ¬π¦ππ¦ π£π₯π’πππ¦π¦:
β οΈDefine the problem
β οΈCollect the data
β οΈData Cleaning
β οΈData Analysis & Visualization
β οΈInterpret the results
β οΈCommunicate the results
π₯Did I miss any ?