@Maggiewitdsauce@jon_d_doee This is the only authentic and realistic advise I have seen so far.
It was the same thing I would have said. Nothing more , nothing less.
End.
๐ Chivita Sales & Distribution Performance Review.
A full sales analytics deep-dive, from raw data to dashboard to written report
https://t.co/yeY4vBgeih
#SQL#DataAnalytics#PortfolioProject
Sharing 2 recent projects from my portfolio ๐
๐ฅ MediCare Plus: a fully normalised SQL Server healthcare database (3NF, stored procs, views, triggers, RBAC, a data warehouse + ETL, and a 93% query speed-up via indexing)
https://t.co/qATpMN2H6T
I analyzed a logistics dataset with PowerBi and found why deliveries get delayed ๐
At first glance, everything looked fine.
Shipments were moving. Deliveries were happening. But the data told a different story.
Then turned it into actionable recommendations:
Reduce Lagos congestion
Improve last-mile delivery
Rebalance transport usage
This project changed how I think about logistics. Itโs not about movement. Itโs about coordination.
Week 7 of my Python data analytics journey focused on data cleaning and feature engineering using Pandas.
I worked with a supply chain dataset to handle missing values, convert data types, and clean messy text fields to ensure consistency.
Beyond cleaning, I built additional analytical features like delivery speed categories, order age, and comparisons between order costs and product category averages.
Week 6 of my Python Data Analysis journey focused on Pandas for data exploration and transformation.
I practiced loading datasets, filtering records, indexing data, creating calculated columns, merging tables, and performing group analysis.
. Applied 5% shipping markup and ran full statistical analysis
. Identified above-average cost products and outliers
. Compared suppliersโ performance & modeled profit with different margins
#TheDataImmersed@TDataImmersed@DabereNnamani@ThePSF
Week 5: NumPy for Supply Chain Analytics
This week, I used NumPy to analyze a cleaned supply chain dataset:
. Extracted costs, quantities & lead times into arrays
. Calculated total revenue, average order value, min/max costs & lead time range