Kingsley.xlsx

| Web3📈 | Aspiring Data Scientist 📈 || SQL || PowerBi || Excel || Python ||

[Kingsley.xlsx @sleevstheworld]

I'm gonna start documenting my journey on this space, no matter how small it looks
be 1% better every day #sql #dataanalytics

[Kingsley.xlsx @sleevstheworld]

My portfolio btw:
https://t.co/MMb7StBNaO

[Kingsley.xlsx @sleevstheworld]

I'm gonna start documenting my journey on this space, no matter how small it looks
be 1% better every day #sql #dataanalytics

[Eyo Eyo, PhD @Eyowhite3]

Job-ready data analytics project with dataset:

⤵️

[Khadijat sadiq 📊 @Data_girrl]

Learn

[Freedom | Excel Boss @ObohX]

Every Monday morning, Aisha, the VP of Sales at a fast-growing B2B hardware company, joins the executive leadership meeting with the same question:

*"How healthy is our pipeline?"*

It sounds simple.

But getting the answer wasn't.

Sales data lived in different CSV files.

Account information sat in one report.

Product details lived in another.

Sales team records were maintained separately.

And the pipeline itself existed in a transactional export filled with typos, missing values, and incomplete deals still in progress.

The team wasn't struggling because they lacked data.

They were struggling because they lacked a single source of truth.

Without it, critical questions took hours to answer:

* Are discounts helping us win more deals?
* Which products generate the most revenue?
* How long does it take to close a deal?
* Which sales managers are outperforming their targets?
* Where are opportunities getting stuck in the pipeline?

To solve this, I built an end-to-end sales analytics solution in Excel using Power Query and Power Pivot.

The project started with four raw CSV files:

• Accounts

• Products

• Sales Teams

• Sales Pipeline

Using Power Query, I designed an ETL process to clean, standardize, and prepare the data for analysis.

This included:

* Correcting data inconsistencies and typos
* Preserving blank values that represented active opportunities
* Standardizing text fields and data types
* Replacing missing account values
* Creating a dedicated calendar table for time intelligence

Once transformed, the data was modeled into a Star Schema using Power Pivot.

Instead of working with one large spreadsheet, the solution separated transactional data from descriptive data using Fact and Dimension tables connected through relationships.

The model was then enriched with DAX calculations to answer key business questions, including:

* Total Revenue
* Win Rate
* Average Days to Close
* Average Discount Percentage
* Quarter-over-Quarter Growth

One of the most interesting insights came from the Agent Efficiency Matrix.

By comparing average discount percentages against win rates, we discovered that higher discounts had little impact on deal success.

The assumption that "bigger discounts close more deals" simply wasn't supported by the data.

The final dashboard provides executives with a single, interactive view of sales performance through:

• Revenue and pipeline KPIs

• Funnel analysis of deal stages

• Product performance rankings

• Agent efficiency analysis

• Dynamic quarter and manager filters

• Automated narrative insights that update based on user selections

What used to take multiple files and hours of manual reporting can now be answered in seconds.

Because effective analytics isn't about building more charts.

It's about connecting data, asking better questions, and creating systems that help people make decisions faster.

**Tools Used:** Excel, Power Query, Power Pivot, DAX, PivotTables, PivotCharts, Slicers

#DataAnalytics #Excel #Powerquery

[Kingsley.xlsx @sleevstheworld]

@FabrizioRomano Bro isnt human

[Kingsley.xlsx @sleevstheworld]

@IsraelDcoder @ahiakwo_john ok

[Kingsley.xlsx @sleevstheworld]

@ObohX @Lima_D_Analyst @TheBoyAnalyst Teach me, idan

[Kingsley.xlsx @sleevstheworld]

Understanding data mechanics isn't just about writing code—it's about turning raw logs into operational strategy (optimizing server scaling and midday workforce staffing).

Full code is live on my GitHub! Check it out here: https://t.co/t0ZpmWc580

#DataAnalytics #Python #SQL

[Kingsley.xlsx @sleevstheworld]

How do you analyze 800,000+ rows of raw, messy e-commerce data without crashing your system? 💻

You drop Excel and open up Python and SQL.

Here is how I synthesized a multi-year retail dataset to find hidden revenue trends and optimize business workflows.👇

[Kingsley.xlsx @sleevstheworld]

The Results? Two massive takeaways for stakeholders:

Q4 Congestion: November grossed over 2.5x the volume of baseline spring months (Lock in logistics early!).

Peak Traffic: 38% of daily transactions happened between 11:00 AM and 1:00 PM.

[Annie🦋 @DabereNnamani]

Starting your Data Analytics journey?

I’ve put together a beginner-friendly starter kit to help you get started faster.

Inside:
• 3 intro videos on Data Analytics
• 2 helpful PDFs
• 4 templates, including a learning tracker

Get access here: https://t.co/LqIrD2e2GJ

[Reads with Ravi @readswithravi]

I’m in love with this sentence:

“One of the most underrated skills you can learn is the ability to ignore your mood and stick with the plan.”

[Kingsley.xlsx @sleevstheworld]

@Femiforge Highest

[Kingsley.xlsx @sleevstheworld]

@Mbadiwejesse very helpful