Morena Mashaba

| Proudly Sotho | We shall be free |

[Morena Mashaba @Realeboha_Pa]

Can we talk to the dead, can they watch over us ? Necromancy is active in our parts in Africa. But what does the Bible say ?

#happysabbath

[Morena Mashaba @Realeboha_Pa]

@HEDrArikana Thia one is a circus 🤡

[Morena Mashaba @Realeboha_Pa]

@FifaWorldCupENG what say you ? How do you ensure this never happens again ?
@FIFAWorldCup

[Morena Mashaba @Realeboha_Pa]

@citezw @PPRIZ2021 @PPRIZ2021 left out the results of the other survey. 99.99% of respondents supported doing away with elections altogether and having Munhumutapa as the life president of Zimbabwe 🤣

[Morena Mashaba @Realeboha_Pa]

@ndues_boy Ndues lawe bafo, it’s imbongi 🙈🤣🤣

[Morena Mashaba @Realeboha_Pa]

@Mathuthu Man was driving a suzuki @11pm. “Brown” car i hear 🤣

[Morena Mashaba @Realeboha_Pa]

@AndyBurnhamGM We congratulate you Andy & we will die labour, but what’s your policy on dehydration breaks ? 😰

[Morena Mashaba @Realeboha_Pa]

@maka_the1st Such a lovely soul she was. RIP !

[Morena Mashaba @Realeboha_Pa]

@MightiJamie Mr M - they could not say out his name at the Madlanga Commission 😭

[Morena Mashaba @Realeboha_Pa]

@zimcelebs1 @Newzroom405 Our economy is doing well 🤔

[Morena Mashaba @Realeboha_Pa]

@drDendere Challenge with diaspora is things can go south very fast & the next minute you are on the dreaded ✈️ back home. Unosvika uchipinda payi, mumba mani ?

[Morena Mashaba @Realeboha_Pa]

That Manhize deal was the worst deal. But well, someone had to eat. Sad thing its a done deal and can’t be reversed 🥲

[Morena Mashaba @Realeboha_Pa]

@Uncle_Augustus_ Hyenas could have done better 😊

[Morena Mashaba @Realeboha_Pa]

Ziyambi na @TembaMliswa vakasamhanya Mwari vane favour 😭

[Morena Mashaba @Realeboha_Pa]

We are happy beyond words 🔥👏🏽👏🏽👏🏽🙏🏻

[Morena Mashaba @Realeboha_Pa]

Our girl did it at the Cdes marathon 🔥
You did us proud Nobuhle Tshuma 👏🏽👏🏽👏🏽

[Morena Mashaba @Realeboha_Pa]

Siyakhubongela dade wethu 👏🏽👏🏽
@SueNyathi

[Morena Mashaba @Realeboha_Pa]

Now it makes sense doesn’t it 🤷🏽‍♂️

[ZimLive @zimlive]

Wicknell Chivayo company, Chinese partner win $2.9 billion Kenya airport tender

♦️ IMC Construction is a joint venture partner with state-owned Chinese conglomerate

https://t.co/ML55QqaSoE

[Morena Mashaba @Realeboha_Pa]

@Mathuthu They must bring him close 🐊

[Morena Mashaba @Realeboha_Pa]

@KMutisi @ajuzah @MadlalaPrince @GondaiMutongi @edmnangagwa @ZANUPF_Official The hypocrisy, iko kakawana yako kare kave kuda kuti vamwe vasawane. @matinyarare go collect your car, not only that, get everything you agreed on including payments to the lawyers.

[Freedom | Excel Boss @ObohX]

Week 4 of EXCEL101 was the week students stopped cleaning data manually and started building systems.

Up until now, we had been working with datasets that were already structured enough to analyze.

This week introduced a different reality:

Data rarely arrives analysis-ready.

It comes from different files, different teams, different systems, and often in different formats.

That's why we spent the week learning "Power Query" — Excel's built-in data transformation engine.

And once students saw what it could do, the conversation changed.

We started with the concept of ETL:

Extract – Bring data in from external sources.

Transform – Clean, standardize, reshape, and enrich the data.

Load – Push the refined dataset into Excel for reporting and analysis.

The most important lesson wasn't the buttons.

It was understanding that Power Query doesn't change your original data.

Every transformation becomes a recorded step.

Fix it once.

Refresh forever.

That's a completely different way of working.

For the practical session, students worked with multiple CSV files based on regional sales data.

The datasets were intentionally messy.

Different date formats.

Inconsistent name formats.

Extra spaces.

Blank rows.

The kind of problems analysts deal with every day.

Step by step, students learned how to:

* Remove blank rows
* Fix incorrect data types
* Standardize text fields
* Create calculated columns
* Generate date-based attributes
* Load clean datasets back into Excel

The highlight for many students was "Appending".

Instead of manually copying and pasting multiple reports into one sheet, they combined separate datasets into a single master table using Power Query.

More importantly, they learned the principle behind it:

Automation only works when structure is respected.

If column names, formats, and data types are inconsistent, automation breaks.

If the structure is right, the process scales.

From there, we moved into "Data Modeling concepts".

Students were introduced to Fact and Dimension tables and the idea that not all data belongs in one giant spreadsheet.

We discussed:

* Fact Tables (transactions and measurable events)
* Dimension Tables (descriptive information)
* Primary Keys
* Foreign Keys

These concepts laid the foundation for one of the most important topics of the week:

Merge.

Many students initially saw Merge as another lookup feature.

By the end of the session, they understood that it is much more than that.

Append adds rows.

Merge adds context.

Append is vertical.

Merge is relational.

We explored different join types and discussed why choosing the wrong join can change the results of an analysis without generating an error.

That insight alone was worth the session.

Then came the assignment.

Students received data from a fictional logistics company containing shipment transactions, customer information, route details, and driver records.

Their task was to:

* Clean and standardize the shipment data
* Merge multiple dimension tables into the fact table
* Create new calculated fields
* Build KPI summaries
* Develop PivotTable reports and charts
* Answer business questions using the transformed dataset

The objective wasn't simply to build reports.

It was to create a repeatable workflow that could handle changing data.

That's what analysts do.

They don't just analyze data.

They design processes that make analysis possible.

Week 4 introduced that mindset.

And with Power Query now in place, students are beginning to see Excel as more than a spreadsheet application.

They're starting to see it as a complete data transformation and reporting platform.

Next up: Power Pivot, Data Modeling, DAX, and KPI Reporting.