Data Transformation with Power BI; From Power Query, Dax to Dashboards

From my previous post, had defined power-bi and talked more about schemas. Schemas fall part of power bi as the create connection between and define relationships within tables in power bi.
Today we dive down to data transformation with power-bi, that's from cleaning, querying down to visualization.
As an analyst I can use power bi to transform data using two processes; ETL (Extract Transform Load)
2.ELT (Extract Load Transform)
Though most analysts ought for process 2, ELT (Extract Load Transform), in turning huge unstructured data into actionable insights for decision making.

1.Extract Load Transform.
The first process is loading the data to power bi.
Mostly being a CSV file of Excel.

As seen here you can Extract data from importing on the sources listed: power-bi sematic, SQL server, excel, csv file...etc.
Here is where we Extract from data sources and load the data to power bi.

2.Transform Data (Power Query/Dax Formulars)
Here now is where all the shenanigans are done on the data.
Is where we clean, remove duplicates, transform columns, create new measures and columns as well as creating connections and relationships within data columns.
Its here where we introduce Dax formulas as we manipulate our data
(Dax-is a formular expression language used in power-bi)

From above you can see an interface of power bi showing where data is transformed.

Dax/Dax Functions
DAX is designed specifically for analytical and business intelligence tasks such as totals,
averages, percentages, rankings, comparisons, and time-based analysis.
Kindly note Dax formulars are not hard, they derived mostly from excel functions concepts.
One of the main functions is the;
1.Aggregation Function
these means summarizing many rows to one value.analysts oftenly use these functions in the analysis of the data.
Thes\y include: (SUM, AVERAGE, MEDIAN,
MIN, MAX, COUNT, VAR, STDEV).
Iterator functions ending in X (SUMX, AVERAGEX, MEDIANX, MINX, MAXX, COUNTX,
VARX, STDEVX) evaluate an expression row by row first, then aggregate.
Mathematical helpers like ABS, POWER, SQRT, ROUND, ROUNDUP, ROUNDDOWN,
and MOD help shape and control numeric results for reporting.

2.LOGICAL FUNCTIONS IN DAX (POWER BI)
Logical functions in DAX are used to make decisions based on conditions. They allow Power BI
to answer “yes or no” questions, classify data into categories, apply business rules, and control
how results are calculated and displayed
Logical functions are commonly used in calculated columns, measures, and KPIs.
This function is "the IF FUNCTION"
IF is used for simple true or false decisions.
Nested IF handles multiple outcomes but can reduce readability.
AND / && require all conditions to be true.
OR / || require at least one condition to be true.
SWITCH is the preferred option for many conditions.
ISBLANK and COALESCE help handle missing data.
Logical functions are essential for classification, KPIs, and business rules.

3.FILTER FUNCTIONS IN DAX (POWER BI)
Filter functions in DAX control which rows of data are included in a calculation. They are the
backbone of meaningful analysis in Power BI because almost every business question depends
on filtering data correctly.
CALCULATE modifies filter context and performs calculations.
FILTER selects rows and returns a table.
Use simple filters in CALCULATE when possible for better performance.
Use FILTER for complex conditions.
ALL and REMOVEFILTERS ignore filters.
ALLEXCEPT keeps specific filters.
KEEPFILTERS preserves existing context.

All above functions play a major role in data transformation.
Some other functions may look complex in writing i.e the "nested IFS" but
after mastering they look simpler like the SUM function.
After all the transformation and calculations KPIs we head direct to visualization where you are able to create dashboard similar to these one below, that's according to the data requirements.

Dax/Dax Functions

2.