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Power BI · Retail_Sales_2025.pbixBuilt by a Batch 2518 learner

Retail sales performance

FY 2025 · sample data

AllDhakaChattogramSylhetKhulna

Revenue৳7.15 Cr▲ 14.2% vs LY

Orders93,650▲ 9.8% vs LY

Avg order value৳763▲ 3.9%

Profit margin18.4%▲ 0.6 pts

Monthly revenue vs target (lakh ৳)

RevenueTarget

Sales channel mix

In store 58%Online 27%App 15%

Revenue by category (lakh ৳)

Top outlets

Outlet	Revenue	YoY
Gulshan 2	৳96L	▲ 12.4%
Dhanmondi 27	৳88L	▲ 15.6%
Agrabad	৳71L	▲ 8.1%
Zindabazar	৳54L	▼ 2.3%
Khulna Sadar	৳47L	▲ 4.9%

Power BIRetail sales performanceBuilt by a Batch 2518 learner

Power BI · HR_Attrition.pbixBuilt by a Batch 2520 learner

HR attrition analysis

FY 2025 · sample data

AllSalesOperationsITFinance

Headcount1,840▲ 6.1% YoY

Attrition rate16.8%▲ 2.3 pts

Avg tenure3.4 yrs

Engagement score3.7 / 5▲ 0.2

Monthly exits

Why people leave

Better pay 38%Career growth 27%Workload 19%Relocation 9%Other 7%

Attrition rate by age band

Overtime vs attrition

Key insight

Staff under 25 who work regular overtime leave at 29%, nearly 3× the rate of others. Pay review and overtime caps in Sales and Operations would cut exits most.

Written by the learner as the dashboard's recommendation.

Power BIHR attrition analysisBuilt by a Batch 2520 learner

R Shiny · household_survey_2025.RBuilt by an R Batch 2603 learner

Household survey analysis

FY 2025 · sample data

AllUrbanRural

Households surveyed2,4008 districts

Avg monthly income৳21,400median ৳17,500

Safe drinking water87%

Mobile money use71%

Monthly household income distribution (% of households)

Main income source

Agriculture 34%Salaried job 26%Business 21%Remittance 12%Other 7%

Development indicators

Model output (R)

lm(income ~ education + hh_size + area)

             Estimate  Std.Err   p
education      2140.3    212.6  <.001
hh_size        -412.8     98.1  <.001
areaUrban     8915.2    644.0  <.001
R² = 0.41   n = 2400

R ShinyHousehold survey analysisBuilt by an R Batch 2603 learner

SQL + Python · customer_retention.ipynbBuilt by a Batch 2415 learner

E-commerce customer retention

FY 2025 · sample data

AllNewReturningVIP

Customers48,200▲ 21% YoY

Repeat purchase rate34%▲ 4 pts

Customer lifetime value৳5,850▲ 11%

At churn risk18%● healthy

Monthly cohort retention (% still buying)

Orders by weekday (%)

Monthly active customers (thousands)

Query behind the cohort chart

WITH first_order AS (
  SELECT customer_id,
    DATE_TRUNC('month', MIN(order_date)) AS cohort
  FROM orders GROUP BY 1)
SELECT f.cohort,
  DATE_PART('month', AGE(o.order_date, f.cohort)) AS m,
  COUNT(DISTINCT o.customer_id) AS active
FROM orders o JOIN first_order f USING (customer_id)
GROUP BY 1, 2;

SQL + PythonE-commerce customer retentionBuilt by a Batch 2415 learner

Dashboards are rebuilt on this site with sample data.

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