Here’s a complete HR dataset for you in CSV format (with 300 rows), along with a list of business questions/problems you can solve using Power BI and the possible types of answers/insights you can generate.

Dataset Link: https://github.com/slidescope/data/blob/master/hr_employee_data_with_nationality.csv

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✅ Dataset Description

Filename: hr_employee_data.csv
Rows: 300
Columns (Fields):

• EmployeeID: Unique ID
• Name: Full name (mock names)
• Department: Categorical (HR, Sales, IT, Finance, Marketing, R&D)
• Gender: Male, Female, Other
• Age: Integer (20–60)
• Education: Bachelor’s, Master’s, PhD, Diploma
• JobRole: Job roles like Analyst, Manager, Executive, Developer, etc.
• MonthlyIncome: Numerical (20,000–120,000)
• YearsAtCompany: Integer (0–40)
• Attrition: Yes, No
• PerformanceRating: 1–5
• OverTime: Yes, No
• MaritalStatus: Single, Married, Divorced
• WorkLifeBalance: 1–4
• TrainingTimesLastYear: Integer (0–10)

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📊 Business Questions & Power BI Use Cases

Business Question	Visualization Type	Insights / Answers
1. What is the overall attrition rate?	Card, KPI	E.g., 18% attrition
2. Which departments have the highest attrition?	Bar Chart	E.g., Sales = 30%, IT = 12%
3. Is there a trend between years at company and attrition?	Line or Scatter Chart	E.g., high attrition in employees < 3 years
4. How does income vary by department?	Box Plot / Bar Chart	E.g., Finance highest average salary
5. Do employees doing overtime show lower work-life balance?	Matrix / Stacked Bar	Overtime employees report WLB rating 1–2 mostly
6. What’s the gender distribution by department?	Stacked Column / Donut	Identify imbalance
7. Does performance rating correlate with attrition?	Clustered Bar	E.g., Rating 2 = high attrition
8. Average training hours per department?	Bar Chart	E.g., R&D = 8 hrs, HR = 3 hrs
9. What is the age group with highest attrition?	Histogram or Slicer Filter	E.g., Age 25–35 most at risk
10. How does marital status affect attrition or income?	Pie/Bar Charts	E.g., Single employees show higher attrition

Here are Power BI questions specifically suited for DAX, along with the appropriate DAX formulas, and explanations on when to use Calculated Measures, Columns, or Tables.

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✅ When to Use What in DAX

Type	Purpose
Calculated Column	Row-by-row computation; added to the data model table directly.
Measure	Aggregation or computation done during analysis; not stored per row.
Calculated Table	Creates a new table based on existing data; used for summaries, filtering, relationships, etc.

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📌 DAX Questions, Formulas, and When to Use

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1. What is the attrition rate?

Question Type: KPI
Use: Measure

Attrition Rate = 
DIVIDE(
    CALCULATE(COUNTROWS(EmployeeData), EmployeeData[Attrition] = "Yes"),
    COUNTROWS(EmployeeData)
)

✅ Use Measure here because it’s an aggregated value (depends on filters, slicers, etc.)

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2. Add a column to group employees into Age Bands (e.g., 20-30, 31-40)

Question Type: Segmentation
Use: Calculated Column

Age Band = 
SWITCH(TRUE(),
    EmployeeData[Age] <= 30, "20-30",
    EmployeeData[Age] <= 40, "31-40",
    EmployeeData[Age] <= 50, "41-50",
    "51+"
)

✅ Use Column because you want to create a static attribute per row (for use in slicers/grouping).

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3. What is the average income per department?

Question Type: Bar chart (by department)
Use: Measure

Avg Income = AVERAGE(EmployeeData[MonthlyIncome])

✅ Use Measure for aggregation—it reacts to the department filter.

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4. Create a new table that lists only employees with attrition = Yes

Question Type: Filtered table
Use: Calculated Table

Attrited Employees = 
FILTER(EmployeeData, EmployeeData[Attrition] = "Yes")

✅ Use Table for generating filtered subsets used in reports, relationships, etc.

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5. Add a column to show Tenure Category (New, Mid, Experienced)

Use: Calculated Column

Tenure Category = 
SWITCH(TRUE(),
    EmployeeData[YearsAtCompany] <= 2, "New",
    EmployeeData[YearsAtCompany] <= 5, "Mid",
    "Experienced"
)

✅ Use Column for row-level classification.

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6. Calculate total overtime employees

Use: Measure

Total Overtime = 
CALCULATE(
    COUNTROWS(EmployeeData),
    EmployeeData[OverTime] = "Yes"
)

✅ Use Measure—aggregates and updates with filters.

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7. What % of employees with poor Work-Life Balance (1 or 2) also do overtime?

Use: Measure

Poor WLB Overtime % = 
DIVIDE(
    CALCULATE(COUNTROWS(EmployeeData), 
              EmployeeData[WorkLifeBalance] <= 2,
              EmployeeData[OverTime] = "Yes"),
    CALCULATE(COUNTROWS(EmployeeData), EmployeeData[WorkLifeBalance] <= 2)
)

✅ Measure, because it’s a filtered ratio calculation.

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8. Rank employees by income within their department

Use: Calculated Column (for display), or Measure (for dynamic ranking)

As Column:

Income Rank = 
RANKX(
    FILTER(EmployeeData, EmployeeData[Department] = EARLIER(EmployeeData[Department])),
    EmployeeData[MonthlyIncome],
    ,
    DESC,
    Dense
)

✅ Use Column if you want a static rank shown in the table.

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9. Count employees per age band with high performance (rating ≥ 4)

Use: Measure

High Performers by Age Band = 
CALCULATE(
    COUNTROWS(EmployeeData),
    EmployeeData[PerformanceRating] >= 4
)

✅ Use Measure so it updates dynamically with the age band filter.

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10. Create a summary table: Department vs Avg. Income & Avg. Tenure

Use: Calculated Table

Dept Summary = 
SUMMARIZE(
    EmployeeData,
    EmployeeData[Department],
    "Avg Income", AVERAGE(EmployeeData[MonthlyIncome]),
    "Avg Tenure", AVERAGE(EmployeeData[YearsAtCompany])
)

✅ Calculated Table for static summary reporting or advanced relationships.

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