Getting Started with Power BI & Your First HR Report

What is Power BI?

Power BI is Microsoft's business-intelligence platform: it connects to data, cleans and models it, and turns it into interactive reports and dashboards that people can share. In 2026 it is part of Microsoft Fabric, Microsoft's all-in-one analytics platform, and it is one of the most requested tools in analyst job posts, alongside Excel, SQL and Tableau.

Component	What it is	Who uses it
Power BI Desktop	Free Windows application to build data models and reports (.pbix)	Analysts, developers
Power BI Service (app.powerbi.com)	Cloud site to publish, share, schedule refresh, create dashboards and apps	Analysts and report consumers
Power BI Mobile	iOS/Android apps to view reports and get alerts	Managers on the go
Power BI Report Builder	Paginated, print-ready reports (invoices, statements)	Report developers
On-premises data gateway	Secure bridge so the cloud can refresh data from your own servers	Admins
Power BI Report Server	Self-hosted option for organisations that cannot use the cloud	IT
Copilot	AI assistant that writes DAX, builds report pages and summarises insights (needs a Fabric/Premium capacity)	Everyone

TipReport vs dashboard

In Power BI a report is one or more pages of interactive visuals built on a data model. A dashboard (Service only) is a single page of pinned tiles from different reports. Many teams use reports only, or share them as an app.

Licences in brief

Licence	Use
Power BI Desktop	Free: build and save locally
Free (Service)	Personal use in "My workspace"; cannot share with others
Pro	Publish and share with other Pro users (about US$14 per user per month)
Premium Per User / Fabric capacity	Larger models, AI features, share with free users

(Prices change; check Microsoft's current pricing page.)

Installing Power BI Desktop

Step by stepInstall

1. Easiest: open the Microsoft Store, search "Power BI Desktop" and click Install (it updates itself every month).
2. Or download it from the Microsoft Power BI website → Download → Power BI Desktop.
3. Requirements: Windows 10/11 (64-bit), 4 GB+ RAM (8 GB+ recommended). There is no Mac version: Mac users can use the Service in a browser, a Windows virtual machine, or Windows 365.
4. Start the app and sign in with a work or school account (optional for building locally).

The three views

View	Icon	Use it to
Report	bar chart	Design the report pages
Table (Data)	grid	Inspect the data and create calculated columns and measures
Model	diagram	See and edit relationships between tables
DAX query view	{ }	Run DAX queries and test measures

ETL and the analytics process

ETL = Extract (get data from sources), Transform (clean and reshape it), Load (store it in the model). In Power BI, Extract and Transform are done in Power Query, and Load puts the result into the data model. (You met Power Query in Excel Lesson 12: it is the same engine.)

Always start with the question: "Why are people leaving?" decides which data and visuals you need. The worst Power BI reports are tours of every chart type with no question.

Your first report: HR attrition

Case studyThe brief

HR wants a one-page report on the company's 1,500 employees: how many there are, how many left (attrition), where attrition is highest, and whether overtime, job satisfaction or income play a role. Use hr_employees.csv.

The data (first rows):

PythonCopy

import pandas as pd
hr = pd.read_csv("hr_employees.csv", parse_dates=["hire_date"])
print(hr.shape)
hr.head(5)

Output

(1500, 15)

	emp_id	gender	age	department	job_title	education	hire_date	years_at_company	monthly_income	job_satisfaction	overtime	distance_from_home_km	performance_rating	city	attrition
0	E00001	Male	39	Operations	Operations Manager	Bachelor's	2025-05-26	0	74500	2	Yes	28	3	Bengaluru	No
1	E00002	Female	45	IT	Tester	Bachelor's	2020-10-17	5	61417	1	No	25	3	Mumbai	No
2	E00003	Male	41	IT	Tester	Bachelor's	2019-05-06	6	59167	4	Yes	18	4	Bengaluru	No
3	E00004	Male	21	R&D	Data Scientist	Bachelor's	2025-08-23	0	51500	4	No	28	1	Mumbai	Yes
4	E00005	Male	41	Sales	Sales Executive	Master's	2015-08-24	10	128333	1	No	3	2	Pune	No

5 rows × 15 columns

Step 1: get the data

In the Data pane you now see the table hr_employees with a Σ next to numeric fields (they can be summed) and no symbol next to text fields.

Step 2: KPI cards

Drag fields onto the canvas, then choose the Card visual:

• Employees = Count of emp_id
• Attrition (leavers): use a measure (Lesson 3 explains measures): Leavers = CALCULATE ( COUNTROWS ( hr_employees ), hr_employees[attrition] = "Yes" )
• Attrition % = DIVIDE ( [Leavers], COUNTROWS ( hr_employees ) ), formatted as a percentage
• Average monthly income = Average of monthly_income

n = len(hr); leavers = (hr.attrition == "Yes").sum()
print(f"Employees      : {n:,}")
print(f"Leavers        : {leavers:,}")
print(f"Attrition %    : {leavers / n:.1%}")
print(f"Avg income (₹) : {hr.monthly_income.mean():,.0f}")

Employees      : 1,500
Leavers        : 389
Attrition %    : 25.9%
Avg income (₹) : 82,611

Step 3: bar, column and donut charts

• Clustered bar chart: Y-axis department, X-axis = Attrition % → ranks departments.
• Clustered column chart: X-axis job_satisfaction, Y-axis Attrition %.
• Donut chart: Legend attrition, Values = count of emp_id.
• Matrix: Rows department, Columns overtime, Values Attrition % (turns into a heat map with conditional formatting, Lesson 5).
• Slicers: gender, education, city.

Step 4: a calculated column with DAX

We want to see joining trends by year, but the table has only hire_date. Add a calculated column: Table view (or Modeling) → New column:

Hire Year = YEAR ( hr_employees[hire_date] )

A calculated column is evaluated row by row when the data loads and stored in the model, just like a new column in Excel. Now a line/column chart of headcount by hire year works:

hr["hire_year"] = hr.hire_date.dt.year
by_year = hr.groupby("hire_year").agg(hired=("emp_id", "count"), left=("left", "sum"))
by_year["attrition_%"] = (by_year.left / by_year.hired * 100).round(1)
by_year.tail(8)