Power BI Project on Indian School Education Statistics 

Problem Statement (Download Dataset)

Educational development is one of the critical indicators of a nation’s growth. Despite significant policy interventions in India, disparities persist across states, gender, and different educational levels. This dataset provides information on Gross Enrolment Ratios (GER) of boys, girls, and total students at primary, upper primary, secondary, and higher secondary levels across Indian States and Union Territories for multiple years.

The challenge is to analyze trends, identify gaps in enrolment across gender and states, and evaluate whether progress has been uniform. This study will help policymakers, educators, and researchers understand:

• Gender disparities in school education.

• State-wise differences in educational development.

• The progression from primary to higher secondary levels.

• Year-wise changes and the effectiveness of education policies.

By leveraging this dataset, data-driven insights can be generated to improve policy decisions, reduce dropouts, and ensure equitable access to education.

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Dataset Description

• Source: Indian School Education Statistics (Government of India).

• Scope: Covers Gross Enrolment Ratios (GER) across different school levels.

• Granularity: Yearly data for each State/UT.

Features

1. State_UT – Name of the State or Union Territory.

2. Year – Academic year of data (e.g., 2012-13, 2013-14, etc.).

3. Primary_Boys – GER of boys at the primary level.

4. Primary_Girls – GER of girls at the primary level.

5. Primary_Total – Combined GER for boys and girls at the primary level.

6. UpperPrimary_Boys – GER of boys at the upper primary level.

7. UpperPrimary_Girls – GER of girls at the upper primary level.

8. UpperPrimary_Total – Combined GER at the upper primary level.

9. Secondary_Boys – GER of boys at the secondary level.

10. Secondary_Girls – GER of girls at the secondary level.

11. Secondary_Total – Combined GER at the secondary level.

12. HrSecondary_Boys – GER of boys at the higher secondary level.

13. HrSecondary_Girls – GER of girls at the higher secondary level.

14. HrSecondary_Total – Combined GER at the higher secondary level.

Detail Steps

1. Upload the Data

Steps:

1. Open Power BI Desktop.

2. Go to Home → Get Data → Text/CSV (if your file is .csv) or Excel (if .xlsx).

3. Browse and select your Indian School Education Statistics dataset.

4. Click Load (or Transform Data if you want to go directly to cleaning in Power Query)

2. Transform the Data (Power Query)

2.1 Remove extra spaces & fix column names

• In Power Query Editor, rename columns to more readable names:

  • Example: Primary_Boys → Primary Boys,

  • Secondary _Boys → Secondary Boys (remove extra space),

  • Uppe_r_Primary → Upper Primary Boys.

• Use Transform → Format → Trim / Clean to remove hidden spaces.

2.2 Handle Missing Values (NR, NA, blanks)

• In Power Query, select the columns with enrolment ratios.

• Use Transform → Replace Values:

  • Replace "NR", "NA", "Uppe_r_Primary" with null.

• Power BI will now recognize them as blank values. 

2.3 Change Data Types

• Ensure numeric columns (Primary Boys, Primary Girls, Secondary Total, etc.) are set to Decimal Number.

• Ensure Year is Text (if you want to keep 2012-13 format) or split into start year as Whole Number.

• Use Split Column → By Delimiter → “-” to create Year Start = 2012, Year End = 2013.  

2.4 Clean State Names

• Select the State_UT column → Apply Trim to remove double spaces.

• Replace values if needed (Andhra Pradesh → Andhra Pradesh).  

2.5 Create New Columns (Calculated Columns in DAX)

In Power BI Data View, create calculated columns:

• Gender Gap (Primary)  

DAX: Primary_Gap = [Primary Boys] - [Primary Girls]

Progression Ratio (Primary → Secondary)  

DAX : Progress_Primary_Secondary = DIVIDE([Secondary Total],[Primary Total])  

Gender Parity Index (Primary) 

DAX: Primary_GPI = DIVIDE([Primary Girls],[Primary Boys])