This is a dataset about literacy rate of different states of India. It shows male and female literacy rate, as well as average literacy rate of that state. I have used the data from internet to make the dataset.

With the help of html, data read and then data set is created in the form of csv file.

The data shows the year-wise and state or union territory-wise literacy and rural and urban literacy, for male, female, and total literacy, in India according to Census.

Note: 1. Literacy rate is defined as the population of literates in the population aged 7 year and above. 2. The 1991 data (Excluding Jammu & Kashmir)and 2001 data (Excludes figures of Paomata, Mao Maran and Pura sub-divisions of Senapati district of Manipur for 2001) refer to Census of India.

Context

This is the official dataset released by the govt. of India based on the census 2001 and 2011 survey.

Content

The data is of 35 Indian states and union territories. The literacy rate is spread across the major parameters - Overall, Rural and Urban. All the data is percentage of the total population of that state.

Acknowledgements

Derived from the govt. of India's official site.

Inspiration

Understand the literacy rate in India and which states/UT's have the highest growth in terms of increased literacy rates.

Among the states in India, Kerala had the highest literary rate with ** percent in 2011. Chandigarh, Himachal Pradesh and the capital territory of Delhi followed Kerala with above average literacy rates. Notably, all the leading states in the country had more literate males than females at the time of the census.

The dataset contains Year and State wise Literacy Rate

Note: 1. Literacy rates for 1951, 1961 and 1971 Censuses relate to population aged five years and above. The rates for the 1981, 1991, 2001 and 2011 Censuses relate to the population aged seven years and above. The literacy rate for 1951 in case of West Bengal relates to total population including 0-4 age group. Literacy rate for 1951 in respect of Chhattisgarh, Madhya Pradesh and Manipur are based on sample population. 2. India and Manipur figures exclude those of the three sub-divisions viz., Mao Maram, Paomata and Purul of Senapati district of Manipur as census result of 2001 in these three sub-divisions were cancelled due to technical and administrative reasons.

Literacy Rate Content

Literacy rate in India is uneven and as such, different states and union territories of India have differences in their literacy rates. The following table shows the details from 1951 to 2011 census data on total literacy rate in percentage.[2][3] According to Census 2011, Kerala has the highest total literacy rate and female literacy rate whereas Lakshadweep had the highest male literacy rate. Andhra Pradesh has the lowest overall literacy rate. Rajasthan has the lowest male literacy rate, while Bihar has the lowest female literacy rate. Literacy figures are collected by census takers which essentially means literacy (or lack therefore) is self assessed.

**Univercity List CONTENT **

The higher education system in India includes both private and public universities. Public universities are supported by the Government of India and the state governments, while private universities are mostly supported by various bodies and societies. Universities in India are recognised by the University Grants Commission (UGC), which draws its power from the University Grants Commission Act, 1956.[1] In addition, 15 Professional Councils are established, controlling different aspects of accreditation and co-ordination.

The types of universities include

• Central universities, or Union universities, are established by an Act of Parliament and are under the purview of the Department of Higher Education in the Ministry of Education.[3] As of 18 October 2022, the list of central universities published by the UGC includes 54 active central universities

- State universities are run by the state government of each of the states and territories of India and are usually established by a local legislative assembly act. As of 23 August 2022, the UGC lists 455 active state universities

- Deemed university, or "Deemed to be University", is a status of autonomy granted by the Department of Higher Education on the advice of the UGC, under Section 3 of the UGC Act.[8] As of 30 November 2021, the UGC lists 126 institutes which were granted the deemed to be university status.[9] According to this list, the first institute to be granted deemed university status was Indian Institute of Science, which was granted this status on 12 May 1958. In many cases, the same listing by the UGC covers several institutes.

• **Private universities **are approved by the UGC. They can grant degrees but they are not allowed to have off-campus affiliated colleges. As of 23 August 2022, the UGC consolidated list of universities lists 421 private universities

Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2023, the degree of literacy in India was about 77 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

Context

This datasets contains data from RBI which is published annually and this data has different features such as

Column names and legends

2000-01-INC = Income of each state for the year 2001 2011-12-INC = Income of each state for the year 2011

2001 - LIT = Literacy rate of each state for the year 2001 2011- LIT = Literacy rate of each state for the year 2011

2001 - POP = Total population of each state for the year 2001 2011- POP = Total population of each state for the year 2011

2001 -SEX_Ratio = Sex_Ratio of the each state for the year 2001 2011 -SEX_Ratio = Sex_Ratio of the each state for the year 2011

2001 -UNEMP = Unemployment rate of the each state for the year 2001 2011 -UNEMP = Unemployment rate of the each state for the year 2011

2001 -Poverty = Poverty rate of the each state for the year 2001 2011 -Poverty = Poverty rate of the each state for the year 2001

Unemployment Rate - for a month is calculated using the following formula: The monthly estimations for India are calculated as a ratio of the total estimated unemployed persons in India to the total estimated labor force for a month

Poverty rate = A common method used to estimate poverty in India is based on the income or consumption levels and if the income or consumption falls below a given minimum level, then the household is said to be Below the Poverty Line

state's Income measured using state domestic product - is the total value of goods and services produced during any financial year within the geographical boundaries of a state

Literacy rate - Total number of literate persons in a given age group, expressed as a percentage of the total population in that age group. The adult literacy rate measures literacy among persons aged 15 years and above, and the youth literacy rate measures literacy among persons aged 15 to 24 years

Acknowledgements

I wouldn't be here without the help of my friends and people who read this post. I owe you thanks for this research.

Inspiration

here are pretty basic question but I would high appreciate the data scientist community for any deep insight of the data in plots Cheers!!

Objective of the study:

-Is state's income is based on the education of the state -Does literacy rate contribute any changes to poverty rate

Source of the Datasets

datasets link

if this found useful kindly up-vote cheers!!

The statistic displays the main states and union territories in India with the highest number of illiterate people in 2011. In that year, Uttar Pradesh was at the top of the list, with more than ** million illiterate people, followed by the state of Bihar with over ** million people.

his dataset contains demographic information for Indian states from the Census years 1951 to 2011. It includes total population, rural and urban population, literacy rate, and sex ratio for each state/UT across multiple decades.

The dataset can be used for:

Analyzing population trends over time

Studying urbanization and rural migration

Examining literacy growth across states

Understanding sex ratio imbalances historically

Building machine learning models for future population prediction

Columns Included:

State – Name of the State or Union Territory

Year – Census year (1951, 1961, ..., 2011)

Total_Population – Total population in that year

Rural_Population – Population in rural areas

Urban_Population – Population in urban areas

Literacy_Rate – Literacy percentage of the population

Sex_Ratio – Number of females per 1000 males

Literacy Rate: Tamil Nadu data was reported at 80.100 % in 12-01-2011. This records an increase from the previous number of 73.450 % for 12-01-2001. Literacy Rate: Tamil Nadu data is updated decadal, averaging 58.525 % from Dec 1961 (Median) to 12-01-2011, with 6 observations. The data reached an all-time high of 80.100 % in 12-01-2011 and a record low of 36.390 % in 12-01-1961. Literacy Rate: Tamil Nadu data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Education Sector – Table IN.EDA001: Literacy Rate.

Documentation

Description and codebook for subset of harmonized variables:

Section 2

Guide to datasets:

Full Project Name: The Impact of Mother Literacy and Participation Programs on Child Learning in India

Unique ID: 458

PIs: Rukmini Banerji, James Berry, Marc Shotland

Location: Indian states of Bihar and Rajasthan

Sample: Around 9,000 households in 480 villages

Timeline: 2010 to 2012

Target Group: Children Parents Rural population Women and girls

Outcome of Interest: Employment, Student learning ,Women’s/girls’ decision-making, Gender attitudes and norms

Intervention Type: Early childhood development, Tracking and remedial education, Empowerment training

Associated publications: https://www.aeaweb.org/articles?id=10.1257/app.20150390

More information: https://www.povertyactionlab.org/evaluation/impact-mother-literacy-and-participation-programs-child-learning-india

Dataverse: Banerji, Rukmini; Berry, James; Shotland, Marc, 2017, “The Impact of Maternal Literacy and Participation Programs: Evidence from a Randomized Evaluation in India”, https://doi.org/10.7910/DVN/19PPE7, Harvard Dataverse, V1

Section 3

Survey instrument:

Testing tools:

Section 4

Survey instrument:

Testing tools:

Section 5

No associated survey instrument

Section 6

This dataset was created on 2021-10-06 20:35:41.921 by merging multiple datasets together. The source datasets for this version were:

Maternal Literacy in India Baseline: Modified from ml_merged : contains data with variables only from baseline surveys

Maternal Literacy in India Endline: Modified from ml_merged : contains data with variables only from endline surveys

Maternal Literacy in India Raw Administrative Statistics: ml_admin_stats_raw: Contains administrative statistics from the 2011 census and aser surveys used in online Appendix Table 1 in the paper; this is merged with some of the survey data to create ml_admin_stats

India Census Dataset

This dataset provides comprehensive census data at the district level for India. It includes detailed demographic, religious, educational, and workforce-related attributes, making it a rich resource for socio-economic analysis.

1. Columns and Descriptions

A. Basic Information

District_code: A unique numeric code for each district. State_name: Name of the state to which the district belongs. District_name: Name of the district.

B. Population Data

Population: Total population of the district. Male: Total male population in the district. Female: Total female population in the district.

C. Literacy Data

Literate: Total number of literate individuals in the district.

D. Workforce Data

Workers: Total number of workers in the district. Male_Workers: Total number of male workers in the district. Female_Workers: Total number of female workers in the district. Cultivator_Workers: Number of workers engaged as cultivators. Agricultural_Workers: Number of workers engaged in agricultural labor. Household_Workers: Number of workers engaged in household industries.

E. Religion Data

Hindus: Total number of Hindus in the district. Muslims: Total number of Muslims in the district. Christians: Total number of Christians in the district. Sikhs: Total number of Sikhs in the district. Buddhists: Total number of Buddhists in the district. Jains: Total number of Jains in the district.

F. Education Data

Secondary_Education: Number of individuals with secondary education. Higher_Education: Number of individuals with higher education qualifications. Graduate_Education: Number of individuals with graduate-level education.

G. Age Group Data

Age_Group_0_29: Population in the age group 0–29 years. Age_Group_30_49: Population in the age group 30–49 years. Age_Group_50: Population aged 50 years and above.

2. Key Highlights

Number of Districts: 640 Number of Columns: 25 Non-null Values: All columns are complete with no missing data. Detailed breakdown of population by gender, age group, literacy levels, and workforce distribution. Religious composition and education statistics are also included for each district.

3. Potential Use Cases

Data Analysis and Visualization:

Explore patterns in population distribution, literacy rates, workforce composition, and religious demographics. Machine Learning Applications:

Build predictive models to classify districts or forecast demographic trends. Social Research:

Investigate correlations between education levels, workforce participation, and religion. Policy Planning:

Help policymakers target specific demographics or regions for intervention. Educational Insights:

Analyze the impact of education levels on workforce participation or literacy.

4. Dataset Overview

Total Rows: 640 Total Columns: 25 This dataset provides a unique opportunity to understand India's socio-economic and demographic composition at a granular district level.

--- ### Context When India got independence from British in 1947 the literacy rate was 12.2% and as per the recent census 2011 it is 74.0%. Although it looks an accomplishment, still many people are there without access to education. It would be interesting to know the current status of the Indian education system. ### Content This dataset contains district and state wise Indian primary and secondary school education data for 2015-16. Granularity: Annual List of files: 1. 2015_16_Districtwise.csv ( 680 observations and 819 variables ) 1. 2015_16_Statewise_Elementary.csv ( 36 observations and 816 variables ) 1. 2015_16_Statewise_Secondary.csv ( 36 observations and 630 variables ) ### Acknowledgements Ministry of Human Resource Development (DISE) has shared the dataset [here](http://udise.in/src.htm) and also published some [reports](http://udise.in/AR.htm). Source of Banner [image](https://unsplash.com/photos/j9jZSqfH5YI). ### Inspiration This dataset provides the complete information about primary and secondary education. There are many inferences can be made from this dataset. There are few things I would like to understand from this dataset. 1. Drop out ratio in primary and secondary education. (Govt. has made law that every child under age 14 should get free compulsary education.) 2. Various factors affecting examination results of the students. 3. What are all the factors that makes the difference (in literacy rate) between Kerala and Bihar? 4. What could be done to improve the female literacy rate and literacy rate in rural area? ---

Changes in literacy rates (%) in Madhya Pradesh and Uttar Pradesh.

Literacy Rate: Kerala data was reported at 94.000 % in 12-01-2011. This records an increase from the previous number of 90.860 % for 12-01-2001. Literacy Rate: Kerala data is updated decadal, averaging 78.850 % from Dec 1951 (Median) to 12-01-2011, with 7 observations. The data reached an all-time high of 94.000 % in 12-01-2011 and a record low of 47.180 % in 12-01-1951. Literacy Rate: Kerala data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Education Sector – Table IN.EDA001: Literacy Rate.

As per the Indian census data of 2011, about 83 percent of the male population in the southern state of Karnataka knew how to read or write. During the same year, the female literacy rate was at 68 percent in the state.

Dataset Description:

This dataset contains two distinct tables that offer valuable insights into the population trends and characteristics of Indian districts. Dataset 1 includes demographic data, such as population growth rate, sex ratio, and literacy rate, while Dataset 2 provides information about the geographical aspects, including district area and population count. Together, these datasets empower researchers and data enthusiasts to explore and analyze India's demographic and geographical dynamics, contributing to a deeper understanding of the nation's diverse regions and populations.

Dataset 1: Demographic Insights - District: The name of the district within India. - State: The state to which the district belongs. - Growth: The population growth rate of the district. - Sex_Ratio: The ratio of males to females in the population. - Literacy: The literacy rate of the district's population.

Dataset 2: Geographical Information - District: The name of the district within India. - State: The state to which the district belongs. - Area_km2: The geographical area of the district in square kilometers. - Population: The population count of the district.

These datasets provide a comprehensive view of both the demographic and geographical aspects of Indian districts, enabling in-depth analysis of population trends and regional characteristics.

This dataset tracks annual american indian student percentage from 2001 to 2010 for Adult & Family Literacy High School vs. Colorado and Harrison School District No. 2 In The County Of El Paso And State Of Colorado

Education System In India (District Level)

Analyzing Educational Performance at the District Level

By Inder Sethi [source]

About this dataset

This comprehensive District Information System for Education (DISE) dataset collects district-level educational statistics in India and provides the most up-to-date data on the nation's schools. The project tracks and compiles data on primary and upper primary school students, teachers, institutions, infrastructures and more from all districts in India. It has drastically reduced the time lag between data collection to analysis - from seven to eight years down to only a few months at both district and state levels. DISE is fully supported by the Ministry of Human Resource Development (MHRD) as well as UNICEF so precise regional insights are available regarding Indian education standards. With this institutionalized flow of raw data being collected, verified at Block Education Offices/Coordinators then computerized at a District level before eventually being aggregated into State level analysis – it’s easier than ever before to understand where educational improvements need to be made. From tracking key performance indicators amongst students across all ages right through to measuring access teacher resources - this DISE dataset serves as an invaluable resource towards unlocking potential within the Indian learning system!

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How to use the dataset

Guide: How to Use the Indian District Level School Data 2015-16

• Familiarize yourself with the features of this data set. The dataset consists of five columns which provides an overview at district level educational statistics in India for the year 2015-16. Each row contains individual district-level data with corresponding educational information and statistics like Total Number of Schools, Number of Girls' Schools, Enrolment and more for each district in India during that year.

• Understand what kind of analysis can be done using this dataset once imported into a statistical software program or spreadsheet program such as Microsoft Excel or Google Sheets. You can use this dataset to analyze many different aspects related to education in India at a district level; including total number of schools, number and percent girls enrolled, teacher qualifications and more across districts throughout all states in India during the year 2015-16 period covered by this data set.

• Pull up a visual representation of your data within a statistical program like SPSS or perhaps one online such as Tableau Public, depending on your preference and needs for analysis purposes - either way it is necessary to have these setup beforehand before attempting to import any given subset into them; click upload file option within them (or any other appropriate action), select all files in your local machine directory where you saved our downloaded csv file “report card” from kaggle above – then just wait until it’s completely uploaded after selecting open/import/apply/etc…and if no errors about encoding appear then begin your desired data mining experience (visualization & analytical techniques).

• Once inside your preferred visualization environment, try out different methods for analyzing individual rows which correspond directly onto specific districts located inside this geographic territory that are meant by our target sheet observations mentioned prior – refer back often if lost & take time understanding what any given county contributes when computer processing their respective responses accordingly without overlooking any particular variables taken into account unlike secondary “missing values” under consideration also..

• Then define relationships between similar items according figures gathered - notice patterns found among these locations while focusing attention isolation instead – graphic qualities captured midst these demographics we choose visualize key representing intent anyways… therefor aim transform knowledge through effective strategy meant enable more meaningful representation ideas presented starting place develops further details follow courtesy

Research Ideas

• Analyzing literacy rate and measure the educational advancement of different districts in India.
• Tracking the progress of various Governmental programs like Sarva Shiksha Abhiyan that focus on improving access to education for children across districts.
• Predicting trends in the quality of school resources, educational infrastructure and student performance to guide district-level decision making processes for improved education outcomes

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