This map depicts US Census data from the 1910 decennial census for total population and race

In 2018, there were 681 state and local law enforcement training academies that provided basic training instruction to 59,511 recruits. As part of the 2018 Census of Law Enforcement Training Academies (CLETA), respondents provided general information about the academies' facilities, resources, programs, and staff. The core curricula subject areas and hours dedicated to each topic, as well as training offered in some special topics, were also included. The collection included information about recruit demographics, completion, and reasons for non-completion of basic training. BJS administered previous versions of the CLETA in 2002, 2006, and 2013.

This data is comprised of U.S. Census tracts for the year 2019 with data from the American Community Survey, CDC social vulnerability index, CDC Places EPA toxic release inventory sites, PM2.5 annual average from the Atmospheric Composition Analysis Group (https://sites.wustl.edu/acag/). This dataset was created as part of the CAFE Introduction to QGIS 101!!! Session on 6/27/2024 and is for training purposes only.

From 2011 to 2013, a total of 664 state and local law enforcement academies provided basic training to entry-level officer recruits in the United States. During this period, more than 135,000 recruits (45,000 per year) entered a basic training program, and 86 percent completed the program successfully. This completion rate was the same as was observed for the 57,000 recruits who entered training programs in 2005. This data collection describes basic training programs for new recruits based on their content, instructors, and teaching methods. It also describes the recruits' demographics, completion rates, and reasons for failure. The data describing recruits cover those entering basic training programs from 2011 to 2013. The data describing academies are based on 2013, the latest year referenced in the survey. Like prior BJS studies conducted in 2002 and 2006, the 2013 CLETA collected data from all state and local academies that provided basic law enforcement training. Academies that provided only in-service, corrections and detention, or other specialized training were excluded. Federal training academies were also excluded. Any on-the-job training received by recruits subsequent to their academy training is not covered.

This map depicts US Census data from the 1960 decennial census for total population and race

An introductory workshop on using GeoSearch and CHASS. GeoSearch is used to retrieve standard geographic classification numbers, which can then be used to find data about specific geographical locations in CHASS.

These data are from the 20-, 15-, and 5-percent samples of the 1970 United States Census of Population and Housing, and contain population characteristics such as education, occupation, income, citizenship, and vocational training. Separate tallies are shown for whites, Blacks, Hispanic Americans (referred to as Spanish Americans in the 1970 Census), and the total population. The unit of observation for these data files is the census tract. There is one file for each of 48 states and the District of Columbia. The Census Bureau did not issue data for Vermont and Wyoming because these two states were untracted in 1970.

This dataset contains measures of the number and per capita density of education and training services per United States census tract from 2003 through 2017. This includes traditional education establishments such as elementary schools, secondary schools, and colleges, as well as businesses offering specialized training such as art classes, driving instruction, computer training, and standardized test preparation.

The US Census Bureau provides a large collection of data files, some of which are encoded separately or do not have an obvious means to integrate. Suppose that the files are located and need to be integrated to make some data-driven decisions using Census population estimates. The resultant files may be very useful to explore, but the user wants to get into visual representation and start considering things spatially and temporally. In this resource, the Jupyter notebook walks through a set of operations created to integrate Census population estimates with the known ESRI shapefile for the equivalent county-scales.

Manually annotated 3-digit occupational codes from the Norwegian full count 1950 population census.

Census Geography - hands-on workshop. Visit https://dataone.org/datasets/sha256%3A6f29f8489651ade75f1db5fcdb62eae1fca243271b106ec58cddb50f0f699ae9 for complete metadata about this dataset.

Recruitment data about initial teacher training in England

Manually annotated 3-digit occupation codes from the Norwegian full count 1950 population census.

The 2002 Census of Law Enforcement Training Academies (CLETA02) was the first effort by the Bureau of Justice Statistics (BJS) to collect information from law enforcement training academies across the United States. The CLETA02 included all currently operating academies that provided basic law enforcement training. Academies that provided only in-service training, corrections/detention training, or other special types of training were excluded. Data were collected on personnel, expenditures, facilities, equipment, trainees, training curricula, and a variety of special topic areas. As of year-end 2002, a total of 626 law enforcement academies operating in the United States offered basic law enforcement training to individuals recruited or seeking to become law enforcement officers.

US Adult Census data relating income to social factors such as Age, Education, race etc.

The Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Each row is labelled as either having a salary greater than ">50K" or "<=50K".

This Data set is split into two CSV files, named adult-training.txt and adult-test.txt.

The goal here is to train a binary classifier on the training dataset to predict the column income_bracket which has two possible values ">50K" and "<=50K" and evaluate the accuracy of the classifier with the test dataset.

Note that the dataset is made up of categorical and continuous features. It also contains missing values The categorical columns are: workclass, education, marital_status, occupation, relationship, race, gender, native_country

The continuous columns are: age, education_num, capital_gain, capital_loss, hours_per_week

This Dataset was obtained from the UCI repository, it can be found on

https://archive.ics.uci.edu/ml/datasets/census+income, http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/

USAGE This dataset is well suited to developing and testing wide linear classifiers, deep neutral network classifiers and a combination of both. For more info on Combined Deep and Wide Model classifiers, refer to the Research Paper by Google https://arxiv.org/abs/1606.07792

Refer to this kernel for sample usage : https://www.kaggle.com/johnolafenwa/wage-prediction

Complete Tutorial is available from http://johnolafenwa.blogspot.com.ng/2017/07/machine-learning-tutorial-1-wage.html?m=1

Youth not in education, employment or training by visible minority and selected characteristics (age group, gender, first official language spoken, immigrant and generation status), for the population aged 15 to 29 years old in private households in Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts.

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of the U.S. Virgin Islands, data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on the U.S. Virgin Island's data products, see the 2020 Island Areas Censuses Technical Documentation..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability or an insufficient number of cases.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, U.S. Virgin Islands.

Analysis of ‘US Adult Income’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnolafenwa/us-census-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

US Adult Census data relating income to social factors such as Age, Education, race etc.

The Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Each row is labelled as either having a salary greater than ">50K" or "<=50K".

This Data set is split into two CSV files, named adult-training.txt and adult-test.txt.

The goal here is to train a binary classifier on the training dataset to predict the column income_bracket which has two possible values ">50K" and "<=50K" and evaluate the accuracy of the classifier with the test dataset.

Note that the dataset is made up of categorical and continuous features. It also contains missing values The categorical columns are: workclass, education, marital_status, occupation, relationship, race, gender, native_country

The continuous columns are: age, education_num, capital_gain, capital_loss, hours_per_week

This Dataset was obtained from the UCI repository, it can be found on

https://archive.ics.uci.edu/ml/datasets/census+income, http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/

USAGE This dataset is well suited to developing and testing wide linear classifiers, deep neutral network classifiers and a combination of both. For more info on Combined Deep and Wide Model classifiers, refer to the Research Paper by Google https://arxiv.org/abs/1606.07792

Refer to this kernel for sample usage : https://www.kaggle.com/johnolafenwa/wage-prediction

Complete Tutorial is available from http://johnolafenwa.blogspot.com.ng/2017/07/machine-learning-tutorial-1-wage.html?m=1

--- Original source retains full ownership of the source dataset ---