08Aristodemus24 / chronic-disease-analyses

IN PRE-PRODUCTION

Usage:

1. python=3.11.4

This will be my first project in the path to Data Analytics. That will probably use PowerBI, SQL, React, and Django to deploy insights on some yet obscure healthcare data I have to still do research about

Initial notions about hte project

1. will probably utilize some sort of google forms survey instead of scraping since web scraping is only for web data not publicly available and used if tabular data that exists online is not sufficient
2. survey will ask about how chronic illnesses, varicocele, skin disease can affect mental and physical well-being
3. my questions are what can be teh possible variables/features of this data that will be molded into a large data set
4. will probably use react or django to render the dashboard I will be building in PowerBI

Relevant articles and links:

1. https://stackoverflow.com/questions/696506/sql-datatype-how-to-store-a-year
2. https://stackoverflow.com/questions/47357855/sql-add-only-a-year-value-in-a-date-column
3. https://medium.com/analytics-vidhya/analysis-of-time-series-data-dad4afa56358

Problems to solve:

1. I can't save year as 4 byte int for 200000+ rows since that would be a waste of space

Initial insights of GPT-3.5:

Based on the sample rows and column names you provided, it appears that the dataset contains time series health care data regarding U.S. chronic disease indicators (CDI) across the nation from 2001-2014. Below is a description of each column:

YearStart: The starting year of the data collection period (e.g., 2013 in the first row). YearEnd: The ending year of the data collection period (e.g., 2013 in the first row). LocationAbbr: Abbreviation code for the geographic location (e.g., "CA" for California in the first row). LocationDesc: Full name of the geographic location (e.g., "California" in the first row). DataSource: The source of the data (e.g., "YRBSS" - Youth Risk Behavior Surveillance System in the first row). Topic: The topic/category of the chronic disease indicator (e.g., "Alcohol" in the first row). Question: The specific question related to the chronic disease indicator (e.g., "Alcohol use among youth" in the first row). Response: The response to the question (e.g., blank in the provided sample). DataValueUnit: The unit of measurement for the data values (e.g., "%" for percentage in the first row). DataValueTypeID: ID representing the type of data value (e.g., "CrdPrev" for Crude Prevalence in the first row). DataValueType: The type of data value (e.g., "Crude Prevalence" in the first row). DataValue: The actual data value for the indicator (e.g., "-" or "No data available" in the provided sample). DataValueAlt: Alternative data value (e.g., blank in the provided sample). DataValueFootnoteSymbol: Symbol representing the footnote for the data value (e.g., "-" in the provided sample). DatavalueFootnote: Footnote for the data value (e.g., "No data available" in the provided sample). LowConfidenceLimit: The lower limit of confidence interval for the data value (e.g., blank in the provided sample). HighConfidenceLimit: The higher limit of confidence interval for the data value (e.g., blank in the provided sample). StratificationCategory1: The first category for stratification (e.g., "Overall" in the first row). Stratification1: The stratification value for category 1 (e.g., "Overall" in the first row). StratificationCategory2: The second category for stratification (e.g., blank in the provided sample). Stratification2: The stratification value for category 2 (e.g., blank in the provided sample). StratificationCategory3: The third category for stratification (e.g., blank in the provided sample). Stratification3: The stratification value for category 3 (e.g., blank in the provided sample). GeoLocation: Geographic location coordinates (latitude and longitude) for the data point (e.g., "(37.63864012300047, -120.99999953799971)" for California in the first row). TopicID: ID representing the topic/category of the chronic disease indicator (e.g., "ALC" for Alcohol in the first row). QuestionID: ID representing the specific question related to the chronic disease indicator (e.g., "ALC1_1" for Alcohol use among youth in the first row). ResponseID: ID representing the response to the question (e.g., blank in the provided sample). LocationID: ID representing the geographic location (e.g., "6" for California in the first row). StratificationCategoryID1: ID representing the first category for stratification (e.g., "OVERALL" in the first row). StratificationID1: ID representing the stratification value for category 1 (e.g., "OVR" for Overall in the first row). StratificationCategoryID2: ID representing the second category for stratification (e.g., blank in the provided sample). StratificationID2: ID representing the stratification value for category 2 (e.g., blank in the provided sample). StratificationCategoryID3: ID representing the third category for stratification (e.g., blank in the provided sample). StratificationID3: ID representing the stratification value for category 3 (e.g., blank in the provided sample). Please note that the data in the sample rows provided contains missing values or placeholders (e.g., "-") in some columns, and there might be more columns in the actual dataset.

User You are also a healthcare expert. So in the context of health care what could be descriptions of these columns still from a data science perspective but with domain knowledge like that of a health care expert

Of course! Let's describe the columns in the context of healthcare data from a data science perspective with domain knowledge:

YearStart: The starting year of the data collection period. In healthcare data, this column represents the year when the specific health indicators' measurements or observations begin.

YearEnd: The ending year of the data collection period. In healthcare, this column indicates the year when the data collection for the particular health indicators concludes.

LocationAbbr: Abbreviation code for the geographic location. In healthcare data, this column provides the abbreviated code representing the specific state or region where the data was collected.

LocationDesc: Full name of the geographic location. This column gives the full name of the state or region corresponding to the LocationAbbr.

DataSource: The source of the data. In healthcare, this column identifies the organization or system responsible for collecting the data, such as the Youth Risk Behavior Surveillance System (YRBSS) in the provided sample.

Topic: The topic/category of the chronic disease indicator. This column categorizes the health indicators into broader topics or areas of focus, such as "Alcohol" in the provided sample.

Question: The specific question related to the chronic disease indicator. This column contains the actual question asked during data collection, e.g., "Alcohol use among youth" in the provided sample.

Response: The response to the question. In some cases, data might have multiple response options, but in the provided sample, this column appears to be blank, possibly indicating a non-response or open-ended question.

DataValueUnit: The unit of measurement for the data values. In healthcare data, this column specifies the unit in which the health indicators are measured, such as percentages ("%") in the provided sample.

DataValueTypeID: ID representing the type of data value. This column provides an identifier for the type of data value recorded, e.g., "CrdPrev" indicating Crude Prevalence in the provided sample.

DataValueType: The type of data value. This column describes the nature or representation of the data, such as "Crude Prevalence" in the provided sample, which may indicate the raw or unadjusted prevalence value.

DataValue: The actual data value for the indicator. This column contains the measured value of the health indicator, e.g., "-" or "No data available" in the provided sample, indicating missing or unavailable data.

DataValueAlt: Alternative data value. In some cases, data might have alternative values or transformations, but this column appears to be blank in the provided sample.

DataValueFootnoteSymbol: Symbol representing the footnote for the data value. This column contains a symbol that corresponds to a footnote explaining specific aspects or caveats related to the data value.

DatavalueFootnote: Footnote for the data value. Footnotes provide additional contextual information or explanations for the data value, e.g., "No data available" in the provided sample.

LowConfidenceLimit: The lower limit of confidence interval for the data value. In healthcare data, this column might contain the lower bound of the confidence interval, which helps estimate the range within which the true value lies.

HighConfidenceLimit: The higher limit of confidence interval for the data value. Similarly, this column might contain the upper bound of the confidence interval for the health indicator's value.

StratificationCategory1: The first category for stratification. Healthcare data often requires stratification to analyze variations in health indicators across different groups. This column represents the first category used for stratification, such as "Overall" in the provided sample.

Stratification1: The stratification value for category 1. This column contains the specific value or label corresponding to the first stratification category, e.g., "Overall" in the provided sample.

StratificationCategory2: The second category for stratification. If further stratification is required, this column would represent the second category.

Stratification2: The stratification value for category 2. Similarly, this column would contain the value or label corresponding to the second stratification category.

StratificationCategory3: The third category for stratification. If additional stratification is used, this column would represent the third category.

Stratification3: The stratification value for category 3. Likewise, this column would contain the value or label corresponding to the third stratification category.

GeoLocation: Geographic location coordinates (latitude and longitude) for the data point. In healthcare data, this column provides the geographic location of where the data was collected, which can be useful for geospatial analysis.

TopicID: ID representing the topic/category of the chronic disease indicator. This column might serve as a unique identifier for the broader topics or areas of interest in the dataset.

QuestionID: ID representing the specific question related to the chronic disease indicator. Similarly, this column could act as a unique identifier for individual questions in the dataset.

ResponseID: ID representing the response to the question. If the responses to the questions have specific identifiers, they would be captured in this column.

LocationID: ID representing the geographic location. This column may serve as a unique identifier for different geographic locations, such as states or regions.

StratificationCategoryID1: ID representing the first category for stratification. This column could be used to uniquely identify the different stratification categories, such as "OVERALL" in the provided sample.

StratificationID1: ID representing the stratification value for category 1. Similarly, this column would contain a unique identifier for the first stratification value, e.g., "OVR" for Overall in the provided sample.

StratificationCategoryID2: ID representing the second category for stratification. If there are more stratification categories, their unique identifiers would be captured in this column.

StratificationID2: ID representing the stratification value for category 2. Likewise, this column would contain the unique identifier for the second stratification value.

StratificationCategoryID3: ID representing the third category for stratification. If the data requires additional stratification, this column would include the unique identifiers for the third stratification category.

StratificationID3: ID representing the stratification value for category 3. Similarly, this column would contain the unique identifier for the third stratification value.

In summary, these columns contain various aspects of the healthcare data, such as the time period, geographic location, data values, stratification categories, and identifiers, all of which are crucial for conducting data analysis and drawing insights in the healthcare domain.