Annual number of interprovincial migrants by province of origin and destination, Canada, provinces and territories.

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update.

A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases.

1. All cases: Includes cases among all San Francisco residents regardless of vaccination status.

2. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated.

3. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.”

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available.

B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address.

We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included.

C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day.

D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”.

Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time.

The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category.

New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days.

New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a particular day with the prior six days. Percent of total new cases shows the percent of all cases on each day that were among a particular vaccination status.

Here is more information on how each case rate is calculated:

1. The case rate for all cases is equal to the number of new cases among all residents divided by the estimated total resident population.

2. Unvaccinated case rates are equal to the number of new cases among unvaccinated residents divided by the estimated number of unvaccinated residents. The estimated number of unvaccinated residents is calculated by subtracting the number of residents that have received at least one dose of a vaccine from the total estimated resident population.

3. Completed primary series case rates are equal to the number of new cases among completed primary series residents divided by the estimated number of completed primary series residents. The estimated number of completed primary series residents is calculated by taking the number of residents who have completed their primary series over time and adding a 14-day delay to the “date_administered” column, to align with the definition of “Completed primary series cases” above.

E. CHANGE LOG

• 6/28/2023 - data on cases by vaccination status are no longer being updated. This data is currently through 6/20/2023 (as of 6/28/2023) and will not include any new data after this date.
  
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “sf_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 7/15/2022 - references to “fully vaccinated” replaced with “completed primary series” in column “vaccination_status".
• 7/15/2022 - rows with “partially vaccinated” in column “vaccination_status” removed from dataset.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

Abstract copyright UK Data Service and data collection copyright owner.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution....

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update. A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases. All cases: Includes cases among all San Francisco residents regardless of vaccination status. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.” On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available. B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address. We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included. C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day. D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”. Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time. The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category. New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days. New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a part

Apple stock prices from years 2014 to 2023. This dataset can be used to predict price trend for next day based on technical indicators.

target : Price trend for next day - Multi Class Classification - bullish - If price increases more than 0.5% - bearish - If price fall more than 0.5% - neutral - If price movement stay with -0.5% to +0.5% range Following technical indicators included: - SMA: Simple Moving Average. Aid in determining if an asset price will continue or if it will reverse a bull or bear trend. - EMA: Exponential Moving Average. Shows how the price of an asset or security changes over a certain period of time. The EMA is different from a simple moving average in that it places more weight on recent data points - RSI: Relative Strength Index. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. Bolling: Bollinger Band. Generate oversold or overbought signals - MACD: Moving Average Convergence Divergence. A trend-following momentum indicator that shows the relationship between two exponential moving averages. - CCI: Commodity Channel Index. A technical indicator that measures the difference between the current price and the historical average price. - TR: True Range. Measures the daily range plus any gap from the closing price of the preceding day. - ATR: Average True Range. Average of true ranges over the specified period. ATR measures volatility, taking into account any gaps in the price movement.

About this dataset

Context

The dataset tabulates the Deland population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Deland across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Deland was 43,009, a 4.14% increase year-by-year from 2022. Previously, in 2022, Deland population was 41,299, an increase of 4.52% compared to a population of 39,514 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Deland increased by 22,110. In this period, the peak population was 43,009 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Deland is shown in this column.
• Year on Year Change: This column displays the change in Deland population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Deland Population by Year. You can refer the same here

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

There's a joke among HR specialists that the most requested report from company stakeholders is an annual list of employee birthdays. The problem is that even in 2023, the joke isn’t too far from the truth. HR has come a long way of late. During Covid, not to mention the Great Resignation that followed, leaders everywhere came to depend on people insights.

But here’s the reality: much of the HR data is still stuck in HR, and HR professionals generally lag behind with respect to analytics and data visualization competency. The purpose of this use case is to dig deeper than reading rows and columns, and get insights from that amount of data to understand those people, the company’s strategy and make a move forward in the data world.

The HR-related dataset is provided by Dr. Carla Patalano and Dr. Rich, which is used in one of their graduate MSHRM courses called HR Metrics and Analytics, at New England College of Business. Therefore, we have no general problem to study but a multitude of HR KPI to uncover.

Inspiration

Here are some questions I answered: Is the absence of our employee due to a low satisfaction ? Is the performance of our employees influenced by the manager? What about the state of diversity in our company ? Are our employees paid enough, compared with the market in 2023, in the USA ?

Background The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.Longitudinal data The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

Secure Access data Secure Access longitudinal datasets for the LFS are available for two-quarters (SN 7908) and five-quarters (SN 7909). The two-quarter datasets are available from April 2001 and the five-quarter datasets are available from June 2010. The Secure Access versions include additional, detailed variables not included in the standard 'End User Licence' (EUL) longitudinal datasets (see under GNs 33315 and 33316).

Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to:day, month and year of birthstandard occupational classification (SOC) relating to second job, job made redundant from, last job, apprenticeships and occupation one year agofive digit industry subclass relating to main job, last job, second job and job one year agoThese extra variables are not available for every quarter or dataset. Users are advised to consult the 'LFS Variable Catalogue' file available in the Documentation section below for further information. Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA GLA Covid-19 Mobility Report Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements. The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking. Public Transport For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house: activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Restaurants OpenTable State of the Industry 2022-02-19 London restaurant bookings made through OpenTable 0% 0.17% 0.024% Home Working The Google Mobility Report estimates changes in how many people are staying at home and going to places of work compared to normal. It's difficult to translate this into exact percentages of the population, but changes back towards ‘normal' can be seen to start before any lockdown restrictions were lifted. This value gives a seven day rolling (mean) average to avoid it being distorted by weekends and bank holidays. name Source Latest Baseline Min/max value in Lockdown 1 Min/max value in Lockdown 2 Min/max value in Lockdown 3 Residential Google Mobility Report 2022-10-15 Estimates changes in how many people are staying at home for work. Compared to baseline of 5 weeks from 3 Jan '20 131% 119% 125% Workplaces Google Mobility Report 2022-10-15 Estimates changes in how many people are going to places of work. Compared to baseline of 5 weeks from 3 Jan '20 24% 54% 40% Restriction Date end_date Average Citymapper Average homeworking Work from home advised 17 Mar '20 21 Mar '20 57% 118% Schools, pubs closed 21 Mar '20 24 Mar '20 34% 119% UK enters first lockdown 24 Mar '20 10 May '20 10% 130% Some workers encouraged to return to work 10 May '20 01 Jun '20 15% 125% Schools open, small groups outside 01 Jun '20 15 Jun '20 19% 122% Non-essential businesses re-open 15 Jun '20 04 Jul '20 24% 120% Hospitality reopens 04 Jul '20 03 Aug '20 34% 115% Eat out to help out scheme begins 03 Aug '20 08 Sep '20 44% 113% Rule of 6 08 Sep '20 24 Sep '20 53% 111% 10pm Curfew 24 Sep '20 15 Oct '20 51% 112% Tier 2 (High alert) 15 Oct '20 05 Nov '20 49% 113% Second Lockdown 05 Nov '20 02 Dec '20 31% 118% Tier 2 (High alert) 02 Dec '20 19 Dec '20 45% 115% Tier 4 (Stay at home advised) 19 Dec '20 05 Jan '21 22% 124% Third Lockdown 05 Jan '21 08 Mar '21 22% 122% Roadmap 1 08 Mar '21 29 Mar '21 29% 118% Roadmap 2 29 Mar '21 12 Apr '21 36% 117% Roadmap 3 12 Apr '21 17 May '21 51% 113% Roadmap out of lockdown: Step 3 17 May '21 19 Jul '21 65% 109% Roadmap out of lockdown: Step 4 19 Jul '21 07 Nov '22 68% 107%

Do you need a data science project in finance domain?

Do you want to work on freshly collected dataset?

Do you need a newly created dataset to work on so that your project can help you get a job?

Do you also need an example on how to perform exploratory data analysis on time series data?

Then look no where. This is because you are at the right place and at the right time.

This dataset was collected using pandas_datareader library. Data source is stooq. This dataset consist of the stock price of Meta/Facebook from January, 2019 till April, 2023. This data can be used to perform time series analysis on any stock price such as open, close, high or low.

I have also created a notebook showing how you can perform exploratory data analysis on time series data.

TASK: 1. Determine moving average. 2. Determine weighted moving average. 3. Determine cumulative moving average. 4. Determine exponential moving average.

On April 5, 2023, the Children’s Bureau issued Information Memorandum 23-04 on the changes to Medicaid eligibility for youth/young adults age 18 and older who transition out of foster care and move to a new state. On the webinar, the Center for Medicare & Medicaid Services (CMS) will provide an overview to the changes made by the Substance Use-Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities (SUPPORT) Act and the State Health Official Letter issued by CMS. Following the CMS overview, the Children’s Bureau will provide information on the action steps states can take to collaborate on the implementation. A critical component of the webinar is to provide information on how the implementation of the SUPPORT Act can be used to address the inequities and health disparities experienced by young people as they transition out of foster care. Leaders from the Center for Medicare & Medicaid Services (CMS) and the Administration on Children, Youth, and Families (ACYF) presented during the webinar. The webinar was originally recorded on May 11, 2023. Audio Description Version Metadata-only record linking to the original dataset. Open original dataset below.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4560787%2F1bf7d8acca3f6ca6adbae87c95df1f33%2F1_MIXrCZ0QAVp6qoElgWea-A.jpg?generation=1697784111548502&alt=media" alt="">

Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

Data availability:

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research the data is not available.

This dataset contains data collected between 2021 and 2023 for the Park-MOVE project.

This project aims to explore brain activity during complex walking tasks involving a combination of motor and cognitive skills. The aim is to study the effects of aging and neurological disease on the performance and neural correlates of these tasks, using non-invasive measures of brain activity with functional near infrared spectroscopy, fNIRS, and advanced gait analysis methods.

Data is collected from younger healthy adults (YA), older healthy adults (OA) and people with Parkinson's disease (PD).

In this dataset, there is data from 42 YA, 49 OA, and 42 PD.

The dataset contains the following types of data:

• fNIRS data
• Clinical data
• Questionnaire data
• Inertial measurement unit (IMU) data
• Walking protocol task performance data
• Neuropsychological test battery data

See project_overview.md for an overview of data. Each type of data has a detailed README file detailing how data has been processed.

Most of the data is stored in CSV format and can be read by any program. The fNIRS data is stored in the BIDS and SNIRF standard formats. Gait parameters calculated from IMU data are stored in CSV format, but raw data is also available in the Apache Parquet format, which can be read using e.g. the Pandas Python library.

Do you need a data science project in finance domain?

Do you want to work on freshly collected dataset?

Do you need a newly created dataset to work on so that your project can help you get a job?

Do you also need an example on how to perform exploratory data analysis on time series data?

Then look no where. This is because you are at the right place and at the right time.

This dataset was collected using pandas_datareader library. Data source is stooq. This dataset consist of the stock price of TCS from January, 2019 till April, 2023. This data can be used to perform time series analysis on any stock price such as open, close, high or low.

I have also created a notebook showing how you can perform exploratory data analysis on time series data.

TASK: 1. Determine moving average. 2. Determine weighted moving average. 3. Determine cumulative moving average. 4. Determine exponential moving average.

The population of a country is the individuals who live in it, known as residents. It also includes residents temporarily outside the country (such as those in the armed forces, diplomats, or astronauts). Each of these people has unique characteristics, such as gender, ethnicity, income level, and education level attained.

Many countries collect information on their population at regular intervals through a census. In the United States, the census is taken every ten years. The most recent census was taken in 2020. The information collected on each person is kept confidential for 72 years to protect the privacy of U.S. residents.

Governments at all levels use demographic data to create or adjust policies and programs they implement. That same data can also provide those governments, or those monitoring them, with a tool to evaluate those policies and programs to ensure they are serving people equitably or monitor the effectiveness of anti-discrimination policies. Additionally, just like we look at data geographically, when we sort data by factors like gender, ethnicity, race, or disability, we can evaluate it to identify issues impacting one group more severely than others.

The data featured in this map layer is from the United States Census Bureau from data collected in the 1930 and 1940 censuses. It was provided by the National Historical Geographic Information System, IPUMS, and the University of Minnesota. The 1930s census introduced a new question for residents. Do you own a radio? The government was curious about the popularity of this communication technology.

Many events impacted the change in population between 1930 and 1940:The Great DepressionThe New Deal is enacted by President Franklin D. RooseveltDrought and dust storms slam the Dust Bowl region

This map compares the change in population between the 1930 and 1940 censuses. The layers 1930 Population and 1940 Population display total population for 1930 and 1940 by county. The layers Percent Change 1930-1940 and Change in Population subtract the total population of 1940 from the total population of 1930 then dividing it by the total population of 1930 and multiplying it by 100 to get the percentage of change. This is the relative difference in an old value (total population 1930) and a new value (total population 1940).

((Total Population 1930 − Total Population 1940) ÷ Total Population 1930) × 100 = Percent Change

While these two layers show the same information they are styled differently. Percent Change 1930-1940 shows which counties populations increased or decreased over ten years. The darker the color the more people moved to or away from the county. Change in Population uses points of different size and colors to show the same information. The larger the circle the more people left or moved to that area. Showing the data in both ways can show the patterns in a clearer manner.CreditSteven Manson, Jonathan Schroeder, David Van Riper, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 18.0 [dataset]. Minneapolis, MN: IPUMS. 2023. Terms of Use

The stock dataset comprises historical market data for various dates. It includes opening, high, low, and closing prices, as well as trading volume for a given stock. Moving averages (MA5, MA10, MA20), relative strength index (RSI), and moving average convergence divergence (MACD) indicators provide insights into price trends and momentum. The dataset spans from May 15, 2019, to September 8, 2023. Notable fluctuations in prices and indicators suggest market volatility. Key metrics like SMA (Simple Moving Average) and EMA (Exponential Moving Average) further analyze price trends, aiding investors in making informed decisions about buying or selling the stock.