All data collected from public databases, mostly government databases or government public surveys. All data are proportioned according to the relevant age group. *Crime data, 2000-2019 average *GDP data, 2020 *Drug, alcohol and mental health data, 2018 *Gun data, 2020 *Population data, 2018

For my vizs:https://public.tableau.com/app/profile/enver2358

Source; *https://corgis-edu.github.io/corgis/csv/state_crime/ *https://www.openicpsr.org/openicpsr/project/105583/version/V5/view *https://www.rand.org/pubs/tools/TL354.html *https://www.samhsa.gov/data/report/2017-2018-nsduh-estimated-totals-state *bigquery-public-data.census_bureau_acs.state_2018_5yr *https://crime-data-explorer.app.cloud.gov/pages/explorer/crime/arrest *https://apps.bea.gov/regional/downloadzip.cfm

This dataset encompasses the metadata drawn from preserving and visualizing the Rural Route Nomad Photo and Video Collection. The collection consists of 14,058 born-digital objects shot on over a dozen digital cameras in over 30 countries, on seven continents from the end of 2008 through 2009. Metadata was generated using ExifTool, along with manual means, utilizing OpenRefine and Excel to parse and clean.

The dataset was a result of an overriding project to preserve the digital content of the Rural Route Nomad Collection, and then visualize photographic specs and geographic details with charts, graphs and maps in Tableau. A description of the project as a whole is publicly forthcoming. Visualizations can be found at https://public.tableau.com/app/profile/alan.webber5364.

Data by medical encounter for the following conditions by age, race/ethnicity, and sex (gender):

Influenza (Flu) Flu/Pneumonia Pneumonia Urinary Tract Infections

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population. Blank Cells: Events less than 11 are suppressed. Starting with data year 2022, geographies with less than 20,000 population contain no age-adjusted rates and all rates based on events <20 are suppressed due to statistical instability. Rates not calculated in cases where zip code is unknown. SES: Is the median household income by Subregional Area (SRA) community. Data for SRA only.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS), 2022. California Department of Health Care Access and Information (HCAI), Emergency Department Discharge Database and Patient Discharge Database, 2022. SANDAG Population Estimates, 2022 (v11/23). 2022 population estimates were derived from the 2020 decennial census. Comparison of rates to prior years may not be appropriate. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, May 2024.

2022 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2022COREDataGuideandDataDictionary/Home

The Tableau View extension for CKAN enables the display of Tableau Public visualizations directly within CKAN datasets. By providing a view plugin, this extension allows users to embed interactive Tableau vizzes, enhancing data presentation and exploration capabilities within the CKAN platform. This offers a seamless integration path for organizations already utilizing Tableau Public to share insights drawn from their data. Key Features: Tableau Public Viz Integration: Embed Tableau Public visualizations within CKAN resources through a dedicated view plugin. This plugin allows for the display of interactive Tableau dashboards alongside the underlying data. Simple Configuration: The extension primarily requires enabling the tableau_view plugin within the CKAN configuration file. Further configuration details and display examples may be available on the extension's wiki page (if any wiki pages exist). Streamlined Data Visualization: Provides a direct method to visually represent data managed in CKAN, improving user engagement and comprehension. Use Cases: Open Data Portals: Governments and organizations can use this extension to embed publicly available Tableau visualizations in their open data portals, enhancing the accessibility and understandability of data. Internal Data Dashboards: Organizations using CKAN for internal data management can use the extension to embed Tableau dashboards providing data summaries, trends, and performance metrics. Technical Integration: The extension integrates into CKAN as a view plugin. Once the tableau_view plugin is enabled in the CKAN configuration file (ckan.plugins), it becomes available as a view option for resources that support it. The readme suggests referring to a wiki page for additional configuration details, which, if available, is crucial for proper setup and usage. Benefits & Impact: The Tableau View extension streamlines data visualization for CKAN users. By embedding interactive Tableau Public visualizations, it becomes easier for users to explore, analyze, and understand the data managed by CKAN. This can lead to improved data literacy, more informed decision-making, and broader engagement with open data initiatives.

Netflix is a popular streaming service that offers a vast catalog of movies, TV shows, and original contents. This dataset is a cleaned version of the original version which can be found here. The data consist of contents added to Netflix from 2008 to 2021. The oldest content is as old as 1925 and the newest as 2021. This dataset will be cleaned with PostgreSQL and visualized with Tableau. The purpose of this dataset is to test my data cleaning and visualization skills. The cleaned data can be found below and the Tableau dashboard can be found here .

Data Cleaning

We are going to: 1. Treat the Nulls 2. Treat the duplicates 3. Populate missing rows 4. Drop unneeded columns 5. Split columns Extra steps and more explanation on the process will be explained through the code comments

--View dataset

SELECT * 
FROM netflix;

--The show_id column is the unique id for the dataset, therefore we are going to check for duplicates
                                  
SELECT show_id, COUNT(*)                                                                                      
FROM netflix 
GROUP BY show_id                                                                                              
ORDER BY show_id DESC;

--No duplicates

--Check null values across columns

SELECT COUNT(*) FILTER (WHERE show_id IS NULL) AS showid_nulls,
    COUNT(*) FILTER (WHERE type IS NULL) AS type_nulls,
    COUNT(*) FILTER (WHERE title IS NULL) AS title_nulls,
    COUNT(*) FILTER (WHERE director IS NULL) AS director_nulls,
    COUNT(*) FILTER (WHERE movie_cast IS NULL) AS movie_cast_nulls,
    COUNT(*) FILTER (WHERE country IS NULL) AS country_nulls,
    COUNT(*) FILTER (WHERE date_added IS NULL) AS date_addes_nulls,
    COUNT(*) FILTER (WHERE release_year IS NULL) AS release_year_nulls,
    COUNT(*) FILTER (WHERE rating IS NULL) AS rating_nulls,
    COUNT(*) FILTER (WHERE duration IS NULL) AS duration_nulls,
    COUNT(*) FILTER (WHERE listed_in IS NULL) AS listed_in_nulls,
    COUNT(*) FILTER (WHERE description IS NULL) AS description_nulls
FROM netflix;

We can see that there are NULLS. 
director_nulls = 2634
movie_cast_nulls = 825
country_nulls = 831
date_added_nulls = 10
rating_nulls = 4
duration_nulls = 3 

The director column nulls is about 30% of the whole column, therefore I will not delete them. I will rather find another column to populate it. To populate the director column, we want to find out if there is relationship between movie_cast column and director column

-- Below, we find out if some directors are likely to work with particular cast

WITH cte AS
(
SELECT title, CONCAT(director, '---', movie_cast) AS director_cast 
FROM netflix
)

SELECT director_cast, COUNT(*) AS count
FROM cte
GROUP BY director_cast
HAVING COUNT(*) > 1
ORDER BY COUNT(*) DESC;

With this, we can now populate NULL rows in directors 
using their record with movie_cast 

UPDATE netflix 
SET director = 'Alastair Fothergill'
WHERE movie_cast = 'David Attenborough'
AND director IS NULL ;

--Repeat this step to populate the rest of the director nulls
--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET director = 'Not Given'
WHERE director IS NULL;

--When I was doing this, I found a less complex and faster way to populate a column which I will use next

Just like the director column, I will not delete the nulls in country. Since the country column is related to director and movie, we are going to populate the country column with the director column

--Populate the country using the director column

SELECT COALESCE(nt.country,nt2.country) 
FROM netflix AS nt
JOIN netflix AS nt2 
ON nt.director = nt2.director 
AND nt.show_id <> nt2.show_id
WHERE nt.country IS NULL;
UPDATE netflix
SET country = nt2.country
FROM netflix AS nt2
WHERE netflix.director = nt2.director and netflix.show_id <> nt2.show_id 
AND netflix.country IS NULL;


--To confirm if there are still directors linked to country that refuse to update

SELECT director, country, date_added
FROM netflix
WHERE country IS NULL;

--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET country = 'Not Given'
WHERE country IS NULL;

The date_added rows nulls is just 10 out of over 8000 rows, deleting them cannot affect our analysis or visualization

--Show date_added nulls

SELECT show_id, date_added
FROM netflix_clean
WHERE date_added IS NULL;

--DELETE nulls

DELETE F...

Dataset Overview

Dataset Name: "Nuestro Amazon" E-Commerce Dataset

General Description: This dataset represents an e-commerce database containing information about products, categories, customers, orders, and more. The data is structured to facilitate analysis and insights into various aspects of an e-commerce business.

Structure and Attributes: The dataset consists of eight tables: categories, customers, employees, orders, ordersdetails, products, shippers, and suppliers. These tables encompass key information such as product details, customer information, order details.

Data Source: The data was generated for educational and demonstration purposes to simulate an e-commerce environment. It is not sourced from a real-world e-commerce platform.

Usage and Applications: This dataset can be utilized for various purposes, including market basket analysis, customer segmentation, sales trends analysis, and supply chain optimization. Analysts and data scientists can derive valuable insights to improve business strategies.

Acknowledgments and References: The dataset was created for educational use. No specific external sources were referenced for this dataset.

Explore Interactive Visualizations

"Quantity per country" in this Kaggle notebook or on Tableau.

"Orders by country" in this Kaggle notebook or on Tableau.

Data Analysis

"Data Analysis of Online Orders" in this Kaggle notebook

"Data Visualization and Analysis in R" in this Kaggle notebook

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

The Social Sustainability global database and its visualization dashboard https://public.tableau.com/app/profile/social.sustainability.and.inclusion.world.bank/viz/SocialSustainabilityGlobalDashboard2_0/Historia1?publish=yes/ are global public goods produced by the Social Development Global Practice of The World Bank Group. They feature leading indicators of inclusion, resilience, social cohesion, and process legitimacy, for 222 countries, disaggregated by population group and analyzed spatially and over time. In addition, the dashboard allows the user to overlay the indicators in the geospatial platform of the World Bank Group.

Data Sources, technical note: The database and dashboard draw from publicly available data sources comprising Barometers, the World Values Survey, the European Values Study, the Global Monitoring Database, ACLED, World Development Indicators, among others. The full list of data sources can be found here, and the technical note used for its construction can be accessed here.

Disaggregation: Population group disaggregation can be performed by: gender (female/male); age (15-24 years vs 25+ years or 15-29 years, 30-59 years, 60+years); location (urban/rural); ethnicity and religion (major group/others). Analysis over time can be performed for two waves: 2015-2018 and 2019-2022. Spatial analysis can be performed at the first administrative level (ADM1).

Open data: The Social Sustainability Global Database and dashboard being global public goods follow the open data and reproducibility policies of the World Bank Group. The STATA codes, codebook, and technical note used for constructing the indicators are available here in GitHub.

Climate change is one of the great threats our natural world faces today, so why doesn’t the U.S. government track greenhouse gas emissions from federal fossil fuel production that occurs on public lands? The lack of effort to record and understand climate emissions is astounding given that the federal government is one of the largest energy asset managers in the world. Limited data leaves Americans, the owners of public lands and shareholders of federal energy resources, in the dark on the extent to which fossil fuel emissions from public lands are contributing to rising global temperatures. In absence of government oversight, The Wilderness Society has started to track and calculate emissions data, using various government sources, including data from the Office of Natural Resources Revenues (ONRR) and US Extractive Industries Transparency Initiative (USEITI).What we found was staggering. Among the results:Greenhouse gas emissions associated with oil, gas, and coal from public lands are equivalent to one-fifth or more of total US emissions. If U.S. public lands were their own country, they would rank 5th in the world for greenhouse gas emissionsOur accompanying report In The Dark: The hidden climate impacts of energy develop on public lands also explains more about why the federal government needs to inform its shareholders (the American people) when managing their assets (energy resources on public lands) .

The Council’s District of Columbia Public School’s (DCPS) Dashboard is an interactive data visualization tool, built in Tableau, that allows users to analyze the agency’s operating budget, expenditure, human resources (HR), and programmatic data. This tool is intended to assist the public in better understanding one of the District’s most complex budgets from fiscal year (FY) 2015 -2018. Please click here for a quick guide on how to use this dashboard. Visit the DC Council Office of the Budget Director website for further documentation.This dashboard blends various data from the District’s official financial and human resources systems. Other data sources for PARCC, enrollment, and star ratings were obtained from the Office of the State Superintendent of Education (OSSE). The human resources data is a snapshot as of October 1 of each fiscal year from the District’s official human resources system.

This dataset accompanies the article Beyond “Place Matters”: Making Spatial Analysis Accessible with Dashboard-Driven Geography. It includes raw and pre-processed data files, a shapefile of census tract boundaries, and a Tableau packaged workbook used in the classroom exercise. The materials demonstrate how to visualize Supplemental Nutrition Assistance Program (SNAP) enrollment and eligibility at the census tract level using Tableau. Files are provided to support replication of the published exercise and adaptation with users’ own data sources.A link to the model dashboard can be found here

Data by medical encounter for the following conditions by age, race/ethnicity, and sex (gender): Congenital Anomalies Maternal Complications

Visit https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html to view MCFHS perinatal health indicators, including: Live Births Teen Births Early Prenatal Care Preterm Birth Low Birth Weight Fetal Mortality Infant Mortality Maternal Deaths

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population. Blank Cells: Events less than 11 are suppressed. Starting with data year 2022, geographies with less than 20,000 population contain no age-adjusted rates and all rates based on events <20 are suppressed due to statistical instability. Rates not calculated in cases where zip code is unknown. SES: Is the median household income by Subregional Area (SRA) community. Data for SRA only.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS), 2022. California Department of Health Care Access and Information (HCAI), Emergency Department Discharge Database and Patient Discharge Database, 2022. SANDAG Population Estimates, 2022 (v11/23). 2022 population estimates were derived from the 2020 decennial census. Comparison of rates to prior years may not be appropriate. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, May 2024.

2022 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2022COREDataGuideandDataDictionary/Home

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses. Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables. Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021. This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data. This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score. This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4. The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting. These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons. For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

The dataset includes various health service-related metrics and indicators related to the Nova Scotia Health. The data is collected from multiple sources within the health system, including Hospital Inpatient, Emergency, Surgical Databases, Continuing Care Home Support and Long-term Care Reports and Emergency Health Services (EHS).The data is aggregated and anonymized to ensure privacy and does not contain any personally identifiable health information. This data set is used to build the Action for Health Public Reporting and the goal of this project is to provide accessible healthcare information to the general public, researchers, and analysts in order to improve understanding and foster improvements in the healthcare system in Nova Scotia.

Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to identify 'gaps' in knowledge of species interactions. We examine the richness of GloBI's datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.

Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI Kingdom Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.

The interactive Tableau map can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm

The companion paper can be found here: doi.org/10.5281/zenodo.814979

Complementary high resolution visualizations can be found here: doi.org/10.5281/zenodo.814922

Project-specific data can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)

Overview: This dataset combines publicly available data on obesity rates, poverty rates, and median household income for all 50 U.S. states from 2019 to 2023. It also includes calculated regional averages based on U.S. Census Bureau-defined regions (Northeast, Midwest, South, and West).

Use Cases - Public health research - Data visualization projects - Socioeconomic analysis - ML models exploring health + income

Sources - CDC BRFSS – Adult Obesity Prevalence Maps (2019–2023) - U.S. Census Bureau – SAIPE Datasets (2019–2023)

Tableau Dashboard View the interactive Tableau dashboard:
https://public.tableau.com/app/profile/geo.montes/viz/ObesityPovertyandIncomeintheU_S_2019-2023/Dashboard1#2

Created by Geo Montes, Informatics major at UT Austin

Learning Data Visualization Tools Market Outlook

According to our latest research, the global Learning Data Visualization Tools Market size reached USD 2.8 billion in 2024, demonstrating robust growth driven by the increasing demand for data literacy and analytics skills across various sectors. The market is expected to grow at a CAGR of 13.7% from 2025 to 2033, projecting a value of USD 8.8 billion by 2033. This surge is primarily attributed to the rapid digitization of education and corporate learning environments, the proliferation of big data, and the critical need for interactive, accessible analytical tools to foster effective data comprehension and decision-making.

One of the most significant growth factors for the Learning Data Visualization Tools Market is the widespread integration of data-driven decision-making processes within organizations and educational institutions. As businesses and academic settings increasingly rely on data to guide strategies, there is a parallel surge in the demand for professionals who possess strong data visualization skills. This has led to a marked increase in the adoption of user-friendly data visualization tools such as Tableau, Power BI, and Google Data Studio in both formal education and corporate training programs. The ability of these tools to simplify complex datasets into intuitive visual representations is a key driver, enabling learners to grasp intricate concepts more efficiently and apply them in real-world scenarios.

Technological advancements and the evolution of cloud-based learning platforms have further propelled the market. The shift toward digital and remote learning, especially post-pandemic, has accelerated the adoption of cloud-based data visualization tools, which offer scalability, accessibility, and seamless integration with other e-learning resources. Cloud deployment eliminates geographical barriers, allowing learners and organizations from diverse regions to access advanced visualization tools and resources at any time. Additionally, the increasing availability of free and open-source visualization libraries such as D3.js has democratized access to these technologies, further expanding the market’s reach across different socioeconomic segments.

Another crucial growth driver is the rising emphasis on upskilling and reskilling initiatives across industries. As automation and artificial intelligence reshape job requirements, data literacy has become a fundamental skill for both students and working professionals. Enterprises are investing heavily in learning platforms that incorporate data visualization tools to train their workforce, ensuring they remain competitive in the digital economy. The trend is mirrored in higher education, where curricula are being revamped to include data visualization modules, reflecting the growing recognition of its importance in fostering analytical and critical thinking skills among learners.

From a regional perspective, North America dominates the Learning Data Visualization Tools Market, accounting for the largest revenue share in 2024. This can be attributed to the presence of leading technology providers, a mature e-learning ecosystem, and high levels of digital adoption in both educational and corporate sectors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, government initiatives to enhance digital literacy, and the increasing penetration of internet and mobile devices. Europe also contributes significantly, with a strong focus on educational innovation and enterprise training. These regional dynamics are shaping the competitive landscape and driving the global expansion of learning data visualization tools.

Tool Type Analysis

The Tool Type segment of the Learning Data Visualization Tools Market is highly diverse, encompassing established platforms like Tableau, Power BI, and Qlik, as well as newer entrants such as Google Data Studio and open-source solutions like D3.js. Tableau remains a market leader due to its intuitive drag-and-drop interface, robust analytics capabilities, and widespread adoption in both academic and corporate settings. Its ability to handle large datasets and integrate seamlessly with various data sources makes it a preferred choice for institutions aiming to provide hands-on, practical training in data visualization. Power BI, backed by Microsoft’s ecosystem, is gaining significant traction, particularly among enterpr

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:

Assaults Disorders of the Teeth and Jaw Drowning Falls Firearm-Related Injuries
Heat-Related Illnesses and Injuries
Hip Fractures
Homicide (See Assault Death)
Injuries
Motor Vehicle Injuries
Motor Vehicle Injuries to Pedalcyclist
Motor Vehicle Injuries to Pedestrian
Poisoning
Unintentional Injuries

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Injury conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:
Alzheimer's Disease
Alzheimer's Disease and Related Dementias (ADRD)
Dementia
Neurocognitive Disorders
Parkinson's Disease

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality, including those of ADRD conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage

Case Study: How Can a Wellness Technology Company Play It Smart?

This is my first case study as a data analyst using Excel, Tableau, and R. This case study is a part of my Google Data Analytics Professional Certification. I know there may be some insights presented differently or any insights might not be covered as per the point of view of the reader who can provide feedback. Feedback will be appreciated.

Scenario: The Bellabeat data analysis case study! In this case, the study is to perform the real-world tasks of a junior data analyst. Bellabeat is a high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Urška Sršen, co-founder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. You have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. The insights you discover will then help guide marketing strategy for the company and present analysis to the Bellabeat executive team along with your high-level recommendations for Bellabeat’s marketing strategy. The Case Study Roadmap followed, In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Ask: Sršen asks you to analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices. She then wants you to select one Bellabeat product to apply these insights to in your presentation. These questions will guide your analysis: 1. What are some trends in smart device usage? 2. How could these trends apply to Bellabeat customers? 3. How could these trends help influence Bellabeat's marketing strategy? To produce a report with the following deliverables: 1. A clear summary of the business task 2. A description of all data sources used 3. Documentation of any cleaning or manipulation of data 4. A summary of your analysis 5. Supporting visualizations and key findings 6. Your top high-level content recommendations based on your analysis Prepare: includes Dataset used, Accessibility and privacy of data, Information about our dataset, Data organization and verification, Data credibility and integrity. The dataset used for analysis is from Kaggle, which is considered a reliable source. Dataset owner Sršen encourages to use of public data that explores smart device users’ daily habits. She points you to a specific data set: Fitbit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius): This Kaggle data set contains personal fitness trackers from thirty Fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits. Sršen tells that this data set might have some limitations, and encourages us to consider adding other data to help address those limitations by beginning to work more with this data. But, this analysis only confined primarily to the present dataset and has not yet been done analysis by adding other data to address any limitations of this dataset. I may take up later to collect additional datasets based on the availability of those datasets for individual analyst circumstances since companies provide datasets that are needed, may be available on a subscription basis or I need to search and access for similar product datasets. That is my limitation to confine my analysis to this dataset only. Process Phase: 1. Tools used for Analysis: Excel, Tableau, R studio, Kaggle 2. Cleaning of Data: includes removal of duplication of data but data itself by its nature includes Id, dates include repetition and also there are zero values by nature of recording since human beings are body and mind are complex, so the possibility of zero values inherent in data or any other reason yet to be known but an analysis done based on available data though which is not correct for live projects where someone available to discuss them. 3. Analysis was done based on available variables. Analyze Phase: Id Avg.VeryActiveDistance Avg.ModerateActiveDistance Avg.LightActiveDistance
TotalDistance Avg.Calories 1927972279 0.09580645 0.031290323 0.050709677
2026352035 0.006129032 0.011290322 3.43612904
3977333714 1.614999982 2.75099979 3.134333344
8053475328 8.514838742 0.423870965 2.533870955
8877689391 6.637419362 0.337741935 6.188709674 3420.258065 409.5...