https://www.seanwarddata.com/blog daily 0.75 2024-05-01 https://www.seanwarddata.com/blog/covid-19 monthly 0.5 2024-04-22 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/9e67d392-bb59-497b-9b64-33adfbed57d9/SpatialMap.png Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - Make it stand out Fig. 5: A spatial map showing the number of cases per one million population by the tone of the country's color, and number of deaths per one million population by the size of the red circle. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1abbc63e-ebaf-477b-b995-8ee6de4135c3/JantoJul2020.png Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - In conclusion, it is disturbing to look at the statistics for the months of January to July of 2020 since the world was dealing with something so unprecedented. As one can see with the arrow on the daily deaths chart to the right that the pandemic still had quite a ways to go. Mask wearing was not as common as it soon would become, and the vaccine was still a long ways off. Testing had just started exploding but only in certain parts of the world. It would be interesting to see what happened to the statistics for the rest of the pandemic, wouldn’t it? Fig. 6: Two charts from Worldometer that show from the beginning of the pandemic to now in terms of the daily cases and the daily deaths. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/b82fbb62-97da-4364-acf2-1bf4dd090b29/WorldometersScreenShot.png Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - I was already familiar with Worldometer, myself daily checking the Covid-19 statistics myself for the state I was living in (Connecticut). I also looked at the stats for the United States, and for the world as trend comparison. I wanted to stay constantly updated on the different trends so that I could try to keep my family safe and isolated when things got really bad. For this project, the statistics that really piqued my interest and awakened my curiosity involved the number of confirmed cases, the number of tests, and the number of deaths. Since this data was divided by country, I thought that looking at the regional averages would be beneficial. Fig. 1: Screenshot of the Worldometer page which kept accurate daily statistics on different aspects of Covid-19 including cases, deaths, and tests by country. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/62a9b671-cc88-45df-9c29-28c7837cba7d/DeathsbyRegion.png Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - Looking at the average number of deaths as it related to the average number of confirmed cases for countries in each region also revealed some fascinating facts. Africa had the highest average percentage of confirmed cases resulting in death with 16.9%. One can see that the overall average deaths/confirmed cases number for Africa was actually quite low. When looking at the below spatial map, many patches in Africa are missing data (along with Southeast Asia as well). I believe that Europe had a very high deaths from confirmed cases rate (8.6%) because of the high population density and the high rate of elderly population which resulted in many more confirmed cases resulting in death. Fig. 4: A look at the regions comparing the average number of confirmed cases and how many of these cases result in death. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1713366576448-BPWIGJ3Q1M1WDKH449FS/image-asset.jpeg Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/e8e2846b-e749-4639-baa7-13cab7d27685/Regionalcases.png Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - Make it stand out Fig. 3: A look at the average numbers of cases, deaths, and tests per one million people for each of the major regions of the world. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/b06d36f1-7fd4-4172-ac37-b4b481ff9aaa/CountryFlag.png Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - Lower test rate countries only averaged a testing rate of 1% (only about 10,000 tests per million people). It also shows that just over 10% of tests resulted in a positive case (1115 total cases from around 10,000 tests) and that 3.2% of confirmed cases result in death. Medium test rate countries averaged a testing rate of 5.5%, a positive case rate of 7%, and had 3.17% of confirmed cases result in death. Higher test rate countries averaged a testing rate of 27.4%, a positive case rate of 2.6% and had 2.45% of confirmed cases result in death. Fig 2: A look at the average deaths, cases, and tests per million people for countries with higher, lower and medium test rates. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1713196406519-1A4MBFD7TOLMPFOCCM2B/image-asset.jpeg Blog - Covid-19: Cases, Tests and Deaths…January to July 2020 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.seanwarddata.com/blog/world-air-pollution monthly 0.5 2024-03-21 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/e9b3627d-cf6a-4fa5-ab5b-57b8dd29180e/AirPollution.jpg Blog - World Air Pollution Deaths 1990-2017: Slow Progression Developing - Fig. 3: The percentage of total deaths that are due to air pollution by year for the world. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/f18ac6b0-1c8b-426a-9af5-9daf59fa80cd/image-asset.jpg Blog - World Air Pollution Deaths 1990-2017: Slow Progression Developing - As I was exploring what to do for my next Udacity project, I felt like I wanted to do something regarding the environment. On kaggle.com, I came upon extensive data regarding world pollution deaths from 1990 to 2017. I had a pretty good idea about the effects of outdoor air pollution, but was taken aback by the statistics regarding household pollution deaths. The first question I asked was…what exactly is household pollution? According to the PAHO (Pan-American Health Organization) and the WHO (World Health Organization), household air pollution is defined as “the incomplete combustion of kerosene and solid fuels (i.e. wood, coal, charcoal, crop waste, dung) from the use of open fires or in poorly vented simple stoves for cooking, heating and lighting.” (1) The effects of household air pollution are devastating. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1710944391989-6FMAT4OCYE1FW4EYWV0Z/image-asset.jpeg Blog - World Air Pollution Deaths 1990-2017: Slow Progression Developing - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/c173606c-f259-492c-9e44-b2d8ea724874/Picture1.jpg Blog - World Air Pollution Deaths 1990-2017: Slow Progression Developing - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/2c6b1018-2bb8-4971-894c-da3ab199acd5/AirPollutionDeaths.jpg Blog - World Air Pollution Deaths 1990-2017: Slow Progression Developing - Fig. 5: Countries with the highest average air pollution deaths per year in the world (excluding the Top 2 countries China and India). https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/a563a9fa-1d01-4972-b540-685875443c3b/Unknown-4.jpg Blog - World Air Pollution Deaths 1990-2017: Slow Progression Developing - Looking at overall air pollution deaths per year, two countries average more than five times as many deaths as the closest countries. China and India both average over a million deaths due to air pollution per year. Let’s take a closer look at the countries just beyond China and India with the next bar chart. Fig. 4: The countries with the highest average air pollution deaths per year in the world. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/09ff6899-bffe-4524-ba24-95fd14ef0c08/Unknown-3.jpg Blog - World Air Pollution Deaths 1990-2017: Slow Progression Developing - When you look at the overall numbers worldwide for household pollution, one can see that there has been improvement overall. The 1990 average was close to 60% of pollution deaths due to household pollution. It improved in every following year until it hit the low of nearly 35% in 2017. It shows that developed countries have the knowledge and the access to cleaner forms of fuel (as shown by Americas/Europe averaging less than 15% for polluting fuels). Fig. 2 The percentage of air pollution deaths due to household pollution throughout the world by year. https://www.seanwarddata.com/blog/going-analytical monthly 0.5 2024-02-28 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/025124dd-c9cb-4c23-adfd-9fddb354b448/1.png Blog - Going Analytical for 2020: My First Data Project - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/0947be36-11ed-43f6-8c6d-8298700bc59c/*State+Population_Deaths.png Blog - Going Analytical for 2020: My First Data Project - Fig. 6: A chart showing the number of influenza deaths by state against the population of every state. Please note that this chart does not include the eight states with the highest number of influenza deaths. Moving on to the rate of influenza deaths by population, the line in Fig. 4 represents the average rate of influenza deaths by population. The states located to the right of the line all have higher than average influenza deaths by population. States such as Tennessee, Missouri, Alabama, and Kentucky have a higher influenza rate while Georgia, New Jersey, Washington and Arizona all have a lower influenza rate. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/d3504dce-03c1-4d0c-b217-7cf7b96d5555/IMG_4127.JPG Blog - Going Analytical for 2020: My First Data Project - 2020 always sounded like a really cool futuristic year to me while growing up. Like “grab your hoverboard, let’s fly to the virtual 3d movie theater on the skyway in our shiny futuristic garb.” Instead, 2020 started with me losing the sister that I grew up with, Karma. We were inseparable as children, but lost touch for many adult years. She was the most creative and vibrant individual that I have ever known. Sadly, I spent very little time with her during her last dozen years. I was a retail manager arriving at work when I got the news that Karma was gone. Losing Karma changed the way I viewed life. A few weeks later, I left retail for good and started a Data Analytics course online. My sister, Karma, certified as the “Coolest Chick ya ever met!” by many people lucky enough to have met her. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/f2998a75-715c-4449-b15c-e7f118e9d750/Influenza+Picture.png Blog - Going Analytical for 2020: My First Data Project - Over 50% of all Influenza Deaths are from 8 States! 1. California~~~~~~110,710 2. New York~~~~~~83,985 3. Texas~~~~~~~~~~56,514 4. Pennsylvania~~~47,178 5. Florida~~~~~~~~~46,764 6. Illinois~~~~~~~~~42,448 7. Ohio~~~~~~~~~~~40,386 8. North Carolina~33,724 Fig. 1: Total number of deaths from influenza from 2009-2017 in the United States. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/d78f2f53-844e-4097-ad76-8edda553fc85/2.png Blog - Going Analytical for 2020: My First Data Project - Make it stand out Fig. 3: Percentage of each state in terms of population considered “vulnerable” which mainly consists of adults over 65 and children under 5. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/4bbfc708-00dc-4c21-a93c-2352b93ea0fa/Patients+Per+Provider+%285%29.jpg Blog - Going Analytical for 2020: My First Data Project - Fig. 5: Average number of patients per provider by state from 2009-2017. The pink bar in the middle represents the rates from 90% to 110%. States #1-#13 are considered "understaffed", States #14-20 are "properly staffed" and States #21-51 are "overstaffed." It is important to study what each state already has in place in terms of staffing for the influenza season. How many patients does each provider have to take care of by average? Something interesting to note is that many of the “understaffed” states actually have some of the lowest rates of “vulnerable population”. Washington D.C and Colorado may have the lowest levels of staffing, but they also have some of the smallest percentage of vulnerable population. The only two states that have both a high vulnerable population (high need) and a high patients per provider number (understaffed) are Iowa and Arizona. Fig. 3 The average number of patients per provider for each state. The thick pink line represents the 90%-110% range of the country average. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/a5eaeec9-4a1e-48a1-8280-0220c3a76e8c/Influenza+Deaths+1.png Blog - Going Analytical for 2020: My First Data Project - Fig. 2: The total number of influenza deaths from 2009-2017 by month of the year in the United States. The next step was looking at influenza deaths by month. Clearly, the months of December, January, February, and March are the months to focus extra staffing on. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/31895f51-d678-44bf-8ede-3fc96edbcf29/Vulnerable.png Blog - Going Analytical for 2020: My First Data Project - Fig. 4: Looking at high need, medium need and low need states based on the amount of vulnerable population each state has. I divided all states into three sections: high need, medium need and low need. The high need states all have higher vulnerable populations while the low need states have far less vulnerable populations. It is no surprise to see states like Florida, West Virginia and Maine near the top. Clearly, the biggest determinate of vulnerable population is senior citizen population. According to U.S. Census Bureau, senior citizens (65 and over) make up 21% of Florida, 20% of Maine, and 19.5% of West Virginia. The national average is 16.5%. Utah and Alaska are amongst the lowest with Utah at 10.8% and Alaska at 11.1% of senior citizens as part of total population. https://www.seanwarddata.com/blog/investigating-movie-revenues-budgets-and-imdb-vote-averages monthly 0.5 2024-02-06 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/3498b10d-176d-4a92-bd88-24bd9097cd92/Actual+Revenue.png Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Looking at the highest and lowest average “actual revenue” years is fascinating. It is not surprising to see the 4 most recent years on the highest list (2012-2015). I believe that many of the other years stem from huge blockbusters that made a ton of revenue. 1973 featured “The Exorcist”, “The Sting” and “American Graffiti” (all over $100 million in revenue, highly unusual for the time). 1975 had “Jaws” and “One Flew Over the Cuckoo’s Nest”, while 1977 had “Star Wars”, “Close Encounters of the Third Kind" and “Saturday Night Fever.” 2009 had the massive blockbuster “Avatar”, along with popular installments of “Transformers” and “Harry Potter. ” Looking at the lowest average “actual revenue,” all of the bottom 6 are pre-1973 (not surprising). What is surprising is sandwiched between the HUGE years of 1975 and 1977. 1976 was not a very profitable year for movies. As the mammoth late 70’s blockbusters kept hitting box office gold, suddenly tons of money was flooding the movie market. Some of the worst actual revenue years were 1980, 1981, 1984, 1985, 1986 and 1988. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1707151699026-Q0AH3U22LDP1E3B7DJB9/image-asset.jpeg Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/7f7f1cc9-2f37-4127-8fe6-b6b944358116/Adjusted.jpg Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Fig. 2 Revenues and budgets adjusted according to inflation as compared to each other. There is no clear correlation between higher budgets and higher revenues. I decided to use the adjusted budgets and revenues for this question. They are adjusted based on how much inflation has occurred between 1960 and 2015. As seen by this chart….higher budget does NOT always guarantee higher revenue. Many low budget movies rake in huge revenues. Many high budget movies bomb at the box office. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/956c18ac-2854-4630-8a42-100c42926538/Runtimes.png Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Fig. 8: Looking at each voting average, and what the average runtime of each of those movies is. It is absolutely no surprise that the movies that have a longer runtime tend to be higher rated than movies that run a shorter runtime. Every vote average that has an average of over two hours has a rating between 7.2 and 8.3. Vote averages that have a runtime of less than 97 minutes have an average rating between 2.2 and 4.5. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/8785de96-cf5a-4bd0-b228-a2c5130c4108/Release+YEar.jpg Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Make it stand out Fig. 1: Number of movies for each year that are featured on the IMDb. More recent years feature around a thousand movies a year, while in the 1960's and 1970's there are often only between 50 and 100 movies a year. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/e81012bd-697e-451b-93a6-588bdf38c812/Actual.jpg Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Fig. 3: The actual revenue against budget for each year between 1960 and 2015. This scatterplot shows that there were increases in movies with higher revenues in the late 1970’s, the mid-1990’s and the early 2000’s. In the 1990’s, it became much more common for movies to lose money due to high budgets coupled with disappointing revenues. Seems like studios were taking more chances. There was a higher reward for big hits from the 1990’s on, so they were willing to risk having more box office bombs. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/c0831dc6-4140-4d90-bccb-a871602c212c/jaws_main-banner_photo.jpeg Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Make it stand out Movie poster for the first summer blockbuster “Jaws” from 1975….and the last time thousands of people felt completely safe on the beach. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/9195a18b-6123-42e1-81f9-fd479a01b2a3/Highest+Vote.png Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Fig. 6: The Top 20 highest "voting average" years in terms of viewer ratings on IMDb. According to IMDb contributors…the “golden age” of cinema appears to be from 1968 until 1975. Critics continue to fawn over movies such as “2001: A Space Odyssey”, “Once Upon a Time in the West”, “A Clockwork Orange”, “The Godfather I. and II.” and “Chinatown.” It is interesting to note that the 1980’s don’t show up until 1982 (ranked #15) and the 1990’s/2000’s/ and 2010’s are not in the Top 20. There are plenty of movies that are highly rated from the 1990’s on, but it is hard to keep an overall high average when there are often hundreds of movies with ratings that keep the average lower. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/246bfda3-f5c6-43d4-b1bd-629e212b79b5/Lowest+Vote.png Blog - IMDb Movie Ratings, and the Rise of the Summer Blockbuster - Fig. 7: The Bottom 20 lowest “voting average” years in terms of viewer ratings on IMDb It makes sense that most years since IMDb gained popularity online (mid-1990’s) would be featured on the lowest “voting average” years. There are a few surprises scattered here and there…despite every other year between 1968-1975 being the very highest “voting average” years, for some reason 1969 is near the bottom. The only other years before 1994 here are 1983 and 1988. https://www.seanwarddata.com/blog/impact-of-911-on-new-york-air-travel monthly 0.5 2024-01-18 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/26dc6178-a4c3-47e6-b2d5-ed520addaa7c/911Numbers1.png Blog - The Impact of the 9/11 Attacks on New York Air Travel - Make it stand out Fig. 3 A look at the total number of minutes for delays/airtime, the number of (scheduled) flights and the number of canceled flights for each section of September. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/bf3ab3c9-b29b-4563-a07c-f73985e5d8b4/911Cancel.png Blog - The Impact of the 9/11 Attacks on New York Air Travel - Now let’s look at canceled flights by day for the month of September 2001… Just about what I expected…a ton of canceled flights on 9/11 and a few days after that. A “new normal” established around the 16th and less canceled flights since there were far less scheduled flights. But something looks strange… Let’s look closer, shall we? https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/e1f6795c-efd6-4a37-902b-033f2bc3318c/911Cancel3.png Blog - The Impact of the 9/11 Attacks on New York Air Travel - That is an unusual number of canceled flights for September 10th… Way more than all 9 days prior…why is this? I looked it up on the internet…why so many canceled flights on September 10th? I found no information as to why this would happen. Were any flights from 9/10 canceled because of 9/11? I don’t find this likely. This data only shows continental US flights, and all flights were grounded by 10am. Was it a Monday thing? No. That explains nothing. A flaw in the data?? Hmmm, there are no specific times in the data. It is possible that many canceled 9/11 flights were accidentally put in as 9/10. A massive conspiracy????? Just stop. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/726ead3a-e0d6-4009-92dd-1734d7719fe5/911Airtime.png Blog - The Impact of the 9/11 Attacks on New York Air Travel - Make it stand out Fig. 1: A bar chart depicting the total amount of airtime minutes for all flights arriving and departing every New York airport for the month of September 2001. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1705425294370-GHB6UGFRMONY6P3X6T6K/image-asset.jpeg Blog - The Impact of the 9/11 Attacks on New York Air Travel - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.seanwarddata.com/blog/bostonvsseattle monthly 0.5 2024-01-01 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1c8b371d-4fa6-4279-9264-c1d32dccbf78/Unknown-4.png Blog - Boston vs. Seattle… MLB? Nah…it’s Airbnb! - Make it stand out Fig. 2: Those same 30 neighborhoods from Fig. 1 but comparing the Airbnb review score averages. Seattle dominates. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/2db49993-f90d-48e6-af7d-70886aea03f4/Unknown-3.png Blog - Boston vs. Seattle… MLB? Nah…it’s Airbnb! - Make it stand out Fig. 1: A horizontal bar chart showing the 30 most expensive Airbnb neighborhoods in Boston and Seattle and what the average prices for a one night stay are. Boston dominates. https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/2a1537d8-01ff-42fa-bf65-15b90a3abecf/Unknown.png Blog - Boston vs. Seattle… MLB? Nah…it’s Airbnb! - The total number of reviews is actually 84,829 in Seattle and 68,208 in Boston. So how different are Seattle and Boston? At the time of this data (2016) Seattle had a population of around 726,000 and Boston had 672,800. Pretty similar so far… But in terms of space the cities take up? Seattle takes up 84 square miles to Boston’s 48.3 square miles. Yikes… https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/0821226c-cf1b-43a6-8f83-694dad4655d7/SeattleBoston.jpg Blog - Boston vs. Seattle… MLB? Nah…it’s Airbnb! https://www.seanwarddata.com/projects2021 daily 0.75 2026-01-04 https://www.seanwarddata.com/home daily 1.0 2026-01-04 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/574e037f-227c-48f1-bafc-88c7d7ac824d/Photos+Desktop.jpg Home - Sean Ward -Data Analyst- There I am exploring with my son, James! https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/ef390bcf-01ee-42af-bbcc-8da4b7ba1006/Attr2.png Home - These are some of the projects that I accomplished as part of the Data Analytics Bachelors Degree Online Program at Southern New Hampshire University. HR Attrition Analysis https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/031eb9f0-3215-4ed7-8c5d-d3d3c20b1726/Screen+Shot+2023-12-11+at+9.19.05+PM.png Home - My first project for the engaging online “Data Analyst Nanodegree”program from Udacity. Delving into TMDB (The Movie Database) statistics regarding 10,000 movies from 1960 until 2015 using Python. Wrangling the data into shape to look at the interplay between revenue/budget (both actual and adjusted due to inflation) and vote averages. Investigating Movie Revenues, Budgets, and TMDB Vote Averages Over Time https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1624384849116-VUY4E84S9WM6JD2GVMYW/Ward_Sean_Portfolio_page-0005.jpg Home - This is a collection of my projects done for my fantastic CareerFoundry course (Data Immersion) online. This includes descriptive analysis in Excel with video game data, analyzing influenza data in Excel then creating visualizations through Tableau, and generating customer spending habit statistics with the use of SQL. I also used exploratory analysis, wrangled and merged grocery data through Python and used advanced visual analysis within Python to explore the vast Covid-19 statistics regarding tests, cases, and deaths. My Portfolio https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/e5cd23f5-f18c-4c1d-b1c2-c0fcb9c0824e/SeaBos.jpg Home I will be writing a blog every few weeks reflecting on past projects, and journeying through current projects. I want to make a blog that you don’t have to be an expert Data Analyst genius to enjoy… Boston vs. Seattle… MLB? Nah…it’s Airbnb! an excerpt: “Boston and Seattle. Two very popular places to visit. Large cities on the water. How different can they be? I won’t be answering that question today… My question today is…how different can they Airbnb? Well, looking at Airbnb data, they are quite different indeed.” https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1619046331107-4G9F0ZC0NNC9P8UJ8XRW/image-asset.png Home - This was one of the achievement projects for my Data Immersion course on Career Foundry focused on preparing and analyzing data in Excel and then creating visualizations to tell a story in Tableau. The goal was to help a medical staffing agency determine where and when to send temporary workers during influenza season in the United States. Influenza Care Plans by State, Month and Population https://www.seanwarddata.com/resume daily 0.75 2023-12-21 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/27bf47cf-af49-4b44-bf64-0431146c2263/Page+1.png Resume https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/fbdb0c1c-5724-401b-a019-06c79f9317bb/Paggge+2.png Resume https://www.seanwarddata.com/influenza daily 0.75 2021-04-23 https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1619206512284-FICBLQ79DK59O7RFSPPQ/0004.jpg Influenza Project https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1619206538822-M3KEL9HZKBAJ95ZG9ZQ0/0005.jpg Influenza Project https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1619206457044-BZ4Z1Y8RUV7BR7M3N0PR/0002.jpg Influenza Project https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1619206487553-2EFS28KIG5U4W3A3A44Q/0003.jpg Influenza Project https://images.squarespace-cdn.com/content/v1/608059d5dd1b837c0abebc8c/1619206405383-9D3ZJMQGAGF48BRE0DQJ/0001.jpg Influenza Project https://www.seanwarddata.com/contact daily 0.75 2021-04-25 https://www.seanwarddata.com/instacart daily 0.75 2021-06-01 https://www.seanwarddata.com/projects2223 daily 0.75 2026-01-04 https://www.seanwarddata.com/projects2425 daily 0.75 2026-01-04