Joe Spagnoletti

  • Just a reminder that your final exam will be on Friday, May 5 at 1:00pm in the same room as class.  Please be on time.  Students will not be permitted to enter late. Please make sure that all missing a […]

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  • Here is the study guide for the third (final) exam.

  • Here is the link for the driver download

  • Here is the exercise.

    And here is the spreadsheet you’ll need [In-Class Exercise 13.2 – VandelayOrdersAll.xlsx].

  • Leave your response as a comment on this post by the beginning of class on April 20, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]

    • A data driven service like Blackboard stores grades and announcements for a student or professor’s classes. For a student the rows are made up of the different classes they take. The columns would consist of the different assignments with the given grades below. Assignments are different for different classes so there could be homework columns, project columns, participation columns, test columns, or whatever the professor assigns.

    • Amazon is a great example of a data-driven service. If my personal amazon data was put into a spreadsheet, it would not look too complicated. The columns would display the different products that I have ordered from the website. Also, the total price I payed for the products (including shipping/handling) would be listed in the rows below. For example, if I ordered a phone charger, the column would show “iPhone 7 Charger” and “$12.23” would be listed in a row directly below.

    • One data-driven service I use constantly is Snapchat, so if I were to store the data into the spreadsheet, I’d imagine that every row in the spreadsheet would be information about every snap I have sent. Some of the data columns would include the duration of the snap, who it was sent to, and the number of emojis used, among other things. So, for instance, one row would have the snap number, the date it was sent, the time it was sent, the usernames of those it was sent to and all the data about the snap itself.

    • One data driven service i use constantly is Google. Google is a great source of information for which I identify the many questions that plague me daily. I would love to see a history of google searches that I have broken down by day, so I can identify if one day where I use google I am more likely to use google on that same day. I think I would, however I am not sure. It would also be interesting to see how many and what my searches are for an entire period like a week or a month.

    • If I am able to store the data of Blackboard’s information in a spreadsheet, I would set each row as as one course that a student takes such as: Statistic, English, and Data Science. Also, I would set each column of the spreadsheet as the average grade of the following: homework grades, assignment grades, test grades, extra credits, and lastly the letter grade for the class.

    • One great data-driven service that I frequently use is Youtube. If I store the data for the service of Youtube in a spreadsheet, I imagine that the rows would consist the name of the channels that I subscribed, and the column would represent the number of new videos were added to each specific channel.

    • One of the data-driven service that I use often is Blackboard. If the data for the service of Blackboard is store in a spreadsheet, the rows would consist the name of the courses that I take, and the column would represent my grade for that course, and also grade for each assignments, exams,…

    • A data driven site I use is Gmail. The rows in a spreadsheet would be something like the dates or times of each email, and the columns would be who I send each email to, or which groups I send my emails to. It would be a fairly straightforward spreadsheet as I do not send and receive that many emails.

    • One of the data driven tools I use is the MLB Mobile app, which stores all the stats about the MLB. One of the rows could be a specific player from the MLB, for example, Mike Trout. Some of the columns could possibly be the player’s batting average, home runs, RBI’s, stolen bases, WAR, fielding percentage, etc. There is so much data on the MLB Mobile app that I could make a spreadsheet for. The row, Mike Trout, would be the first example in the Row section, followed by all of their stats in the Column section.

    • A data driven site I use frequently is Blackboard. If I were to store this data in a spreadsheet I would display the row as the specified class I am enrolled in. The columns would consist of Attendance grade, Exam grades, Quiz grades, Homework grades, and Extra Credit. Looking at the data from Blackboard on a spreadsheet would make it easy to view how well I am performing overall in all of my classes because all of my grades will be presented in one screen.

    • A data driven site that I frequently use is fantasy football. To store all the data on a spreadsheet I would place all my players in the rows. On the columns there would be different types of stats like yards, turnovers, and touchdowns followed by total points earned at the very last column.

    • A data driven service I frequently use is the Lyft rides app. If I wanted to store the data in a spreadsheet from this service each row would represent something about my past ride history from each ride I have taking monthly. Finally, the data columns would include distance, pick up & drop off locations, fares, time periods, and ratings of each ride.

    • Using blackboard as a service that I use regularly. If I wanted to store that data on an excel spreadsheet, I would make out columns to be the names of each class that I’m enrolled in. the columns would consist of each assignment segmented by intervals of 1 week. If no assignments were scheduled then it would simply be blank. The corresponding array would have the grade value and percentage weight of the overall grade that the assignment holds. This allows the students to accurately measure there performance while actively being able to gauge there security or lack there of.

    • One data-driven service I use regularly is online banking. Each row in their spreadsheet I think would represent a different account. Some columns in the excel spreadsheet would be owner name, amount, type of account, email and address.

    • One data-driven service I use is Linkedin. If wanted to store that data in an excel spreadsheet, some things in the columns would include: first name, last name, number of connections, city, state, and industry that they are in. So in the rows would fill out all the names, # of connections, addresses and industry type.

    • A data driven service that I use often is FanGraphs, a website that shows different articles and baseball statistics. If I wanted to store the data in a spreadsheet, the columns would include the different sections of the site such as FanGraphs, RotoGraphs, Hardball Times, Podcasts, and the other listed sources of content. Each row in the spreadsheet would represent a different article from each section. For example, the column would be FanGraphs and the row would list “Marte Suspended, Pirates Lose Remaining Margin of Error.”

    • While thinking about data-driven services that I use regularly, I found that data storage in a spreadsheet can be very complex (for example for Uber, Waze or Facebook). For Blackboard, it sounds a bit easier. A row would be the performance of the student, and some of the rows would be, for example, “average exam grades”, ‘attendance”, “extra-credit’, “homework”, etc.

    • One app that I use consistently that is data driven is the ESPN app. The rows for this would include the team, what league they are playing in, their record, who their next opponent is. It could also go as far as having the overall team stats, and the individual player stats specific to each team.

    • One data-driven service is IMDB website. The rows would be the information of the movie, the columns will have director, stars name, type of movie it is, length of the movie, when it’s released, and details of the movie like language, made by which country, short introduction of the movie.

    • I order supplements on onnit.com regularly. If stored in a spreadsheet, the rows would be the dates of my orders, and the columns would include the product ordered, the size of the product ordered, the price, the loyalty points earned, and the regularity of orders of each product. Onnit offers to automate my orders by averaging how frequently I order each product and comparing it to how long a container is supposed to last. I get emails when they think I need to refill a particular product, and the formula for creating these notices would be contained somewhere in there, and would change every time I order, so there would be $’s in there too.

    • Blackboard is one of the examples of a data-driven service I use. I would put the rows as the individual information about the class. The colums would be things like name of class, course number, location of class, assignments(with the dates shown), grades etc.

    • A great example of data-driven service is IMessage.It stores all types of informations based on your text messages. It collects data such as who sent the text, what time it delivered, what time you read it, and what type of message it is. IMessage is basically set up like a table with the most recent texts you recieved first all the way to the oldest text messages.

    • One of the data-driven services that I use regularly is google plus. I use it for my character design data. I opened around 30 gmail accounts in order to store each of my characters’ data in each account. Then I manage all of them together in excel sheet. A row would be an individual’s information. A column can be name, ID, personality, height, weight, belonging group and so on.Then on the next sheet is about the group. Raw can be each group asset’s infor, and column can be different asset type like aircraft, marine vehicle, real estates…

    • A data-driven site I use a lot is amazon.com. I have a prime membership and buy tons of different things on the site. I would make an excel spreadsheet and in the first column of each row I would type in the product name. Then for the following columns I would title them price, name of seller, prime or not, number of stars I would give, date I purchased, and date received.

    • As a frequent user of Facebook and other social networking outlets, many of the activities I engage in often include sending messages, sharing my opinions on different posts, commenting on mainstream news media, viewing/liking photos, and watching live video broadcasts of people I know. The characteristics of data that could be potentially collected based off these actions can be translated analytically from a qualitative & quantitative standpoint. For example, the columns could represent the users I’ve historically interacted with and certain pages that have sparked my interest, where the rows could provide tallied statistics that count how many times I viewed a specific page, the # of messages I’ve sent to indnvidual friends over a fixed period of time, the amount of comments I’ve posted total, etc…

    • One website I use constantly would be Craigslist, Craigslist stores data by saving the items for sale in their specific category’s online. The data stored is then organized on their platform to be promoted, items get sold and taken of the market as new items are being added everyday. Storing this data on a spreadsheet could potentially be organized as the following, columns would be item type, location, price , and rows would be The specific names of the items, the listed prices ,and so on. Craigslist is an old site, but people all over the area still use it come snatch a bargain.

    • The data driven i use frequently is Blackboard. As a student, i can find different class information and what i need to do(assignment due, etc). As a result, the row of spreadsheet will be all kinds of class i choose, and the column will be the tasks i must do in a time order.

    • A data-driven service that I use regularly is Gmail. A row would be information of different letters, and some columns would be the name of the person who sent me the mail, the date, and the subject. If I had set up to include the person in to a label, then the columns of labels would have information as well.

    • I use amazon very regularly. A spreadsheet of data from this site would be simple, and include the items that I purchased in the columns and followed by the price of the items in the row beside it. I would have a total amount of money at the bottom to track how much I have spent on this site. Perhaps I would include the date purchased as well, to see if I tend to buy more items during a certain time of year.

    • One data-driven source I use on a regular basis is Amazon. With Amazon, there would be a rather large amount of data because I use this service rather extensively. Some rows that would go into the spreadsheet would be quantity of item, date of order, date of delivery,
      and number of stars. The column heading would be the name of the item.

    • While playing games, the data driven service i often use is Steam. Data are all well organized. The categories are at rows and columns and it is easy to find games. While searching particular keyword, there will be several results in the column, including the name, release date, costumer rate and price.

    • If I were to hold data for a service like that I would use an excel sheet to hold information that we need on users. If I were using an information storing system like amazon I would keep track on customers interests and specific info that I need to hold to make sure sales are up. I would rows for the types of categories each customer purchased. I would have data on how much customers purchase in a certain amount of time.

    • If I were to use the spreadsheet, I would do it on the NFL website. On the columns, I would write it the teams names. And in the rows, I would write, the starts for each team. I’d have the team listed alphabetically, so its easy find the teams and look up the stats.

    • A data-driven service I use everyday is Twitter. An example of how to store this data would be:
      Columns = Date, Username, Tweet, Profile Photo, etc.
      Rows = the actual date of the tweet, the person who tweeted it (their handle), the raw text of their tweet, profile picture image URL
      Twitter is massive obviously but something simple like this would be easy to maneuver and manipulate!

    • A data driven service I use often is Facebook. I use data analytics for seeing how my viewers are watching certain videos on my business page. If I was going to store data it would be columns with specifications like how many views total, duration of views, duration of how long they viewed it for, organic click or paid click, mobile or desktop. The rows could be names of videos or possibly individual viewers. This helps for finding what I should target in my videos for maximum efficiency.

    • A data-driven service I use is Instagram. An example of how to store data is.
      Columns= Username, Profile pic, Email.
      Rows= Photos I have liked, Comments, DMs.

    • A great example of a data driven app I use daily is a little game on my phone Clash of Clans. The owner of the app can collect all the information of User’s email, username and level and compare how much consumers are spending (in real dollars) to achieve that level. It would be interesting to see since it is a free to play game but also has many microtransactions to move through the game quicker

  • Some quick instructions:

    You must complete the quiz by the start of class on April 18, 2017.
    When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to […]

  • Here is the exercise.

    And here is the spreadsheet you’ll need for the exercise [In-Class Exercise 12.2 – Sentiment Analysis Tools.xlsx].

  • Some quick instructions:

    You must complete the quiz by the start of class on April 11, 2017.
    When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign […]

  • Leave your response as a comment on this post by the beginning of class on April 13, 2017.

    Leave a post about your group project:

    What is the subject of your group project?
    Which of your fellow […]

    • My group consists of Courtney Smith, Hien Thi Thu Dao, Jacob Coleman, Adam Yoosufali, and myself. Our project will consist of a comparison of the net economic impacts of two opposed government actions. We will explore not only the tax implications, but the effect on population, job creation or destruction, contributions to education, and other impacts of the legalization of recreational marijuana as compared to those effects as caused by the soda tax in Philadelphia.

    • My group consists of Edward Gruchacz, Nathan Ernst, Raymond Wong, James Gallo, Poojan Patel and myself. The subject of our project is Netflix usage, specifically looking at how many people use their own account versus someone else’s. We will be collecting multiple sets of data in order to tell our story, including year in school, gender and hours spent.

    • My group consists of Michael Lo, Faith Debolt, Matthew Pumell, Justin Haley, Peter Nysch (MAYBE), and myself. For our project we have decided to look at the question of whether a countries healthcare system, be it state sponsored or totally private or whatever it is, is correlated with how long people live (life expectancy) and their quality of life.

    • My group consists of Diogo Barboza, Tzu-Chun Lin, Leeah Tomes, Hsin-Jung Yu, and myself. For our project, we will be examining the people who eat apple per day and seeing whether it helps them to be healthier or not. Also, we will figure out if there is another type of fruit that is better for people’s health than apples. In addition, we will also be discussing whether the origin of apples affects the nutrition of apples or not.

    • Our group consists of Baron Pote, Vincent Kao, Vaibhav Katrodiya, Demetri Zambas, and myself. The topic of our project is the cost efficiency and the health benefits in GMO vs Organic Foods. The comparison will entail cost analysis, demographic preference, average life span and many other categories.

    • My group members are: Owen Crowley, Khoa Ha Bui, Ricardo Burton, Raymond Xu, and myself. Our group project is researching if people living in capitalist countries have a higher standard of living than people living in socialist countries. We will be using many standard country measures like GDP, GNP, average incomes, etc to complete our project.

    • My group consists of Alexandra D. Brace, Soorya S.Karthik, Alex Sy, Rabia Ugucu, and myself. We are not really sure about our project, but we maybe do the research of the relationship between the accesses to healthy food and the average income of different area in Philadelphia. We will try to find data to see if we can prove people with lower income living in the area which has less accesses to healthy food.

    • My group consists of Edward Gruchacz, Nathan Ernst, Raymond Wong, James Gallo, Poojan Patel and myself. The subject of our project is Netflix usage, specifically looking at how many people use their own account versus someone else’s. We will be collecting multiple sets of data in order to tell our story, including year in school, gender and hours spent and etc.

    • My group consists of Brendon Rothrock, Seon Mi Cho, Charif Shell, Le Tang, and myself. Our project is about America’s flawed prison system. Right now our research and data searching is very broad but we are starting to narrow down different topics within the problems in our prisons.

    • My group consists of Brendon Rothrock, Seon Mi Cho, Charif Shell and Daniel Pearson. Basically our group project is about how the prison overcrowding and disciplinary problems of American prison system. We try to focus on some specific topics with this system now to prove that system is no longer effect than before.

    • I’m in a group with Chynah Grabe, John Francis Meko, Kacie Kemmerer, and Mathew Tarasiewicz. We’re examining the relationship between company scandals and business performance. We’ll be telling our story by highlighting case studies of Uber (Trump political scandal), Target (transgender bathroom policy debate), and Sony (tentative but regarding the Sony hack). We are gathering time series data on sales, stock, and other business KPIs to aid our narrative.

  • Here is the exercise.

    Here is the excel spreadsheet you will need to complete this exercise [In-Class Exercise 11.2 – NCAA 2013-2014 Player Stats]

     

  • Some quick instructions:

    You must complete the quiz by the start of class on April 4, 2017.
    When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign […]

  • Leave your response as a comment on this post by the beginning of class on April 6, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]

    • http://www.techrepublic.com/article/march-madness-5-data-sources-that-could-predict-the-2017-ncaa-championship/
      I posted this article because I always wondered how it is possible to create a tournament bracket that does not leave me upset. This goes into multiple data sources from different websites that break down what goes into creating a good bracket.

    • https://gma.yahoo.com/police-self-driving-uber-arizona-150458162.html
      This article is interesting from my stand point because I am studying Risk Management and Insurance. Self driving cars are a new loss exposure for insurance companies and this instance with a self-driving Uber car getting into an accident is a perfect example. Insurance companies aren’t quite sure how to price these exposures and how detrimental they are to society. Although this self-driving car was not at fault for this accident, it’s difficult to determine how these cars are programmed to react to situations like being cut off in an intersection.

    • https://fivethirtyeight.com/features/how-the-gop-bill-could-change-health-care-in-8-charts/
      I picked this article because I was not fully aware of how the GOP Bill would affect people under Obamacare. I found the data graphs for representing the impact the bill would have on every segment of Obamacare to be extra helpful when trying to understand it. Overall I found the article to be pretty interesting, easy to follow and relevant to anyone living in the US, as the decision pretty much affects everyone one way or another.

    • https://fivethirtyeight.com/features/nba-teams-are-resting-players-earlier-and-earlier/
      Being a basketball fan, I found this article interesting, but also saddening. In a graph in the article it shows how from the 05′-06′ season up to this current season, NBA coaches have steadily been increasing the number of players not playing in games due to rest. It has at least, tripled since the 05′-06′ season!! I think this makes the game less competitive and less fun to watch, it is really a bad message to the fans.

    • From Shelter to Forever Home, Big Data Helps Pets

      I googled “dog data” because I really love dogs. The first few articles were about a rude graph rating dogs on a scale when they’re all good. Anyway, this one is about how big data helps shelters figure out where and when animals will be abandoned so they can find them more often. It also helps make marketing more effective so animals can find new owners. I’m a fan of big data now.

    • http://www.espn.com/nba/story/_/page/tripledoubletracker/follow-russell-westbrook-triple-double-chase
      Russell Westbrook is having a historic season. Right now in the league he is averaging a triple double. He is on his way to being the player with the most triple doubles in a season. On this website it shows graphs and charts of how Westbrook is actually doing on his pathway to being the player with the most triple doubles in one season. His triple double count has sky rocketed based on the chart on the website compared to a few years ago to this year. Also his specific stats are compared to Oscar Robertson’s stats for when he became the player with the most triple doubles in one season.


    • Oddly enough there is an article about being”OWNED” on New York Times Magazine. This article illustrates the increasing level of threats and vulnerability we are exposed to as technology advances over time. This raises many concerns as what you have on your labtop/desktop or phone can be accessed without you even knowing. Someone could potentially own all of your data including , search history, pictures(naughty or nice), documents, contracts, basically your entire life. With exposure this threatening we might all want o invest in some protection at the least.

    • Here is the article that I found interesting when I read it: http://www.pewresearch.org/fact-tank/2017/02/15/u-s-students-internationally-math-science/. The article basically gives me the general information about how well American students do in certain subjects in schools comparing to other students from other countries. The article also includes the bar chart which shows the average math, reading, and science score of the test that many students from a lot of countries took. Surprisingly, students in Singapore have the highest average score in all three subjects. And there are 17 countries have a significant higher score than the U.S in science, 35 countries have a significant higher score than the U.S in math, and 14 countries have a significant higher score than the U.S in reading.

    • Visual simulations to show Uber game strategies


      The article I chose is Visual Simulations to show Uber game strategies which was posted on April 4,2017. I thought the most interesting fact about this article is how Uber company encourages drivers to work longer and drive in certain areas by using the same visual simulations as a video game mechanic. Uber is basically mastering their workers mental circuitry to get employees to work when they want and I find this interesting and it makes me wonder how many other jobs uses this technique.

    • https://fivethirtyeight.com/features/unc-played-ugly-enough-to-win/

      So this article was interesting to me because I watched the whole championship game between Gonzaga and UNC, and boy was it ugly. Between the fouls and the overall poor fg% as the article points out, it made the game slow, choppy and difficult to watch. Like the author noted, it came down to which team played less worse, and in this case it turned out to be UNC. Congrats to them and my friend who won $240 because they played ugly enough to win.

    • http://www.post-gazette.com/sports/Pitt/2017/03/06/Sloan-Sports-Analytics-Conference-Boston-Data-driven-stats-geeks-make-their-mark-on-sports-Mark-Cuban-Nate-Silver/stories/201703060073?pgpageversion=pgevoke
      This article was very interesting to me, because I am a very big sports person as well as being very interested in the technology field. It is crazy to think how using complex systems may be used translate to perfect team chemistry, which will then translate to team success. This makes me really wonder how sports betting/gambling will be like in the future.

    • About All These Velocity Spikes


      The article discussed the increase in velocity for Major League pitchers already in the 2017 season. I thought it was interesting that despite the apparent increase in velocity, most pitchers still throw around the same velocity as last year, but the tracking systems changed. This year there was a change in where the velocity gets measured, which results in different data and large misconceptions for scouts or broadcasters.

    • http://www.baseball-reference.com/teams/PHI/2016.shtml
      I picked an article on the Philadelphia Phillies from the 2016 season because I love the Phillies. It shows each player’s stats and what they accomplished that season. It also shows the team totals from the year and what the team as a whole accomplished. One of the stats that is shown is the players WAR. WAR stands for wins above replacement. The player with the best WAR was Odubel Herrera, who had a WAR of 4.3. That is above average for a major league player. Overall, I choose this article because it had all the stats from the Phillies 2016 season and I love the Phillies.

    • Why the White House Is in for a Fight When It Comes to Repealing Net Neutrality
      I picked this because I heard about congress passing a new legislation allowing internet providers to sell its users’ browsing data. When I first heard this I did not think much of it until I started thinking about it more. I was surprised that it got that far, especially the fact that I don’t think anyone would want this. I also felt as if this did not get enough news coverage, especially since it affects all people around the country.

    • https://www.theguardian.com/technology/2017/apr/04/uber-google-waymo-self-driving-cars
      This article is about the driving statistics published by California, it shows that Google is 5,000 times better than Uber at autonomous driving. The way to measure the autonomous driving solution is how often the driver has to take over to correct shortcomings in the autonomous driving software. I think the autonomous driving is going to strike the driving market, so it’s much more better to know which one have the greater probability to win the future competition with a better performance.

    • http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-heartbeat-of-modern-marketing?cid=soc-web

      McKinsey&Company discuss how marketers use data activation and personalization strategies to reach consumers in the moment – no matter where they are or what device they’re on. Customer Data Platforms give marketers the ability to scale this messaging in real-time once they “feed” the CDP with IF/THEN rules and data regression models. In turn, the CDP is able to deliver targeted communications once triggered by consumer data signals. With the influx of messaging that users receive on a daily basis, hyper-personalized marketing really breaks through the clutter.

    • http://www.techrepublic.com/article/5-steps-to-turn-your-companys-data-into-profit/
      The article is talking about how to use five steps to help businesses use their data to get the biggest business impact. First, discovery: focusing on the nature of the problems or opportunities the organization is facing. Second, decision analysis: begins to structure the analytical problem a company is addressing. Third, monetization strategy: determine if its solution has value, and the impact on business performance. Next, agile analytics: using guided analytics and decision theory to create an end solution. Finally, enablement: making sure the data is valid, the calculations are correct, and the end users are engaged in the testing process. I found the article is useful, since many company are still struggling to turn the information into business outcomes. This is what we have to learn about.

    • https://www.theguardian.com/lifeandstyle/datablog/2017/feb/09/national-pizza-day-how-many-slices-do-americans-eat
      The article I chose talked about American’s consumption of pizza. It shows that pizza is one of the only types of food that American would consume on any given day no matter what gender or ethnicity group they are from. It is also found that more than half of the consumption is seeing pizza as a snack, the second up option is lunch, and people rarely have pizza as dinner or breakfast. They also show the comparison between the data of pizza consumption with drinks. Although it is thought that people would consume more soda than juice, it also depends on the consumer’s background as well. Although I wish there would be more data to support their claims, I find the topic interesting because it is relevant to my daily life.

    • http://www.zdnet.com/article/commonwealth-bank-targets-smes-with-new-big-data-analytics-platform/

      Since I am interested in banking, I thought this article about a bank creating a new data analytics tool for small to medium sized businesses to use was a great article. The goal of the new tool is to empower the SMEs (small-to-medium enterprises) to use these tools to their advantage. Another goal is to make data analysis tools easier to use and to not require too much training. They hope to give these smaller companies access to big data and the power to know how to use it.

    • https://fivethirtyeight.com/features/james-harden-gets-fouled-on-3s-more-than-any-nba-team/
      I found that James Harden gets more fouls on 3s more than any other team interesting. James Harden has drawn 108 fouls on 3 point attempts while the highest team has drawn 73 fouls. This just shows that James Harden is on another level when drawing fouls.

    • http://www.omaha.com/money/we-want-to-move-fast-facebook-s-new-data-center/article_0ac9beb4-1943-11e7-8206-ef22f010baa4.html
      Mark Zuckerberg said yesterday: we’re building our ninth data center in Papillion, Nebraska. That means facebook needs extra data storage. Facebook bought Instagram in 2012. Facebook now has a stable growth of users. I was thinking how facebook picks these place for data center. “Facebook currently has data centers in Prineville, Oregon; Forest City, North Carolina; Lulea, Sweden; and Altoona, Iowa. Construction on additional data centers is underway in Fort Worth, Texas; Clonee, Ireland; Los Lunas, New Mexico; and Odense, Denmark.”(omaha.com) So I guess doesn’t matter for data storage.

    • Where Bars Outnumber Grocery Stores

      This article has data visualizations that show which places in the US have more bars than grocery stores. I found it surprising that there are about 13% more grocery stores than bars, I expected there to be a lot more grocery stores than that. Some places, such as the entire state of Wisconsin, have an average of 2.7 times more bars than grocery stores. It seems that the more rural the area, the higher average of bars there are. This is most likely due to the fact that in a less populated area there is not a need for multiple grocery stores, but there will still be many bars.

    • Yesterday throughout social media and news sources I learned that it was Equal Pay Day, very interesting and a good social talk. Interesting enough I found an article named Equal Pay Day: a wage gap fact check, and the author Mona Chalabi investigates Equal Pay Day. She says, “That date is symbolic – it shows roughly how many days of 2017 women need to work to earn as much as men did in 2016. Tuesdays are symbolic too, they represent “how far into the next work week women must work to earn what men earned the previous week”. She also states that the wage gap is much larger than the 80 cents to the dollar myth and proves it with data.

      https://www.theguardian.com/us-news/datablog/2017/apr/04/equal-pay-day-us-wage-gap-gender-race-ethnicity

    • http://www.businessinsider.com/how-much-money-doctors-make-2017-4
      This article shows how much different types of doctors make a different amount of money. And also, if you didn’t know, race does play a role I the salary. Different races earn different amount of money. Such as in the graph it shows that the “white” doctors make the most out of all the races. This is related to me because, I actually might be switching my major because I want to make a lot of money after I graduate. And fro this article, I know how much doctors makes, and i know which type of doctor makes the most.

    • https://fivethirtyeight.com/features/americans-shift-to-the-suburbs-sped-up-last-year/
      As I get closer to graduation I certainly think about where it will be best to live after school. This article is very insightful and accurate about the location tendencies of individuals at various life stages, especially recent graduates, and in comparison, those who already have a career and possibly a famil. The data visualizations here show very large differences for suburban and urban living relative to scale.

    • http://www.ncaa.com/news/basketball-men/bracket-beat/2017-01-18/march-madness-brackets-how-often-we-pick-ncaa-tournament
      With the NCAA Tournament just ending on Monday I decided to try to find something that related to the tournament. In this case with so may people making brackets every year, I found an article that shows the percentage of people that pick each different seed, and the percentage of how often they get those picks right. I thought that it was very interesting to see which upsets that people tend to pick the most and least, and which upsets actually happen the most. It even talked about the average on how many seeds that are 10-15 on average make it past the first round every year.

    • A current news story right now is President Trump’s and Republican senators’ struggle to confirm Federal Judge Neil Gorsuch to the Supreme Court. I found an excellent line graph showing all the political ideologies of every justice in the highest court of the land since 1940. If a justice’s line is vertically above 0, it means they lean towards the right. If it’s below, they lean left.

    • https://blogs.spectator.co.uk/2017/01/oxfam-wont-tell-capitalism-poverty/
      Growing up I always had a niche for entrepreneurship and free enterprise, the overall idea of independent work sounded amazing. This realization made me want to dive deeper to better understand it and in turn I discovered something. Free enterprise has been saving billions of lives from poverty. Country’s all over the world have given the opportunity for people to change their everyday reality. In this article I found a better explanation on the impact free enterprise has on the world’s poor and how without it billions of people would have lost their lives. Today Free enterprise has brought the poverty levels down to 10 percent and even though there is a lot of work that needs to be done this achievement is worth celebration. Today people are finding new discovers and changes through time faster than ever all because of data.

    • Parental Time Use


      The article I chose displays average Parental Time Use. There are two different charts, one for mothers and one for fathers. The charts divide time into three sections; Childcare, Housework, and Paid Work. I found this particularly interesting because as I get older I wonder more and more what my future will be. If I will be a professional most of my life or a stay at home mom. I found it interesting how the amount of mothers with paid work has tripled in my lifetime.

    • Americans Widely Support Paid Family and Medical Leave, but Differ Over Specific Policies

      This article is extremely interesting to me because I am currently in the middle of trying to change the PA Family Medical Leave Act with my mom. We have been trying to include siblings and parents in to the Family Medical Leave agenda for almost 8 years now. We have struggled with the bill passing because most politicians only care about themselves and don’t want actual positive change. So, the more information I can get on citizen’s support of medical leave, the better of my cause will be.

    • http://uproonline.com/index.php/UJCT/article/view/20
      The article i post because it introduces the ensembles of distributed, heterogeneous resources. It has 4 main parts, 2 of them specifically introduce the data management and data integration in grid computing environment. This paper is of interest to distributing computing researchers because Grid computing provides new challenges that need to be addressed.

    • Big data and the creation of a self-fulfilling prophecy


      The article is about the self-fulfilling prophecy of big data. I liked the example of the police taking an eye on the neighborhoods that come to have higher crime rates. The problem is that data on crime is often biased because arrests are more likely to occur in neighborhoods that are “monitored more often”. This article says that the algorithms used for data analysis are somewhat oppressive, and might isolate groups that are already at society’s margins. I thought this was a negative side that we should really think about, considering how much data analysis means to our societies more and more.

    • Visual simulations to show Uber game strategies

      Very interesting to see that Uber uses very creative and smart ways to incentive drivers to work for a longer time and in certain areas.

    • https://www.fastcompany.com/3014911/top-10-iconic-data-graphics
      In this article there are some very good data mapping and infographics. Like windmap, gapminder, The Ebb and Flow Streamgraph, Paths to the White House, Death and Taxes, Gay Rights, State By State, Bikini Chart, A Peek Into Netflix Queues, Why Is Her Paycheck Smaller?, How Common is Your Birthday?. These were very interesting for me.

    • Americans and Cybersecurity

      Internet is our daily, almost everyone uses it to do each kind of things. Crime in the reality and so does the internet. Hackers may get our personal information if we don’t learn how do protect them. A majority of Americans (64%) have personally experienced a major data breach, and relatively large shares of the public lack trust in key institutions – especially the federal government and social media sites – to protect their personal information.

    • http://foodfitphilly.org/eat-healthy-on-a-budget/ I think this article is interesting because it gives you information on how to spend the most of your money. It supports local small-business corner store owners. the article shows what corner stores you can visit with what budget to get the healthiest food possible. With obesity rates so high, information like this can only help the local community.

  • Here are the assignment instructions.  Groups MUST be 5 members.  You may not do this assignment on your own or in smaller groups than 5.

    The assignment is due April 25, 2017. We’ll do the pres […]

  • Here is the study guide for the second exam.

  • Here is the exercise.

    And here is the Excel workbook you’ll need [Pew Story Data (Jan – May 2012).xlsx]

     

     

  • Here is the exercise.

    And here are the workbooks [2012 Presidential Election Results by District.xlsx and Portrait 113th Congress.xlsx]

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