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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Leave your response to the question below as a comment on this post by the beginning of class on November 30. It only needs to be three or four sentences.
What was the most important takeaway (from your p […]
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Here is the exercise.
And here is the spreadsheet you’ll need for the exercise [In-Class Exercise 12.2 – Sentiment Analysis Tools.xlsx].
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Here is the exercise.
And here is the spreadsheet you’ll need for the exercise [In-Class Exercise 12.2 – Sentiment Analysis Tools.xlsx].
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Some quick instructions:
You must complete the quiz by the start of class on November 28.
When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign […] -
Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Some quick instructions:
You must complete the quiz by the start of class on November 28.
When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign […] -
Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Here is the exercise.
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Here is the exercise.
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Laurel Miller wrote a new post on the site INDUSTRY EXPERIENCE IN MIS-FALL 2017 6 years, 9 months ago
What lessons did you learn during your internship that you just couldn’t learn in the classroom or from a textbook?
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I was really looking forward to working in a professional environment at Cross America. I knew that I would be able to expand my knowledge in business by actually being in a workplace/office opposed to a classroom or retail store. I realized that people working in my office were generally laid back and cool people. I think some classes paint the professional world as uptight and always formal, but this does not seem to always be the case. I learned to chat about everyday things with my superiors and co-workers in addition to discussing work. It was nice to actually be contributing to a company. Classes do not have a real world impact like working an internship does, and I liked that what I was doing mattered to the economy.
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The one thing that you can never learn unless you are in the work environment is that you don’t always get recognized for every effort you make. Unlike in the classroom where you always get a grade as a reward for your hard work, in a professional setting you don’t always get that recognition. Sometimes even mid-project, the whole thing is cancelled or whatever you worked on is not even used and is thrown in the trash. The lesson is to get used to it, know that your work is valuable and keep getting better at your job.
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Throughout my experience, I have learned that many tasks aren’t as simple with a direct answer. Many tasks required me to reach out to various members in order to retrieve data and get specific perspectives. These tasks are best performed through communication and working with others as a team. Next, we aren’t always given tasks we have done before, sometimes we are given new assignments that require us to learn new skills in order to achieve them. Next, time is another important thing to keep in mind, it is necessary to prioritize tasks depending on what is needed and when. Deadlines get pushed around a lot so you have to be ready to work on different projects at the same time or shifting between projects based on priority. Finally, the biggest difference between the textbook and actual experience is when you complete a project on the job it feels a lot better to accomplish knowing that you made a difference in a process or an actual outcome within a company.
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In my internship something I learned is to get questions answered sometimes I had to be persistent. To solve problems it was not as simple as walking into office hours or asking a question during class. I had to understand first who would be able to help me and what I specifically needed from them. In the first few weeks of my internship I would go to meetings and leave realizing no one really gave me what I wanted. So I learned to have specific questions ready and different way to ask these questions. Most employees do not care about interns, but throughout the summer I learned how to make people value my time so I could complete tasks. This persistence is not something I could have learned in the class room.
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Throughout my internship I have learned a lot of technical skills and soft skills that you cannot just learn in a classroom. Some of the technical skills I have learned are performing test execution audits and setting up databases in MS-Access. Audits would be very hard to teach effectively in class because its really something that you need to deal with real testers and situations for a period of time in order to learn. Creating databases in MS-Access you can learn to an extent in class but it was so much different doing it in the business world where many of your requirements are not ideal and theres no answer in a textbook to your specific problem, its a lot of learning on your own through trial and error. Some of the soft skills I have learned are effective communication, determining priority between multiple projects, and being a team player. For me working on the IT Governance team has provided an interesting team-environment where I work with QA, Change Management and Compliance daily and each group has different skill sets and needs. So determine how to communicate with each individual, who to help first and how to work everyone has been essential.
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Throughout my internship as a software developer I have learned different coding languages and have learned to research on the web. In school everything is set up and we learn php as our main language. In the beginning of the project I was in, people were debating which language to develop in. Our team decided to code in a new rising coding language. I also became a better developer by learning how to research on the web. I have learned which key word to use when looking for different pre-written code. Also getting the experience in the working environment was something I was not able to pick up at school. Working in an open area with co workers right next to me was not a setting I have worked in.
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The main skills I developed over my internship that weren’t learned in the classroom dealt with professional communication, and more specifically email etiquette. At Pfizer, the majority of people I interacted with were at least twice as old as me, so there was certainly a learning curve on how to effectively communicate with them in a professional manner. When I first started there, I would spend a lot of time editing and revising my emails in order to come off professionally and while it seemed tedious at first, it truly paid off. My sponsor and other colleagues took note of my email skills, and would frequently come to me to distribute large emails to members of the organization. While this may seem like a small thing, it was rewarding that higher-ups on my team noticed the effort I put in and this ultimately helped me get my name out there at Pfizer.
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One of the many things I learned at my internship over the summer that I could not learn in the classroom is the importance of asking questions. During meetings with stakeholders or project sponsors I learned it was vital to ask questions to really dig down to the core of the problem or issue at hand. I also learned the importance of building relationships with those you work with. This is important so whenever you come to a roadblock or need help you can reach out to those you’ve built these relationships with, and they can help you with any issues. These two key learnings were vital throughout my internship at BD.
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Laurel Miller wrote a new post on the site INDUSTRY EXPERIENCE IN MIS-FALL 2017 6 years, 9 months ago
Just a reminder that the PowerPoint draft is due tomorrow.
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Leave your response as a comment on this post by the beginning of class on November 16. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, n […]
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Facebook is the online data-driven service that I use most frequently. For Facebook, each row of the spreadsheet would represent a different person. The first column could show the first name of a person; the second column shows the middle name, and the third column shows the last name.. That way you could count how many people have similar names. Another column could show the number of friends a person has while the next column could show the number of photos the person has. Other potential columns include: number of posts, number of private messages sent/received, number of liked pages, high school name, college name, etc.
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One data-driven service that I use quite frequently is Instagram. The rows would represent different Instagram accounts. Some of the columns would include: the person’s user name, the number of pictures, the number of followers, the number of people followed, average likes per picture, and the number of tagged photos.
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One data-driven service that I use at work is HEDWIG. It is a service used by residence halls to log packages for students. One row could represent a student’s name or a package. The information in the columns would include a student’s TUID and room number. For the package the columns would include the carrier, where it’s shipping from, the date it was delivered, and the bar code we need to scan.
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The data-driven service I use the most regularly is Blackboard. If I want to store the data in the spreadsheet,
1st row: Name of the Subject and subject code
2nd row: Name of Professor
3rd row: The name of the file(assignment) I uploaded on the blackboard
4th row: The date I uploaded my file
5th row: the score of each assignment
The other rows: The content of the assignment, The magnitude of the assignment(how important it is in %), The type of the file(ppt, word, pdf)
By using this spreadsheet, I can know how many files I uploaded in a certain period(1 day, 1 week, 1month), the average of my score, which subject has the most assignments and so on. -
I use GroupMe, a group message app, very often. It would be interesting to break down everyone’s individual groupme data. One column would be name of the person, one would be how many groups they are a part of, one could be how many messages they send on average each day.
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A data-driven service I use frequently is something called coriscahockey. Corsica hockey is a data base that shows detailed advance hockey statistics. A spreadsheet that would contain the data of this service would have rows such as: player name, player position, player team, and each individual row for different player statistics.
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A data-driven service I use regularly is an app that logs my study hours. Each row could represent a week while the columns would include avg. study hours per day, study location, and how many hours left until the user reaches his or her weekly goal for hours studying.
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One data driven service that I use regularly is blackboard. A row on a spreadsheet could represent each course that I’m taking. The different columns in the spreadsheet could contain information about the professor of the course, the data and time of the class, and different assignments for the class, as well as my grades on those assignments.
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A data driven service that I use regularly is Apple Music. A row in the spreadsheet could represent each artist in Apple Music. The columns in the spreadsheet can be how many songs, how many albums, genre(s), rankings (ie #3 on the pop charts) and also how many total streams they have gotten.
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I use Spotify everyday. If I wanted to analyze the data derived from this app, I would be most inclined to study the artists I listen to most often. Thus, each row could represent my 10 most listened-to artists. A column could be the song or album of that artist I listen to the most.
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A data driven site/app that I use almost everyday is Apple music. If this data was transformed into a spreadsheet it would include things like genre, name of playlist, artist in the columns. Then in the rows it would include the name of the song.
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Instagram is a data driven app that I use everyday. If the data was transformed into a spreadsheet it would be based off each user’s page. The row would be each Instagram user’s page and the columns would be based off the number of followers the page has, the posts of the user, as well as the likes and comments they get on each post.
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I use Blackboard everyday as a data driven app. The data if made into a spreadsheet would have my classes, teachers, and grade in the columns. In the rows would be the full name of the class, who teachers that class, and the specific grade I have in that class.
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A data-driven app I use on a regular basis is SnapChat. The row would contain the individual’s username, in the columns there would be the number of snaps they send in a day, how many streaks do they have, how many snap points they have, the number of snaps on their snap story, and the number of users they have on their “Best Friends” list.
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A data-driven app I use everyday is Spotify. If Spotify’s data were to be transformed into a spreadsheet, it would report song titles, artists, albums, and the type of genre the song falls under. The row could represent its rating in your playlist or your most recent songs listened too.
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A data-driven source I use every day is my Scottrade brokerage account. I use Scottrade to invest in many different stocks as well as researching other stock quotes for future stock purchases. Some columns I use in my excel spreadsheet to track my investments are stock price, stock quantity, gain/loss, dividend distributions per share, and total gain/loss. Each row lists the company’s name and ticker symbol.
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I frequently use Spotify. A good way to organize a Spotify spreadsheet would be to make each row its own song. Columns would each be details about the song. For example, these could be things like artist, featured artist, producer, name of the song, duration, release date, album, genre, number of plays, stream or download etc.
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Canvas is a data driven source that I use often. If it were in a spreadsheet, the first column could list the class, and the following columns could contain other information pertinent to each one. For example, due dates, grades, and assignments could all be listed in the following columns. This would be a great way of organizing an academic schedule.
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A data driven service that I frequently use is Amazon. If I wanted to analyze Amazon data in a csv format, I would have the product name, product number, price list, reviews using 1-5 number. From this data, I would be able to analyze if certain products are most frequently purchased by customers.
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A data driven service I would use would be Twitter. My data I would be storing would be, the time majority people tweet on the East Coast, the average likes or retweets they receive on post, the area in which the tweet the most. From this data I would be able to determine which States have the most twitter activity.
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A data driven service that I would like to see in a spreadsheet would be Amazon. I would have rows for the product name, product description, and company that makes it. For columns I would like to see the price of the product, the SKU number, average star rating of the total reviews, and number of times that the product has been purchased. By having it in this format, I believe that the information would be keep in a clean matter, allowing the data to be easy to follow.
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I use GrubHub service often times and I believe if I’d store their massive data in a spreadsheet would be:
Column 1: restaurant ID
Column 2: User ID
Column 3: Restaurant Name
Column 4: Restaurant Location by Zipcode
Column 5: Dynamic User ID Location
Column 6: OrderIDAll the rows would be values of each specific column.
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A data service I use regularly is Twitter. A spreadsheet for Twitter would include rows for the profile picture, tweets, likes, retweets, followers, and columns would include the number of tweets, likes, retweets and followers.
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I use the ESPN app often to check scores of games which I’m interested in and check out specific stats of players I like. To make a spreadsheet I could make rows of the teams, players, positions, etc. and columns filled with things such as points per game, blocks, points allowed, passing yards, etc.. You could filter this by being able to minimize to specific positions of whichever league you’re looking at or on a certain team as well.
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Leave your response as a comment on this post by the beginning of class on November 16. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, n […]
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Twitter is a data driven service used daily by millions of people. Twitter stores posted tweets. A tweet would be a row in the spreadsheet. The columns in the spreadsheet would be the name of the person, the date, the time, and the number of characters.
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Uber is a data driven service that many people use regularly. Uber stores uber rides. In a spreadsheet, a row would be an individual Uber ride and the columns would be how long the Uber ride was, the distance, the traffic involved, and the number of people riding.
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Canvas is a data driven service used daily by Temple students. Each row in the spreadsheet would represent classes the student is enrolled in. Some of the data columns would be syllabus, schedule, grades, assignments, and announcements.
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Google Drive is a file storage service developed by Google. Google Drive stores all kinds of files including Word documents and Powerpoints and allows users to collaborate on documents. A row in Google Drive’s database would be information on an individual file. Columns would include metadata on the file including the upload date, contributors, name, last edit date, size, and the actual data included in the file.
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The grade center in Blackboard records the grades earned by each student. If put in to an Excel spreadsheet, the row would be the grade received, and the columns would include what class the grade was for, how heavily it is weighted overall, what kind of grade it was (quiz, exam, homework, etc.), who the professor of the class is, and date the grade was posted.
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The data driven service I use everyday would have to be Canvas. If I were to make an Excel spreadsheet for the information that is stored on Canvas the rows would be all my different classes. The columns would represent professor name, my grades for each class, upcoming assignments, assignments completed, and dates for exams/projects.
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Amazon stores information about different products for consumers to search and choose from. If the information was to go on a spreadsheet, the columns would be: Product name, Category, Price, Seller, Rating, Number Purchased.
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A data driven service I use everyday would be snapchat since it’s used by millions of users every second. If I were to store the data for snapchat in a spreadsheet what each column in the spreadsheet represent would be the time I posted my snapchat story, how many people viewed it, and how many responded to it. The rows would be individual users I sent a snap too or users I got a snapchat from.
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A data driven source I used regularly is Twitter. If data from Twitter were put into a spreadsheet, a row would be an individual’s Twitter handle, and the columns would be things like the individual’s total number of tweets, number of tweets on a specific day (or over a period of time), number of retweets, number of likes, number of media posted, number of followers, number of people the individual is following. Or, a row would be an individual tweet, and the columns could be the Twitter handle of the person who made the tweet, the number of characters, the time and date posted, how many likes and retweets the tweet got, and how many times the tweet was viewed.
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Amazon is a data driven source used regularly by millions of people. If i were to use it and put it in a spreadsheet, some of the columns of data ratings would include product name, average rating, price, manufacturer and category. I could also put average shipping and handling if that is done through the individual seller or amazon and is a fixed price.
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Instagram is a website and app for sharing photos and videos. One row could be for pictures and another row could be for videos posted. The columns could be how many likes, comments, and the date it was posted.
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UberEats is an app I use, which is a data driven source. Rows would be location, restaurant, name of food, order time, delivery, and delivery driver. Columns would likes, dislikes, comments, and date.
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YouTube is a website/app that millions of people use everyday to view videos about all different kinds of things. In Excel, a row would be a specific video and the columns could be the name of the video, the date posted, the genre of the video (music, sports, travel, etc.), the length of the video, the number of views it has, the number of likes/comments it has, whether the video has ads on it, and the name of the account posting it.
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Amazon is an app I am constantly using to buy things on the go. I would put the items bought in the rows and in the columns I would put information such as: price, amount of time the item remained in the buyers cart, date of purchase, the delivery time, information on whether or not the buyer wrote a review, and if the item was kept or returned.
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One of a data-driven service is the Linkedin, a professional social networking site for business community. Some of the column would be the LinkedinID, Experience, Skills, Accomplishments, Place of work/ Study, and Contact Information. The rows would be information of each person that create a profile in Linkedin.
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Amazon is a service with inordinate amounts of data behind the scenes. I think if I had the data for Amazon it would likely include product names, price, customer rating, a short description, and seller. If the data was made available things like number of sales and number of returns or complaints would be extremely helpful to amazon and its sellers; with that information they could fix issues with their products and bolster their sales numbers.
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An example of a data-driven service that I use regularly would be Amazon. If I were to store the data (of my purchases) for that service in a spreadsheet, the information would be formatted by rows = list of products and columns = product name, order date, shipped date, delivered date, delivery location, price, and average customer rating. We could also add a column titled “type of product.” This would be a cool way to analyze my shopping patterns of either house supplies, kitchen supplies, office supplies, etc.
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A data driven service I use everyday is my Citi credit card application and the Citi credit card online banking. If I wanted to integrate the data into Microsoft Excel, the rows would be categories of the purchases. For example, the rows would be named entertainment, merchandise, restaurants, organizations, and vehicle services. On the other hands the columns would represent the time period such as November 2017, October 2017, September 1017, and so on. The cells would be the total amount of money spent on the purchase category in accordance with the time period accrued.
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Netflix is a data-driven service that I use often. If I wanted to create a spreadsheet to store data each row would be the name of a movie/show and some examples of the columns would include genre, length, rating, actors, director, and Netflix category.
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Snapchat is an example of a data-driven service that I use every single day. If I created a spreadsheet to show Snapchat’s data, I would make the rows be individual users. For the columns, I would put their snap score, how many snapchats they send per day, what filters they used, and how long their streaks are with other people/their highest snap streak.
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Instagram is a data-driven service that I use everyday. If I wanted to store information into an Excel spreadsheet, I could do multiple things. One spreadsheet could include making the rows, the individual account’s username. In the columns, I could put the number of followers the account has, the number they are following, how many pictures have they posted, how many likes have they received, the average number of likes per a photo, and number of photos they have liked.
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One data- driven service is black board. If I put blackboard’s data into a spreadsheet I would have many different columns. One would be courses so that the data in the other columns can be organized in rows by course. Then it would be assignments and grades. All the data would be filtered by courses.
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rotten tomatoes stores movie reviews. the row will be short reviews, and columns will be the name of the movies, how many stars the movie got (0-5), the year of the movie produced, names of the director and main actors, which type the movie is, and who writes the reviews.
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A data driven service that I use on a regular basis would be YouTube. Some examples of columns could be the YouTuber’s username, genre of video, and what is trending. The rows would include the information of these categories or a potential link to any given video.
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An example of a data-driven service I use regularly is my bank account. And the spreadsheet would have different columns. That is my spendings like cash and different categories like food, entertainment, and transportation.
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A data driven service that I use regularly is Facebook. On a spread sheet, each individual row would probably be a facebook page. Each column would then be an attribute of that facebook page. There are probably over 100 collumns per facebook page, however some of the columns would be, an Indentification number, the Page name, page type, Date created, Login email, login password, gender, description, etc.
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One data driven service I use regularly is Amazon. This online retail site keeps record of every purchase I make. In a spread sheet the item I purchase would be in rows and the columns would include price, order number, seller information, and my method of payment.
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I use Blackboard just about everyday, even if it is just to check if any grades have been entered, which they usually haven’t. If it were to be converted to excel, some rows could be grades, upcoming assignments and and the email address for the professor. The columns in this sheet would be the classes themselves, and with those rows showing everything about the classes that is necessary.
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A data driven app that I use regularly is Serato, my personal database for music. If I were to make an Excel spreadsheet for the information that is stored on Serato the rows would be the different crates I’ve made. The columns would be for the song name, BPM (beats per measure), artist, album, and the year that the song came out.
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A data-driven service that I use regularly is blackboard. The rows would represent each individual class. There could be a lot of columns. One could be the professor. Another would be the name of the course. The CRN for the course could be used as the id, or you could just assign an id to class. You could also have a column for my grade in the course. Lastly, there could be a column that is just a description of the course.
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Ebay is a data driven source used for online shopping. Columns would be product type/category, price, sales, etc and rows would be product names, quantitative data, etc. Buying behavior and trends can be identified from the data to gain key marketing data for better overall market insights. This can include prediction of trends, pricing, promotion, etc.
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A data driven service I use frequently is Amazon for online shopping. The row would be my my order number. And some columns could be the items I ordered, the price I paid, method of payment, shipping address, company the product is coming from.
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A data-driven source that I use almost every day is Amazon. I buy multiple items from this company a week. if I was to put my order history into a spreadsheet I would put the products I bought into the rows and for the columns, I would put in details such as price, customer rating, and the category the product falls in and its shipping and arrival dates.
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I use Twitter a lot and if the data used from Twitter was placed into a spread sheet the rows would most likely consist of trending topics, tweets per day, number of total tweets, and most used hashtags.
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Twitter is a data driven service that I use regularly. If I put twitter data into a spreadsheet, one row could represent tweets that I compose myself. Another row could be tweets that I retweet. Some columns could be the time the tweet was composed, the date the tweet was composed, the number of likes that the tweet received, the number of retweets the tweet received, the amount of comments and some of the most popular ones.
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Data Driven Service – Gmail
The columns will store the data like categories
Example:: NAME OF SENDER SUBJECT
The rows will have a more detailed fields following the column so for example
NAME OF SENDER. Subject
Joe MIS weekly questions -
Data Driven service : Stats.nba
In an excel spreadsheet, the NBA stats per player would be listed. Player/Team names would be in columns and the individual statistics would be in rows. Statistics used in our previous in class exercise for NCCA such as field goal percentage, etc. An additional row that could be added that wasn’t included in the in class exercise could be forecasts for players in future games. -
Instagram is a data driven service that I use everyday. I could have the rows be the usernames. The columns could include things like number of followers, posts, likes, and comments.
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DARS is a data-driven service that I regularly visit. Rows could be individual courses, while the columns would consist of all of the descriptive information and student data; these would include fields such as Course Location, Semester/Year, Subject, Final Grade, Grade Point Earned, Number of Attempts.
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
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]
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
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]
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Some quick instructions:
You must complete the quiz by the start of class on November 14.
When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign in. […] -
Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Some quick instructions:
You must complete the quiz by the start of class on November 14.
When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign in. […] -
Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Here is the exercise.
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 9 months ago
Here is the exercise.
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 10 months ago
Leave your response as a comment on this post by the beginning of class on November 16.
Leave a post about your group project:
What is the subject of your group project?
Which of your fellow […]-
1. The subject of our group project is “University Data Analysis”.
We feel data from universities is easily accessible because these businesses tend to brag a lot with statistics such as job placement rates, acceptance rates, retention rates, etc. Therefore, an in-depth analysis of such data will be possible and effective.2. Group members: Dan Moy, Anne Lin, Jeevan Jaganath, Henry Fok
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1.) The subject of our group project is about how women’s and men’s professional soccer varies in popularity and why this is the case.
2.) Group members: Caroline Doyle, Graceanne LeNoir, Tom Hess, and Alexa Lessard. -
The subject of our project is looking at NBA allstars, like over the past few years, and looking at those allstars measurement at there rookie predraft combine and seeing if certain factors in measurements can predetermine an all star, or at least boost their chances.
Group Members: Myself (Julien Bombara), Sayem Rahman, Kaoi Peixoto, and Nicholas Napolitan
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1. Relationship between police positioning with the crime rate
2. Kendall Jones, Devisha Walia, Nalah Clark -
1. Our subject will be a comparison between the 2008 Philadelphia Phillies and 2008 Tampa Bay Rays
2. Joseph Benvenuto, Nick Tarducci, Joseph Rovnan, Ryan Stifnell and Will McAndrews -
1. Are NFL pocket quarterbacks more successful than NFL running quarterbacks?
2. Alex Brennan, Zachary Kressler, Robert Snyder, Jonathan Ganon -
1. Social media trends over time
2. Brianna Morales, Michael Vidra, Grace Stuart, Mariah Tulaney, Hussain Alshola -
1. Our subject is Health/Wellness across different neighborhoods in Philadelphia
2. Kacie Rettew, Cassidy Lorenz, Dalton Veight, Nick Thull -
1. Our subject will be the comparison between the different type of crimes people are jailed for in Philadelphia.
2. Jose Gil, Chongxin Zhao, Amanda Barnabie, Dmitriy Summovskiy, Justin Waters. -
1. Our subject is the success of different Philadelphia stadiums.
2. Briana Vetter, Jillian Thompson, Glen Diener, Josh Ogg -
1. For our subject we will be looking at the Indego bike share trips and comparing the usage of the bikes in the summer months compared to the winter months.
2. Danielle Redman, Summer Holmes, Xiaoyu Liang, Hasan Husain -
1. Our subject is comparing the in-game performance of the Cleveland cavaliers vs the golden state warriors.
2. Zeke Menke, Bob McPeake, Nadir Munir, Joseph Pavelick
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 10 months ago
Leave your response as a comment on this post by the beginning of class on November 16.
Leave a post about your group project:
What is the subject of your group project?
Which of your fellow […]-
Subject:
Airbnb Data Set
We are going to analyze data about Airbnb’s in different cities and compare the results of the cities each of us choose. Each person will choose one city as there are thousands of rows of data per city.Group Members:
Nicholas Griffaton
Zach Delphais
Kate Sliwinski -
Subject: Which Big 5 Philly School has the Safest Campus?
Members: Vanessa Ioannidi, Zach Burkhardt, Jacqueline McCarrik, and Jeffrey Angstadt -
Subject: We will be focusing on the amount of crime that occurs around schools. We will compare the same types of crimes, and look at the different factors that affect the crime rate.
Members: Raghad Ayoub, Rami Aboud, William Wrobel, Luke Reynolds -
Subject: Complaints against the Philadelphia Police Department. We are going to analyze the lack of service, abuse, and unprofessional conduct of the department. We will also be analyzing each summary of the event that was filed.
Members: Kieragh McMenamin, Jessie Turner, Bennett Sigourney, Preston Monah, Adam Scalfani.
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Subject: We are going to answer the question: Who is the best statistical NFL player to play the quarterback position. We will compare numerous key statistics, such as: QBR, passing yards, interceptions, etc. to answer this question.
Members: James Villani, Brandon Weiss, Noah Clay, and Shabbir Lageli
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1. TED Talks datasets, we’re analyzing the most viewed and most favorited Talks of all time? the TED main dataset contains information about all talks including number of views, number of comments, descriptions, speakers and titles dating from 2006 to September 21, 2017.
2. Members:
Jasmine Rogers
Bosung Park
Phoebe Davidson
Hansel Tan
Devon Hill -
Subject: Black Friday deals vs. Cyber Monday deals
Members:Tracy Glova, Kayla Cohen, Courtney Wilson, Kourtney Thompson
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Laurel Miller wrote a new post on the site MIS 0855: DATA SCIENCE FALL 2017 6 years, 10 months ago
Leave your response as a comment on this post by the beginning of class on November 9. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]
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Article: https://newsroom.intel.com/editorials/intel-power-5g-network-2018-olympic-games/
Explanation: I chose this article because it is about the 2018 Winter Olympics. I always watch both the winter and summer Olympics, so this information is very interesting to me. In the article, the author talks about how they are creating a “foundation for a massive new wave of connected devices and data” as Intel is collaborating with South Korea’s KT Corporation in order to share their advancements with 5G technologies.
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I found this article very interesting. The author performs an analysis on trends related to occupations in terms of gender. The data is eye opening and shows the progression society has made within gender roles. -
This article is interesting to me because it discusses Trump’s tax bill and I am an accounting major and am doing a project that has to deal with the tax reform. Only 25% of people believe the tax plan is a good idea, but that is pretty good. The 25% of people that think it is a good idea probably know about taxes while the rest may not know or just do not approve of Trump. Also 39% of Americans did not form an opinion yet.
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https://www.theguardian.com/environment/2017/sep/26/national-park-plastics-bottled-water-ban
I found a really interesting article pertaining to the environment and how Trump’s reversal of the ban of water bottles in national parks will continue to hurt the environment. There are 331 million people that go and visit the US national parks each year and there are one million plastic bottles sold per minute. That is a lot of people who will come and want to bring water bottled. Because of this idea, the national parks banned the use of water bottles, which saved them 2 million water bottles, which is the equivalent to 419 cubic yards of landfill space. With all of this great information found, Trump reversed the ban 3 months later, leaving the national parks and the environment vulnerable. -
This article is about the recent mobile data leak of 46 million users in Malaysia. The leaked data includes personal user information, ID card numbers, mobile phone numbers, SIM card information, and addresses. The main threat of this data leak is for the affected customers to have social engineering attacks against them where further information could be leaked, or the information could be enough for the criminals to crate fraudulent identities to make online purchases. This is particularly interesting to me as it relates to cyber-security, a field I may decide to work in in the future.
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An article I recently read was: https://www.theguardian.com/news/datablog/2017/oct/31/terror-trends-what-are-the-most-common-phobias-among-us-adults. I thought it was only fitting to include it here since Halloween was just a week ago. The article presents data about the most common phobias among adults. As an advertising student, I found this article quite interesting because of the demographical insights they were able to pull from the survey. These insights included “being female, young, and low income increased the risk of developing any of the phobias, while being Asian or Hispanic decreased risk.” I’m easily fascinated by how the mind works and I liked how they were able to gather these conclusions off of a simple survey. Insights like these are what drive successful advertising campaigns.
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http://fivethirtyeight.com/features/the-ultimate-halloween-candy-power-ranking/
I found this article, “The Ultimate Halloween Candy Power Ranking” to be very interesting and relevant because Halloween was a few days ago. I thought it was interesting that the collectors of the data split what makes candy most desirable into 9 categories (i.e. chocolate, fruity, contains peanuts, etc.). I thought this was a very cool article to read because it shows that data really is everywhere, and even something as simple as what Halloween candy is the most popular can be analyzed to figure out exactly why people find certain candies more appealing than others. -
I think this article is very interesting and relevant to today. Its about the mass shooting problem in America, that seem to never end. In 2017 there were no more than five days without a mass shooting. The data shows that each circle, sized by total deaths and injuries, represents a mass shooting. A mass shooting is defined as when at least four people are injured or killed in one location, not including the suspect with a firearm. This is an ongoing problem in America because of all the gun control debates, but its deeper than that. This kind of stuff doesn’t happen in other developed Nations like in Western Europe, Asia, and Australia, the problem is America has an obsession with guns. Gun control won’t end it until the culture of the obsession with guns ends.
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This article shows is an interactive graph, showing the percentages of people who are married at a certain age, vs those who aren’t married at that age. They also break it down by race, which I thought was interesting because in todays day and age there are a lot of mixed relationships/ marriages. The graph would have been better if it allowed you to mix and match races as well as genders, considering the world we live in now. Other than that I found the article to be interesting and informative to an extent. -
One thing that I also shared with Elon Musk that we are not fascinated with the advancement of Artificial Intelligence(AI). For me, human should always be the one that rule the earth. If, A.I managed to take advantage on the all available data on earth about us, together with their super analytics capabilities, doomsday might be looming.
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I find this article extremely interesting because if this tax bill is passed, it would be the biggest reconstruction of the tax system since the 1980’s. This article goes in depth on the polling numbers in regards of how people view this potential bill passing into legislation. Further in the article they show the approval ratings of President Trump, and also the Democratic advantage over the republicans on the generic congressional ballot which i thought both were very interesting.
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https://sports.yahoo.com/eagles-now-4th-best-odds-192411317.html
My article has to do with super bowl odds and MVP odds. Right now the Eagles have the 4th best odds at winning the super bowl behind the Patriots, Seahawks, and Packers. Carson Wentz also has high odds of winning MVP this season behind Alex Smith and Tom Brady. I love the Eagles so that is why this data is relevant to me. -
https://www.engadget.com/2017/11/07/mobile-phone-data-could-replace-census-questionnaires-in-the-uk/
This article talks about how in the UK, the use of collecting mobile phone data could replace the census questionnaire. The Office of National Statistics collects census reports through paper questionnaires but are also looking into mobile phone data. I think this is very interesting because this is a new way of collecting data. I see a problem with using phone data as the collection process because not everyone has a phone or some people have multiple phones (one for work and one personal). -
I chose this article of Biometrics in sports because it is interesting and also similar to the fitbit/insurance reading we did in class. In summary, sports are using the app Whoop, which measures heart rate, sweat, and other factors, that show potential exhaustion in sports. This is a blessing and a curse for sports, because while it can warn of potential dangers, it also limits the amount of playing time for a player, which can cause the star players being forced to come out because of “warning” from the app even if the player wants to stay in, he will be forced out by coaches.
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TV Ratings: ‘Sunday Night Football’ Hits Season Low
As someone who is interested in the sports field, a KPI of how popular the sport is, can be the television ratings that the nationally televised games bring in. I also wrote a paper on declining NFL ratings last year when this trend started, so keeping up to date with the ratings has become somewhat of a hobby of mine. This article pointed out that the most recent Sunday Night Football game hit a season low in ratings, if these trends continue, it could create the panic that the networks had last year. -
From this article and data set, I found it interesting that the 3 activities those who are employed so with their time is sleep, work, and socializing/leisure/relaxing time. Those who are unemployed spend most of the time sleeping, socializing/leisure/relaxing, and household activities.
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Diagnosing the Internet of Things: Predictive Maintenance Through Analytics
This article talks about data analytics and the internet of things. The internet of things is a system where every device that’s part of the business is connected to the internet. One of companies they talk about in the article is Augury. Augury has 35000 devices and growing that institutes the internet of things. -
https://www.cnbc.com/2017/10/27/bitcoins-origin-story-remains-shrouded-in-mystery-heres-why-it-matters.html
This article was interesting to me because the creator of bitcoin’s identity is still unknown and the creator essentially owns 5% of the 21 million finite supply of bitcoin. For comparison purposes, the united states holds the most gold reserves in the world at about 8,000 tons, which is a little over 4% while the creator of bitcoin owns 5% of the bitcoin supply valued at around over 5 billion. In other words, the creator of bitcoin has more currency dilution power than the united states has over gold. Dumping more currency in circulation essentially decreases its value and having this unknown identity that owns about 5% of a rapidly appreciation currency can be troublesome in the short term if this person were to dump their stash of bitcoins on the market. -
https://www.theguardian.com/news/datablog/2017/oct/31/terror-trends-what-are-the-most-common-phobias-among-us-adults
This article was about the most common phobias among our population. the sample size for the study was 43,093 people. the data was displayed in a table, and also displayed in a graph that is shaped like a mountain because a fear of heights is one of the phobias. The graph showed just the 5 most prevalent ones. I thought this article was interesting because the graph was very appealing, so I thought they did a good job with that display of data. I also thought it was interesting because most people have weird things that creep them out, so it was pretty cool to see some of the most common ones. -
This article has an extremely interesting take on the Equifax data breach, the dives into why it happened in the first place. I’ve always known that when you submit information for something free in return you are the product so it doesn’t surprise me that Equifax has been making a profit off of all the consumer data they collected through credit karma. What does interest me is that Verison and Yahoo are defending Equifax’s rights to own the data, while this might legally be true I’m concerned about what precedent it sets for future businesses.
If they own are data does this mean they have a right to decide whether or not they want to protect it from potential hacks?
For example, if your identity was stolen from this data breach, does this mean you can’t hold Equifax accountable for not protecting the data you gave voluntarily? Does we forfeit the rights to our own data when we are given something free in return? -
This article is about how people can use social media platforms like instagram to make money. The article suggests that people with 3,000 to 10,000 followers can make around 250 US Dollars for one branded post. I found this interesting because I use social media everyday, and while I do not have this many followers on Instagram or Twitter, it is funny to think that having followers can become a career for some people.
http://www.businessinsider.com/inkifi-website-tells-you-how-much-your-instagram-posts-are-worth-2017-11 -
According to a survey of over 40,000 Americans, the most common phobias are animals (birds, insects, and others) and heights (mountains, tall buildings, bridges). The researchers found that people most likely to have phobias are young, low-income females and that the average age for developing a phobia is between 9 and 10. Some of the other most common phobias include being in closed spaces, being in/on water, thunderstorms, and traveling.
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This is a pretty cute chart that is mapping happiness. It shows each state and a smiley face or frowning smiley face. The bigger the smile the happier those people are living in those states. If the face is frowning, most people in that state are more likely to report worse physical and financial health. These people are more likely to be obese, smoke, or have little interest in life.People living in PA are moderately happy. Some of the happiest people come from Texas, Hawaii, Arizona, and Maine. A lot of unhappy people live in Mississippi, Ohio, and Rhode Island. -
http://www.propertycasualty360.com/2017/10/26/wearable-technology-discoverable-data
I found this article interesting because it covers how wearable technologies such as smart watches and fitbits can provide data for new uses. For example, in a Canadian court case the claimant provided evidence of requiring disability benefits by presenting data from their fitbit proving they have insomnia. This is interesting because this is a unique way to prove existing health conditions. -
https://fivethirtyeight.com/features/can-an-nhl-player-finally-score-50-goals-in-50-games-again/
This article is about how there are only 5 players in the entire NHL history that have scored 50 goals in the first 50 games of the season, and the last to do it were 25 years ago. However, due to data on previous seasons, looking at trends, there’s reason to believe that this year may be the year to break the 25 year curse. Those reasons being several rule changes to hopefully boost the number of goals per game, as well as a low among goaltending that’s the lowest it’s been in almost 10 years. Goalies are just not covering the goals as well this year as previous years. Not to mention superior players that are capable of the milestone, based on their stats thus far. All of this has the potential to break new records, something that the last 25 years of hockey has been missing for decades. -
This article was written shortly after the tragic shooting at the Texas church. This is a scatter plot that portrays the number of mass shooting, as well as the severity of mass shootings since 1984. What makes this scatter plot effective is that the thickness of each plot represents the severity of the shooting. Therefore, the bigger the shooting, the larger the size of the plot is. The graph shows that mass shootings are becoming both more common and more severe as time goes onI think this is very relevant because it was a tragic event and need to be aware of one of the biggest dangers in this country which is mass shooting.
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https://fivethirtyeight.com/features/can-an-nhl-player-finally-score-50-goals-in-50-games-again/
I found this article to be interesting to me because the main question it focus’ on, “Can an NHL Players score 50 goals in 50 games?” I am a big hockey fan so this article caught my eye instantly. The site shows the reader graphs about the top scorers throughout the years and how many goals they got. It is a very hard thing to do and it gives a challenge for the players. Gives detail about the players and compare stats throughout the article. -
http://www.newsweek.com/russia-checking-facebook-stores-data-countrys-servers-banning-social-network-705709
I found this article interesting because it deals with the rights of a private company versus the rights of a sovereign nation. Russia wants the company to store data of Russian citizens on servers in Russia rather than outside the country and are currently checking to see if Facebook is compliant with Russian law if Facebook isnt then Russia will ban the website in the country. -
I found this article really interesting which it talks about the driverless Ubers, so more and more cars without drivers will pick people up and take them wherever they want to go.
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This article is interesting because it shows how different groups of people spend their time throughout the day. It shows that a majority of females who aren’t look for employment spend their weekdays sleeping from 10pm-8am, doing household activities from 8am-12pm or 4pm-5pm, and socializing, relaxing, and leisure from 1pm-10pm. On the other hand, males who aren’t looking for employment don’t focus on household activities as much as females and spend more time on socializing and leisure activities. It’s interesting to see the variation of things people do through the day depending on whether its a weekday or weekend, if they are male or female, and if the are employed, unemployed, or not looking. -
Article: http://flowingdata.com/2017/10/19/american-daily-routine/
This data visualization focuses on different types of activities people do, what time they do it, and the percentage of the activity during all hours of the day. When i first opened the article, i had assumed that it was going to be a little bit more trickier than it was to read and comprehend, but it turns out the data was represented very clearly. All of the activities in the chart are listed on the left side, while the bars are represented in different levels of color to indicate the percentage. Overall, i really like this visualization. I have never seen information represented the way this one is laid out but I think it is very clear. The only thing about it that could be improved is to add more activities to it.
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http://results.regattatiming.com/backoffice/webpages/staticRaceResults.jsp?raceId=429 This is the results page from the Princeton Chase regatta about two weeks ago. This is important data to me because my team and I look at this data to see how we compared to other teams that showed up and how teams performed this year versus last year. Race results are always important to look at because we can pull a lot of statistics and data from them.
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https://www.cnbc.com/2017/11/08/microsoft-starts-using-linkedin-data-to-help-you-get-a-better-job.html
I think this article is interesting and relevant to me because I use LinkedIn on a daily basis, and for one, I was never aware that Microsoft owns LinkedIn. I also didn’t know that Microsoft has a program called The Resume Assistant, which could be very helpful to me. I think it’s cool that Microsoft uses data from LinkedIn and helps people see different descriptions of the job they’re looking for and the skills necessary for it, too. -
Article: http://www.dataversity.net/data-analytics-education-advances-villanova/
This article interested me because it discusses a new program that Villanova University is implementing in the Fall of 2018 to help its accounting students obtain the CPA 150 credit requirement. As an accounting major this is really relevant to me because obtaining 150 credits for the CPA exam is no easy task. The program is called MACDA, which offers 36 credits with classes on Big Data and Data Analytics. The courses are all online, which was done to allow students to allocate more of their time to prepare for the CPA exam. Some courses offered in the program are Data Modeling/Business Intelligence and ERP Systems/Data Analytics.
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This article describes and graphs what cities it predicts will be under water by 2100. Climate change appears is a real thing and people are still in denial about it, and it only seems to be getting worse. It is estimated that 275 million people will be not be able to live where they do now due to flooding. -
This article is about the controversy surrounding the president and Russia’s involvement in our last election. It speaks of how the Russians used the masses the same way advertisers do, by data mining. This is the act of using an algorithm to filter data and taper it to an individual’s preferences. There isn’t much about how or why they did it, but it is an interesting development to the whole story.
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So this morning Fivethirtyeight put out a new article showing the voting landscape of a county in Iowa. I am really into politics so for me it is really interesting to see the trends and demographics shifts in polling data. The article pointed out that there was only one country in the united states that voted both Obama in 2012 and with Trump in 2016 in land slide victories, and that county was Howard County in northern Iowa. Landslide victories are described as winning the vote by more than 20%. This paints a bigger pitcher on what the country wants from their president. They want change, and Hilary reflected she was not that.
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Recently, about two weeks ago, I received an email informing me that I may have been affected by a privacy breach on Equifax. I was granted a free credit report for each of the main three bureaus. The article I read discusses why Equifax even contains some of these peoples information. Supposedly Equifax does not believe that they should have to give up individuals personal information because they “own” it, even though people would prefer to opt out of them storing the information. All in all, Equifax is an ethics battle determining if they should be able to withhold personal data of many Americans who prefer them not to have the data.
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https://www.engadget.com/2017/11/27/google-trends-real-time-search-data-news-images-video/
This article highlights a tool Google created in 2015. The program is able to show what people search for on Google in real time using the system’s analytics. Being able to trace and follow trends in an instant, is extremely valuable to companies and media outlets alike. -
https://channels.theinnovationenterprise.com/articles/how-has-big-data-shaped-social-media
This article is interesting because it explains how big data influences social media. Obviously the two are connected but it did not occur to me to think about social media in terms of the data being collected from our posts. It’s also interesting to see how it’s changing the business of social media. -
This article is interesting because i didn’t know that disney studio is in the lead with the most movies revenue. Now Disney is trying to make a deal with 21 centry Fox. If deal were to go through, then disney will most likely be the top revenue maker in movie studio since they own other studios too like marvel.
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I think the most important takeaway from this course would be how important data is and how data should be analyzed plus visualized. I think learning how to use Tableau was an important takeaway as it could be something I could offer to potential employers as a skill. If I had to explain to a future MIS0855 scholar what this course is about, I would say is that data is everywhere and in this class your learn why its so important. As an MIS major, I believe this course is a better introduction to MIS than MIS2101 and you actually learn more in a sense of using data to understand it and make decisions.
I think the most important takeaway from this course was learning that data is everywhere and is relevant to us in many ways. If I had to explain this course to someone else I would say it teaches students how to find, sort, and analyze data to make it resourceful for others. For example, using the data collected and putting it into Tableau tot create a visual representation of the data that makes it easier to understand. Another important part of this course was learning how to utilize data when making critical decisions.
Personally, the biggest takeaway from this class is how important data is. Data can be used in so many different ways for so many different things which is why it is necessary to have correct and clean data. MIS0855 is a course that teaches you some of the many things you can do with data. This class makes you familiar with excel and Tableau which is something that many people do not fully understand how to operate. This class is very hands on having in-class activities that help with assignments and projects that you have to do in class.
In my opinion, the most important takeaway from this course is learning how to analyze data. We must first be careful of what data we choose, know how to clean that data, and finally know how to derive information and knowledge from data. This course has taught me all of these steps, and how to use tools such as Tableau and Excel to do so. If I was explaining to a future student of the course, I would say this course is all about how we can extract useful information from data, and in doing so it teaches you how to use helpful tools including Tableau and Excel.
I think the most important takeaway from this course is the hands on experience of analyzing and cleaning data. The softwares we used in the course actually taught me how to make use of the topics we learned throughout the course and gave me the ability to apply the stuff we learned. I am now able to use Tableau, Excel, and Piktochart because this course taught me a lot about each of them.
The biggest takeaway from this course is how important it is to analyze data and finding errors in data. I would tell someone that this course teaches you how to sort, clean, and use data in Excel and Tableau. Also, how you would transform data into useful infographics.
I think what I learned the most from this course was how to properly clean data, analyze it, and effectively display it to an audience. There is a lot that goes into analyzing data and I believe the course taught me the basics of analyzing data. Although it is a very complicated process the course broke it down so that it was easy to learn and understand and I can confidently say I can clean, sort, and display data proficiently which is a valuable skill to have in 2017.
I think that the most important takeaway from MIS0855 is that data is everywhere. This data can be dirty data, also known as data that contains errors. Even though data can be dirty, it is still everywhere we look and affects everything we do. If I were to explain to a future MIS student what this course is about, I would say that the course is all about understand how to find data, understand it, and make further analysis on the data. As an MIS major, I would definitley recommend this course becuase I thought the class was very well taught with many hands-on, in-class activities which really emerges students into the world of data.
The most important takeaway for me from this class, is realizing that even things you don’t think are data, are in fact data and since we are surrounded by data, it is important to realize the good from the bad, and how to handle it. In addition, the course helped me figure out how to, not only realize data, but how to use and predict from data. If i had to describe the course to someone, I would say this course was about locating and using data in a way to benefit ones self, either by knowing how to formulate data for easy structure and visual, or to be able to use pass data to find correlation in order t predict a future outcome.
I think the most important takeaway for me from this course is that data is literally everywhere, and there are multiple different ways to fix, analyze, interpret, and visualize data. If I had to explain to someone what MIS 0855 was about, I’d tell them the course first introduces you to the different types of data and then continues to teach you how to interpret and manipulate data. You learn to differentiate between good and bad data, and then learn how to fix bad data, as well as how to put the data into visualizations for a better understanding of the story the data is trying to tell.
I believe the most important takeaway from this course is understanding how much data is out there flowing around. It is important for us to know what do to with all this raw data. Being able to analyze, interpret, and visualize data makes every possible thing easier in life. This class is about learning hands on experience with technology and data tools. You will learn how to make your life ten times easier if you have a handful of data.
The most important thing I took away from this course was the understanding that their is different types of data, and different ways in which data is handled. It is important to grasp the breakdown of analyzing, visualizing and clarifying data, because these steps allow people to better comprehend the data that is being presented to them. Prior to this course, I did not fully know what data science was. This class has taught me a great deal about the importance of data as well as the behind the scenes process in which data scientist go through to bring that data to life.
The most important takeaway from this course is that data is everywhere. Before really learning about data in depth, you don’t realize how much data surrounds you on a day-to-day basis, but it really is everywhere and people are always analyzing it whether they want to or not. In my opinion, gaining hard skills within actual data analysis/visualization programs like Tableau and Excel went along great with the course. It’s one thing to just understand what data is and the different types, but its better to be able to use tools to help you analyze it better and see what it means visually. If I had to explain the content of this course, I would say it is really learning about data in general including the different types, where you can find it, cleaning it and being able to figure out ways to visually represent it within programs like Excel and Tableau.
The most important takeaway from this course is that we can have insights about anything when we have data about it. We can do almost anything from data as long as we are using the right tools and the right techniques. The way I would explain what this course is about to future MIS0855 is that this course is teaching them how to work with raw data, how to make sense about them and how to benefits from the data.
I think the most important takeaway from this course is that data is literally everywhere and being able to actively sort and analyze data is something future employers are going to want from their employees/new hires. In this class, I learned that there are so many insights that you can pull from data and that those insights are crucial in any and every work environment. Before this class, I didn’t realize how dependent my future career path was in data analytics and how often I actually encounter and use data every day. For future students, this class essentially teaches you the importance of big data, why it’s relevant and applicable and how you can sort, analyze and display big data. It’s your first introductory course in properly sorting raw data and teaches you how to, essentially, read data to produce insights.
This biggest takeaway from this course, for me, is that data is everywhere, and is in every aspect of society. Every major should not how to find data, clean data, and analyze it. Having this skill set will come handy in your everyday life. If I were to explain this class to another student, I would tell them the course shows you data in its simplest and most complex forms, and by the end of the course, you will know how to handle both. It’s an introductory course that will prepare you for other courses, and not just MIS ones.
My biggest takeaway from this course is the importance of data. With us being a business school no matter what major, the one thing we all use and rely on is data, so it was very valuable to better understand the logic behind it and how to interpret it. I would say analyzing data is definitely a challenge for most people without a technological background. However, MIS 0855 broke it down so well into terms that I was able to enhance my prior knowledge to data. I learned so much more when I look at data from this course.
I think the most important takeaway from this class was how important data is and how to clean it properly and analyze it. Data can be viewed in so many different ways, so it’s really important to make sure the data is displayed correctly and shown to the audience in the proper way, whether it is in an infographic or a table or any other display. Also, it is really important to make sure the data is not dirty because that can skew all of the data and someone’s opinion of the data. In this case, the data will not be correctly displayed and the people looking at the data will be misinformed. I would say this course is about how to properly extract and analyze data and then how to use the data the display to our audience.
I think the most important takeaway for this class for me was the importance of cleaning data and using Tableau. So many companies have such an abundance of data out there that they don’t know what to do with. Taking the idea of cleaning the data and making it into useful information is something that many companies are looking for these days, and I think that skill will be very useful in the future. On top of that, I had no experience with Tableau so that is something I am going to take and use with my career in the future. Tableau is an excellent program to demonstrate how you can manipulate data into useful information, and I am happy to have learned the basics of it.
My most important takeaway from this class was just to see how much data affects our day to day lives more than i have ever imagined. Obviously i understand the huge part technology plays in our lives, especially when compared to other generations, but data can tell such a telling picture about our past, present and future. If i had to explain what this course was about, i would say it enlightens you on the future of companies and shows you realistic applications of what we are learning. Sometimes classes may seem dull and boring because we can never perceive to understand how this applies to our future, but with this we can see data being used in our everyday lives.
I think the most important takeaway from this course is how important data visualizations are. Not only is it important to gather and analyze data correctly, you must also represent the data properly. Data visualizations, through many different softwares, convey messages plain text simply cannot. This class showed me just how important data visualizations are and supplied me knowledge and skills I can use throughout my professional career.
I think the biggest takeaway from this course is just how quickly and drastically our world is moving toward data. Data is the future of not just the business world, but society at large, with large amounts of data being taken from social media, Google searches, reviews, etc. Data is an incredible indicator and predictor for success for companies. It’s more than a decision making tool, it’s the sole basis of most decisions in a successful work environment. Similarly, data is nothing unless interpreted in useful ways. Programs like Excel, Tableau, and many others provide us with interesting ways at looking at data and interpreting it. This course is really good at highlighting the importance of data visualization as not only a practical use for data, but also as a creative one too. I know that my career and many of my peers’ careers will be defined and shaped by data.
I think the biggest takeaway from this event is that data is the amount of ways that we can analyze data. Just by using Tableau and Excel, we were able to learn that there are many different ways that we can interpret the data, as well as visualizing it. Another thing I learned was how complicated data can get. The best example of this was the cleaning data assignment, how when the data set is too large to go through, there are many ways to manipulate the data to get more accurate answers. There are lots of people who look at a data set and do not know what to do with it, and this class does a terrific job on teaching students what you can do with a dataset.
I think the biggest take away from the course is different ways to analyze data. For me personally, I was able to work with excel more on the data side. There are other courses at Fox that introduce to excel, however this was honestly the most helpful course for what I am trying to accomplish in my career. I was introduced to Tableau, which is a very helpful application for analyzing data. I will definitely being using Tableau in the future. Honestly every business major should take this course as their Gen ED science class.
My most important takeaway from this course would definitely be being able to apply what we learn in class to topics that mean something to me personally. I feel like once you take what we learn in Data Science outside of the classroom and into real life with real life things, it shows that you truly understand the work. If I had to explain to a future MIS0855 scholar what this course is about, I would say that it’s about understanding everything about data: what data actually is, different types of data, what and how it can be used, and then applying all of that to real life things, because data is everywhere.
To me, the most important thing I have taken from this class was the ability to visualize data and present it in an effective way. This course should be viewed as not only being able to analyze data, but also being able to show it to an audience. This course also allows students to dive into current issues with data analysis and understand the slang behind some terms used by IT specialists. Finally, this course does a phenomenal job of breaking down terms and applications of big data and allows for a complete understanding of each element of data.
The most important thing that I can take away from this class was learning how to use Tableau and visualize data. Before this class I have never heard of the software Tableau. The only software that I used for data before this was Excel, and I must say Tableau is a much better way to display data. Now that I have learned about Tableau I can use it for other classes in the future to give my work a better appearance. This class allows students to understand everything about data, from what data is all the way to working with different types of data. It taught me how important data can be and how the smallest errors can give you bad data. This class taught me the data is all around us and everywhere you go there is some type of data.
I think that the most important takeaway from this class is learning how to analyze and visualize data using Tableau. Working with data an cleaning it is really important as well. If I had to explain this course to someone I would explain it as that this course is all about data. It allows students how to work with the data all the way from cleaning them till visualizing.
The most important takeaway from this class is how valuable data is for companies and how important insights can be made to benefit both the companies and consumers. The emphasis of utilizing programs to turn data into knowledge will be useful skills in the this age of big data and information overload. Tableau and excel were effective tools when analyzing data and obtaining key insights and it seems professionals are still reliant on these programs for data analysis based on the articles read throughout the class. Although qualitative analysis has it complexities, I wish the course focused more on it and went into deeper detail about qualitative analysis since it seems to be a skill high in demand.
I’m a psych student, and I know the data is very important part of the research study. and before this course, I had a statistic course, but there have some differences between those two courses. The most important thing I learned of this course is how to use Excel and Tableau. I will recommend this course to future students, I will say this course will help you not just in the college, but for the whole career after yours graduated.
The most important takeaway from this class was developing your data analysis skills to a software called Tableau. Being able to intergrade and certain data sets that may be dirty or clean into the program and finding different ways to look at it. Another big takeaway is, how much data is used in our everyday lives but we don’t realize it because it may not be in number sets.
This course taught me that data is everywhere and that are many different ways in which that data can be analyzed. One main takeaway with this is that when analyzing data it is important to make sure that the information within your data set is correct and that there are no mistakes. This ensures that the data does not become “dirty”. In addition, when displaying visuals about your data, for instance, using a pie chart or bar graph, it is important to use the appropriate visual. By doing this, the audience will be able to fully understand the message you are trying to convey with that data.
The biggest thing that I got from this class was the tableau skills. I do not have a lot of hard skills so this was a very important thing for me to pick up. I also just have a better understanding of data now an the role it plays in our daily lives. I think these two things combined have helped me prepare for the future even more.
The biggest take away I got from this course would be the lessons we learned about practical applications of data and how to use excel and tableau. I took the MIS class required for my major and hated the professor and subject and got a low grade, but I thought that it was a decent subject so I took this elective the next semester. I can honestly say I learned more useful tools in this class than I did in the big MIS class and the online Excel course we are forced to take. I would’ve taken this class just to learn tableau alone but ended up learning useful tips on how to be more efficient in Excel as well.
The most important takeaway from this course for me was the usage of Tableau. I heard a lot about this data visualization software but I never really used it. This class helped me learn this software. This course also helped me brush up my excel data manipulation skills.
Although this class was a gen ed for me I learned a lot of stuff that is important in my major. As a communications a major, we look at a lot of data when creating advertisements or when finding out information about a brand. The most important take away from this class is having clean data. Before this course I kind of just assumed that if data was created by a reputable source than it was correct. I now know how to go through any data set, small or large, and how to clean it and make it valid for use.
Although for me this class was a gen ed, I learned a far amount. I would say I improved my technical skills, I learned a lot about tableau and excel which are important skills to learn. That is what I would say as the most important takeaway from this class is the knowledge of how to use this programs. This programs are very useful skills to have in the business world and can help a lot on resumes.
I would have to say that the biggest takeaway from MIS0855 is the importance of data and its applications. For instance, raw data is useless unless you organize it, clean it, and make use of it. I learned how to display data in this class from using softwares such as Tableau and Excel.
The biggest take away i learned from MIS 0855 is using discovering another database application other than excel or acess
Tableu comes in handy and makes visual presentation looks better
I think learning how to use tableau was my best and most important takeaway. I am an MIS major, so i knew a lot of the topics that we discussed about data. However, I knew nothing about Tableau before this class and I learned that it is very useful.
The most important takeaway I received from this class is the importance of checking your data. There are several instances when data is dirty and it can make a large impact on the information someone is trying to develop. If I had to explain what the course was about to a future student I would tell them that it is about data, to say the least. You learn about the different ways in which data can be utilized and you learn how to actually utilize it in those ways.
The biggest takeaway from this class was how important it is to have clean data for a proper analysis. I think learning how to use tableau to create a visualization for the data is important and relevant to everyone regardless of what their majors are. This course taught me how to look at data and clean it so the results are not skewed, then turn it into visualizations that are easy for audiences to interpret.
The most important takeaway from this class would be how to analyze data and that data analyses can be skewed. How to clean data and look for errors was also useful. This class showed me that it is important to know how to view/analyze data no matter what field you go into.
I would say that the most important takeaway from this class is that, generally speaking,” it’s not all about the numbers” – meaning that while data in and of itself is powerful and plenty, the analysis of data is even more important. For data to be able to engage an audience and make persuasive arguments, it must be organized, clearly evaluated, and appealingly visualized with precision. I would say this class is all about understanding how to approach big data sets from the start. It is focused on learning: how to ensure the quality of data being used in analysis, how to best measure and compare specific pieces of data, and how to present findings in a way that fully explains the illustrated data and makes a coherent point by telling the data’s “story”.