Joe Spagnoletti

  • 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]

  • Leave your response as a comment on this post by the beginning of class on March 23, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]

    • An example of a KPI that students pay close attention to is our GPA. It is specific, measurable, and achievable for example, all graded school work is reflected in your GPA and it measures how well you are doing in school while also helps you decide if you need to study more/work harder. Additionally, it is extremely relevant because it is essential to schooling and getting a job/internship and its time-variant because you can look at specific semester GPAs, over all GPA, predicted future GPA and more.

    • One example of a KPI I use on a daily basis is the sleeping app on my phone. It essentially tracks the amount of time I spend sleeping based off of the times I turn the program on and off. From there, it estimates what I can do to improve my sleeping habits as well as just keeping previous tracks of data stored within the app. With that being said, the app proves to be specific in that its purpose is to keep track of my sleep, measurable in that it measures how long I sleep daily and time phased, as it is based off how much time I spend sleeping.

    • One example of a KPI that I used daily would be the fit bit watch used to have. It was very specific to use step as an accurate measurement of caloric output. it was extremely accurate and time phased to daily activity. It would also track your sleep cycle based of of movement and rem duration.

    • A KPI that i use on a daily basis is the Pearson Portal online book, which tracks all the work I’ve done in the class and what my grade total is. It is specific in that it only shows the work I’ve done for the specific class. It measures all the work i have done of the class and adds it up. It achieves its goal by display and taking all my work for the class. It is relevant to my grades, which is a huge part of my schooling.

    • A Key Performance Indicator I use on a regular basis is my class gradebooks. These grades are specific because they are broken into specific assignments, tests, and quizzes. The gradebook is measurable because they present numerical data relating to how I performed out of a total scale. Grades are achievable because I can study harder and prepare for my tests, quizzes in order to receive a higher grade next time. These grades are a relevant performance indicator because grades affect my GPA and GPA impacts my present and future career. Gradebooks are also time-variant because I can look at them weekly, monthly, or a semester.

    • A Key Performance Indicator I use is my workout journal. Whenever I go to the gym I write down how much I lift and how many reps I do. This KPI is specific because I can see exactly how much I benched and how many times each time I workout my chest. It is measurable because the weight and reps are broken down into side by side columns representing each day. It is achievable because I can work to improve the weight and get the most out of my workout. My workout journal is relevant because how much weight I lift and how many times relates directly to how good my workout is. It is also time based because I can compare when I worked out with a certain weight on different days.

    • A KPI that I use everyday is the planer notebook. It has all of the needed elements in the SMART criteria. It is specific because it provides all of the information regarding my homework with the due date, test dates, meetings, etc. It measures how much of work I have finished and stuffs that I still need to complete in order to achieve the goals that I have set. It is also relevant to the grade of classes I am taking because if I finish all of my work on-time and also have enough time for studying tests, I would probably receive a good grade for the class since I am always on top of my work by following my planer notebook. It is also time-variant because I could go back to check the date/ month that I submitted my work in case my work and grade is missing, so I have information to back it up and easily find the missing items.

    • A Key Performance Indicator I use everyday is my diet tracking application. It is specific because I can see exactly how many calories I am taking into my body every time I eat something. It is measurable because there is a chart with columns that show how many calories each food has, the amount of each food I am taking in every meal and what is my total calories input. It is achievable because I can manage my calories intake and make sure I get enough healthy calories everyday. This diet tracker is relevant because making sure I eat enough food with a healthy diet is important for me to live energetically and happily. It is time-variant because I can view my calories intake daily, weekly, monthly, plan my diet for the future and see the progress of myself.

    • A KPI I used regularly in high school was a website that tracked our deadlines for major assignments. The website showed specifically which classes needed improvement or attention with a completion percentage for each task. It is achievable because by the middle of the year, all work submitted was sent out to graders; therefore, the website was relevant to my success. Due to the specific international deadlines, the KPI was time-variant and the submission dates were extremely strict.

    • A KPI that I used on a daily basis is caloric intake. Usually I have a specific goal, usually around 2000-2500 calories per day, which is also measurable and attainable. Also, it is realistic, and I even go over and under sometimes, and it time related over a day’s time.

    • One example of a Key Performance Indicator is the DARS Self Service audit that Temple offers to students. The program helps every student stay on track by measuring a roadmap for your major. It is achievable because it is letting you know in advance all the classes you have to take to receive your degree. It is relevant because it shows your current GPA and also all the credits you already have earned. Finally, it is time-phased because it will help you map out the progression of the coursework needed to graduate in four years.

    • One KPI I use everyday is BlackBoard. I use it everyday to check my grades because I care about my success. It is Specific because it tells me exactly what each grade is and the percent I have. It is measurable because it measures my performance in each class and it shows if I am passing or failing. It is achievable because I can try to get better grades if I am failing a class. It is also relevant because my whole life at the moment revolves around my school work and grades. It is also time-variant because I can look up my grades whenever I need to so I can make sure I succeed in my classes.

    • An example of an KPI that I use on a daily basis is the app MyFitnessPal. I use it everyday to make sure that I consume enough calories throughout the day and that I consume enough protein daily. It is specific because it tells me the exact amount of each nutrient that I am taking in. It is measurable because it counts based off of every single thing you eat. It is attainable because I can change the food types or amounts whenever I want. It is relevant because I need to make sure that I consume enough nutrients throughout the day. Lastly, it is time-based because it only counts the nutrients for 24 hours and then starts a brand new sheet the next day.

    • An example of KPI is the health app on my iPhone 7 that tracks my steps. It tracks them over time (how many in day, week, month or at any specific time in a day), shows steps in distance (how many miles a day), floors climbed, and how many calories I burned taking those steps and climbing those floors. Also, a certain amount of steps I want to take in a day is achievable.

    • A Key Performance Indicator that I use every time when I go to exercise is the information on the running machine. I can type in my weight and age, hands hold the sensor as well. Then, it calculate my speed, heart rate, how many miles I have run, and how much calories I have used. It is specific and measurable – the information is a precise measure. It is achievable and relevant – I can change my running speed or track my heart rate to achieve more efficient way to burn my fat. It’s time-variant, I can record how much calories I have used per day, per week or per month in the gym.

    • The KPI I use is the activity monitor. It’s helpful when I encounter no responses for my computer . It’s specific and measurable that it shows all the applications I’m using and also the percentage of CPU they used. It’s achievable that I can quit some applications if they’re using too much CPU. It’s relevant that higher CPU occupation can slow down my computer.

    • A KPI that I look at on a regular basis is my bank account. U.S. dollars are specific and measurable numbers. It is achievable, as i can work more or spend less to make it go up or down. What is in my bank account is relevant because I need money to buy food and everything else. Also it is time-variant as I can see how I did for a year, for a month or even a day.

    • A KPI that I use regularly is my Apple Watch. More specifically, the GPS/health tracking that it uses. It track specific and measurable variables, such as my steps, heart rate, mileage, time per mile, and calories burned. I use this all day everyday, but more specifically, when I go for my runs. I have been a runner since I was 11 (making it relevant), and keeping track of my progress is very important to me. Apple has an “Activity” app on the watch and my iPhone where I can access all this data. It is time-variant, luckily, because I can look back on my activity logs until the day I got the watch which is great for seeing how far I’ve come. Looking at my data helps me adjust my running habits in order to decrease my times, lower my heart rate (over time) and be able to run farther and faster.

    • A KPI I use regularly is my daily step goal on my fitbit. I aim to get at least 10000 per day. It is a very specific and measurable goal because the fitbit is fairly accurate in measuring my steps. It is achievable because I prefer walking places so my goal is fairly easy to hit. It is relevant because 10000 steps is the daily amount you are supposed to do to stay healthy and it is a daily goal so it is very strictly time phased.

    • One KPI that I use on a regular basis is the activity app on my Apple Watch. The app conforms to SMART criteria. It is both Specific and Measurable because it measures my activity and it very precise. Each goal from the app is specific to me. It is also Achievable because I am able to track the progress towards my goal and I am able to achieve that goal. It’s also relevant because each goal is relevant to me and the goals change over time as my health changes. And since I am able to go back in time and see my progress and goals, it is also time-variant.

    • An example of KPI I use is the statistics about my online blog. It is specific because I can see how many people visited my blog, and other things such as by what keyword they came to see my postings. It is also measurable because I can see the visitors in numbers, and is achievable because I can know what keywords people are interested in, and change my contents according to that. It is also relevant because the number of visitors directly means how popular my blog is becoming. Lastly, it’s time-variant because I can see the visitors in intervals of days, weeks, months, and years.

    • An example of a KPI that we use everyday would be the weather. It is something that can be very important for our day to day lives, and it has all of the SMART qualities. It is specific because you can narrow it down to place, day, or hour, Measurable in that we can have amount of precipitation or temperature, Attainable at any time through technology, Relevant because it is what we have to live in every day, and Time-Bound because we can track the changes hour by hour. Especially with the weather as unpredictable as it is right now, keeping track of it has to be a daily activity for almost everyone.

    • Number of attendances is a good KPI. It indicates how dedicated a person is to his class or company. Attendance is measurable. If you do not be late, your attendance will be good. A lazy person can try to get to his office or class early to improve his attendance. Also, attendance is always relevant to people’s performance. If a person really want to do well in his class or office, he shouldn’t be late or absent. Last but not least, a company can always track a employee’s performances by looking at his yearly attendance or weekly attendance or any other time scale.

    • An example of a KPI I use daily is my computer’s task manager. The task manger shows me all processes I am running and each processes’ CPU, memory, disk, and network usage. Also it shows my overall computer usage for those categories. This has a large impact because of one of the categories are too high and causes performance issues I can look at the process’ usage and act accordingly.

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    • A Key Performance Indicator that I use on a regular basis is an app called Robinhood. This app helps me with my stocks that I buy on a regular basis. In this app, it shows how many stocks I can buy, or how much profit I will be getting after I sell my stocks and etc. This app is very helpful to my everyday routine, because it helps me make money the easy way and without any trouble.

    • One of the KPI i use is an app about calculating your steps when you are walking. The steps have measurable statistics and the app shows my beginning point and destination, personally i think it shows the specific criteria.

    • I measure the traffic and engagement on my website everyday by looking at sessions and bounce rate on specific top-value pages. I’m then able to analyze how well people are responding to my team’s content which allows me to see if there are any opportunities for optimizations based on these results. In addition, I track user acquisition to understand where our users are coming from and how they are hearing about the blog.

    • One of the example of KPI can be like the smart watches. They follow the SMART criteria they tell you specifically how much exercise you have done or how much calories you have burned in a given time. You could also set goals that how calories you have to burn.

      This link leads to the statistics page for online player v player of a game I play frequently. Within this page there are numerous KPIs as to how well I perform in the PvP. This takes form in the Kill/Death ratio as measured against a standard of 1.00, a positional ranking compared to all other active players, as well a statistical ranking, referred to as ELO. These measure are specific to my character only, with separationsome by day, game type, and each specific match. The data are very clearly measurable and achievable by my own standards. Relevance only applies to myself but the data and KPIs are highly time variable, reaching back to May of 2016.

    • A KPI I depend on daily is GPA , as a student GPA Specifically visualizes how I am doing in school. My GPA coming directly from my performance in the class making it relevant. GPA has a scale of it’s own, from 1 point to 4 points, I have an understanding of whats good and whats not and how it was measured. GPA is also achievable, a student can raise as well down grade their GPA making school Challenging. GPA over the course of time is able to be planned and takes time , but its is achievable to reach your goals before the semester ends.

    • I use my smartphone everyday, which can be an example of KPI. How often I use each application will makes my phone decides to close the application or not. Power usage is also important, it tells me which application uses the most power and I can use my battery more smart. Network usage is very important to those limited data plan. I can see and choose not to use that app that much to avoid the data usage.

    • The KPI I monitor closely on a daily basis is my punctuality. I really believe in the “if you’re early, you’re on time; if your on time, you’re late; and if you’re late, you shouldn’t have even bothered to show up” ideal. This boils down to preparation, which is key in cultivating relationships and experiences that further one’s goals. (I am aware that I’m turning this in a little on the late side, but it’s not for lack of preparation, rather lack of internet access.)

    • An example of a KPI would be the myfitnesspal app on my iPhone. It is specific, measurable, achievable, relevant, and time-bound, all while helping me set and achieve, and measure daily fitness goals. It is specific because it has many components that factor into specific aspects of health. It is relevant because I use it on a daily basis. This app is something that directly impacts my life because I use it everyday to better myself. This app is user friendly and helpful most of the time.

  • Some quick instructions:

    You must complete the quiz by the start of class on March 20, 2017.
    When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign […]

  • Here is the exercise.

    And here is the spreadsheet to complete the exercise [In-Class Exercise 8.2 – OnTime Airline Stats [Jan 2014].xlsx].

  • Leave your response as a comment on this post by the beginning of class on March 9, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]

    • I have not worked with excel too much but one mistake that I remember vividly is when I forgot to do a full backup before working on data.This happened when I was working on a project and as I was about halfway through it the system shut down and my work was not saved. This mistake was very inefficient and led me to have to start all over creating more work for me.

    • I myself have not made any of the mistakes listed in the article, as I have never been tasked with something that required one of the steps mentioned in the article. However, I think #3, the step about working on a database without backing it up first, most important. Realistically, the easiest way to figure out why something in the program went wrong is to backtrack, and having a backup file would be the easiest way to do. In addition, especially with how haywire some computers can become, it’s always useful to have a backup in the case of a surprise shutdown.

    • The mistake I have made often is “Start working on the database without doing a full backup first”. Many times I get so wrapped up in the data I am inputting or problem I am solving and forget to save. For example, while taking the Excel for Business Applications class I forgot to save my Excel work which provided answers for the quiz I was taking and I had to redo the whole Excel sheet in order to submit the quiz.

    • i cannot recall making any of the mistakes listed in the article. However, number 4 (sort a spreadsheet without including all columns) seems like it would be the best to avoid. Ive come to this conclusion because if someone was to work on a data sheet and not include all the columns, then they would have bad data and not even know it. When they finally would realize then they would have to start the entire thing from the beginning.

    • I have made the mistake before of not including all of the columns in a sort (#4). I was in an information technology class in high school learning excel and creating a small database. I sorted the names by alphabetical order, but forgot to include the phone number column. Every person then had an incorrect phone number associated with them.

    • I have made the mistake before of not including the word “false” as the 4th vlookup parameter. It was for a personal finance class and we were pulling information out of a grocery list. I just remember it not working when I initially tried and then had a classmate come over and we figured out the false was missing. To this day still do not completely understand why that false has to go into the formula.

    • I really am not a big excel user or fan, so I don’t know if I’ve even done advanced enough work in the program to run into one of these errors. The one that seems the most deadly is the last one: clicking “yes” on the message that says “do you want to remove this from the server?” It seems like all of the other errors, while headaches, will only screw up one set of data or one attempt to use that data. This error seems to be the only one capable of removing data from an entire system. It also is the only one that removes the metadata you’d need to correct the error. This seems to be the most widely devastating and most difficult to fix error.

    • From my point of view, we are very likely to make any of the mistakes that listed in the article. From my personal experience, I have not done so much work which is related to Excel particularly.; however, if I have to choose one mistake that is so important to avoid while doing Excel, it would be mistake #3 “Start working on the database without doing a full backup first.” It is obvious that it is always helpful to save the original version of work before doing anything with it; therefore, if you have any issues while doing work with Excel, you can always go back to the original version and compare the current version that you are working to figure out what steps you did do correctly. In addition, it is so important for everyone to keep in mind that while they are doing work with Excel or Word or anything, they should save their work multiple times because they might accidentally close the work without saving it or the computer suddenly shuts down because it runs out of battery.

    • I do not recall making any of the mistakes listed in the article because I have not had a lot of chances to work with data. In my opinion, corrupting data in anyway is not good and it will affect your understanding of the topic that the data represents. However, I think it is best to avoid number 3 in the article, which is start working on a database without backing it up first. This is because there might be a technical error at anytime and you might lose your database. Furthermore, while evaluating a data, you might start to recognize problems here and there and it will be very hard to chase back the data if you do not have a back up of it.

    • I do not believe I have ever made a mistake, however, if I did, the one to avoid the most in my opinion is step three. If you do not create a backup, you could lose a lot of times work and effort into creating a data set. If you do not create a backup, you might lose all of your work that was created. People make the mistake all the time of not creating backups or when to save your work. Whenever I make papers or Excel sheets, I always save my work throughout the process. Overall, step 3 is crucial, as you always need to save your work throughout the work process.

    • I personally don’t remember making any of the mistakes listed on the article, but after reading the article I believe that number 3 would be the most important to avoid. Creating a backup isn’t a hard step to do but yet could be a lifesaver. If something happens during the process and someone loses all their hard work, it could take hours or days to get back what they lost, so number 3 would definitely the most important to avoid.

    • Last year when I worked on a paper about trends in on base percentage in baseball players over a season, I hit “select all” but Excel missed a few columns. I did not realize the mistake for a long time and the sorted spreadsheet showed completely different results. Unfortunately it took me two times as long to figure out and fix the issue so the data was correct.

    • The thing from this article that I have definitely done before is Number 3, which is not doing a full backup when you’re working on something. This actually happened to me last class when we were working on Excel and as I was going through my work Excel shut down unexpectedly. This then sent me from being almost finished to barely started in the blink of an eye. You would think something so simple would be easy to do, but sometimes when you’re working on something, like me in this situation, it can slip your mind very easily.

    • I haven’t done too many assignments through excel, however, last semester when I was taking the Business Excel course I made a horrible error. I did not back up my files when I was working and in the middle of an assignment my computer suddenly died and did not autosave my work. I was so close to finishing the assignment and I had to start all over again which resulted in me wasting 2 hours of my time, but I definitely learned my lesson to always backup my work.

    • I have only been exposed to Excel very few times, and when I was I was always careful with which cell referenced what. For example, I had to make a bunch of probability distributions and their respective graphs for my Actuarial Prob & Stat course last semester. every cell had to be in reference to certain criteria (trials, successes, failures) so I could not use hard coded data. I had to be weary of how the function affected each value and if it was correct.

    • I have barely used excel, but from what I see, I would say that no. 3 , “Start working on the database without doing a full backup first”, is the most important. I think as a general rule for pretty much any document or any electronic piece of work that this would be the case. I think this would apply especially to excel because I’ve seen excel sheets with 10s of thousands of data points on them, and I would hate to have to lose all that data.

    • I’m not an experienced Excel user however I have gone through a couple introductory courses. In on of the lessons we were utilizing the Vlookup option and computing excel sheets with formula inputs. About 15 minutes in my computer decides to update. Forgetting I had selected an option to update in 30 minutes , half an hour prior, I panicked thinking all my data was gone. Little to my surprise I have no backed up any of it and the result, well you can already guess. At least 25 minutes of work gone just like that.

    • I have not made any mistakes listed in the article in the result of I just used Excel to do basic things. But I think the number three: “Start working on the database without doing a full backup first” is the most important to avoid. Think about that you are doing a huge assignment which you have done the whole day and it almost finished, But your computer shut down suddenly. You found out that what you have done the whole day are disappear, It is pretty disappoint, you will not want to do it again even if you know how to do all of them. That’s why I think it is the most important to do a full back up first before you start working on database. The action does not take your time more than one second after all.

    • I believe #3, “Start working on the database without doing a full backup first” is the most important. After doing all the data compilation like taking out the Nulls , changing the type for specific column cells and also clean up the missing data for excel when I was doing a group project, my old computer crashed for no reason. I guess because I just got an 4G RAM and at the same time I using other applications. The worst thing of all is I didn’t back it up, I need to redo all the progresses. It turned out that I stayed up late to finish all the work. Since then I never forget to press Ctrl + S and also after I finish one progress I’ll back it up on cloud.

    • I don’t really remember what mistake I did, maybe it was number 9. Copying the cell without checking from the top to the end. This made me so confused about it because at that time I don’t even know what is wrong! Undo it would destroy the data I created, don’t undo it the data is all wrong. And number 3, really nightmare for not backuping the data. Lost almost everything due to accidents.

    • There have been a couple of instances where I have copied formulas that use relative coordinates when trying to make calculations using a set of data. When this happens, the formula doesn’t use the correct cells and there will be a miscalculation. Luckily, these problems have been easy for me to fix because the data sets I have worked with are relatively small and it is obvious when there has been an error in the formula. However, these minor errors show the importance of being careful when using formulas in Excel.

    • To me making a backup is the most important aspect when working it excel. In high school I had to do a excel project in personal finance but I was not regularly saving my work. At one point I accidentally put in the wrong equation and it messed up all of my numbers. It was so messed up I had to start over. Now, whenever I do any project I make sure to save it twice on my computer, then put it in Google drive. This way there’s almost no chance of me losing my data.

    • In my labs, we use Excel a lot to show graphs and organize our data from our lab notebooks. At first, I made so many mistakes and my labs would take me an unreasonable amount of time. My brother uses Excel a lot for his job so he had to help me out with my labs and show me how badly I was messing my graphs up. The mistake that I made the most was missing the data type. A lot of the labs had to do with time intervals, and sometimes dates for more extensive labs. I was putting my times in terms of seconds, but some of my labs required several minutes. My numbers became way too large in comparison to the rest of the data, making my scales unnecessarily large. Instead of doing seconds for labs like this, I started recording in a 00:00 format, or in minutes.

    • I have not used excel too often but one mistake I did make was starting to work without making a backup. I was doing a lab where I had to put in number, make graphs, and compare them. While I was working on this my power went out and all my work was gone. This helped me learn to always save and often while working on important assignments.

    • Personally I haven’t had the opportunity to work a lot with excel so I can’t really say I have made any of these mistakes. However, I feel like number 3, start working on the database without doing a full backup first, is the most important thing to avoid. There is nothing more frustrating than having to redo an assignment after just completing it.

    • I am an “Apple Guy” and have worked more with Numbers than Excel. However, I feel that number 6 is the most important to avoid: Miss the data type. This is becuase misrepresenting dates and times as integers could have very negative effects (like not knowing when an entry was from).

    • I personally have not made a mistake that was mentioned in the article. However, I believe the most important mistake to avoid is not backing up the data. You should always back your data up because if you do make a mistake, you can always go back to the backup. I think that is why backing up your data is the single most important thing you can do to avoid corrupting your data.

    • Not only for Excel, but with other kinds of files(like PowerPoint, Word, etc) I mainly had problems similar with Number 1 : click “yes” without carefully evaluating the message that says “do you want to remove this from the server?”. This usually happens when I have a file on my online cloud, something like google drive. I click on the file and open it, work with it, then just close it(thinking that it would have been automatically saved). But then I realize that I didn’t even download the file in the first place, I just literally opened the file and then closed it.

    • I have made one of the mistakes listed in the article. The mistake I made was sorting a spreadsheet, but not including all of the columns. The cause of this problem usually happens to me because the spreadsheet I was creating had over a thousand columns which made it easy for me to forget to include a small amount that was towards the end of the spreadsheet.

    • Given that I used to major in Business Management at Widener University before transferring here to Temple in 2014, I’ve worked with Excel quite often in Accounting, Finance, and MIS classes. The processes involved with data entry and functions used for calculating several different figures at once was tricky, and required much practice to become accustomed to the program. One mistake that I frequently make when using Excel are when I “Put values in fields that are supposed to be pointers or references” (#7) and “Copy formulas that use relative coordinates” (#9). In one respect or another, both of the errors I mentioned often lead to numerical miscalculations from the program having trouble recognizing the function you’re asking it to perform. Another problem that can arise from this is when the program cannot differentiate between certain column terms by attributing the same function to two closely similar words that each command separate functions.

    • I’m not really good at Excel, and i don’t really use it as well. So I am not sure about the other mistakes in the article talking about for the exact meaning. But one thing I remember during I used Excel is that “Copy Formulas that use relative coordinates.” I always get trouble when I want to calculate something in Excel since I’m not professional in it. I like copying and paste the formulas from one to one, actually some of them not using the same formulas. It really gives me a hard time.

    • Having experience using excel and having to learn how to use it you definitely come across problems. For instance, I had a hard time with VLOOKUP, which was utilized quite frequently and if it’s not exact it won’t work. Also a good habit is to always do a full backup, it saves a lot of time if anything occurs. Also to avoiding missing data types, which definitely corrupts data.

    • It’s easy to forget to check data type. Sometimes the data represents fine but its the wrong data type. Then there is an error when typing in specific date or phone number.
      Click “yes” without carefully evaluating the message that says “do you want to remove this from the server?” happens a lot too. Because the dialogue is so annoying. People just don’t want to read it.
      The third one “Start working on the database without doing a full backup first” should be the most important one to avoid because that gives you a chance to redo your data instead of losing them all.

    • After working with excel on numerous projects and assignments , the one mistake that has stuck with me even tell this day is the copying of formulas. Working on accounting assignments in high school and college, I have alway had troubled calculating the logged rows of numbers given to me from the information. The copying of formulas in my case resulted in errors that eventually made me think about the numbers being in the wrong place, when in fact the formulas were not specifically adapted correctly throughout the entire data sheet.

    • I haven’t made these mistakes before. If I did have to pick one that I felt was the most important it would be “Don’t miss a data set”. I feel like this is important because if you miss a data set then you will be missing information. Without the information it will be hard to create an excel spreadsheet at all. Even so missing one set of data can probably mess up all of the other data.

    • I started working without saving one time. I was doing all my work and the computer crashed. The excel restarted and all my information was lost. I should have saved everything beforehand but I made a mistake not doing that and I had to start all over again. Next time I made sure I saved my work and made sure I backed it up so just in case it got deleted I would have a recovery copy of it.

    • I haven’t make any mistakes yet, but personally i think the Number 2 ‘what systems i log to’ is the most important mistake to avoid’. There are 4 different management systems, we must have solid knowledge on each system and be familiar with them

    • Number 6: Miss the Data Type, When I was in the High School I have to create an attendance sheet in excel and I Typed the wrong data type and like no ones attendance was showing and I almost lost my position of IT captain of school. Due to people started complaining about my mistake on my captain position. So after that I never made any mistake related to excel.

  • Some quick instructions:

    You must complete the quiz by the start of class on March 7, 2017.
    When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign […]

  • Here is the exercise.

    And here is the dataset you’ll need [Vandelay Orders by Zipcode.xlsx].

  • Here are the instructions (in Word)   (and as a PDF).  Make sure you read them carefully!  This is an assignment that should be done individually.

    And here is the data file you’ll need: Vande […]

  • Leave your response as a comment on this post by the beginning of class on March 2, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]

      These series of data sets tell about trade and how it is has had an impact on teams that choose to trade players as well as the amount of trades teams do per year. This is interesting to me being an NBA fan because it identifies the actual opportunity costs and benefits of trading players.

    • Are We at the High-Water Mark for Shifting in Baseball?

      The data sets show the change in shifts in baseball throughout the past few years and how the hitters adjusted to this change. Although there was a drastic increase in shifts recently, hitters responded by changing their approach to avoid hitting into the shift. Through the different player statistics, the author evaluated the change in approach over two years. I found this interesting because shifts are so popular in baseball, but it is only a matter of time until hitters figure out how to beat the shift and these types of data sets are probably what I will be collecting in the future.


      The article I chose is titled, “The Minimum Wage Movement is Leaving Tipped Workers Behind.” The data basically shows how minimum wage has increased for workers who do not receive tips and how it has stayed the same for tipped employees such as a waitress throughout every state. The most interesting fact about this article is that waitresses salary does not increase with every other employees in their state and they have to rely on an unstable income to support their families which I believe is unfair. Also, I am glad many states are trying to eliminate this situation and hopefully the new President helps push the movement further so it will be eliminated and each state and everyone will be paid fairly.

      The article I chose was about the genetic variation in cannabis and its relation to our relationship with chimpanzees, in that the plant contains many cousins within it. It discusses how the great amount of variations in the plant is complicating the research to study it, and how the lack of research in turn sets off more obstacles in the way of the business. The article even includes a data visualization to show the genetic relation to different samples sent around the world. I found this article particularly interesting because I myself did not even know there was that many strains of the plant, let alone the amount of research going into. The included data visualization was a little confusing to understand however.

      This article is very interesting because it talks about Erie, Pennsylvania and how it would not be able to function without Refugees. After Trump took office, he has taken many steps and actions to try to limit the amount of Refugees to the minimum, so that is why this article stood out to me in the first place. Then it really surprised me that it says that Refugees in Erie represent 18% of the city’s population and that they take up most of the low skilled jobs that Americans would not be willing to do.

      This article is very interesting to me because it talks about the Phillies and I love the Phillies. The article talks about stats from every Phillies team 1883 to the present. It has stats on winning percentage, wins/losses, who the best player was from that season, who the manager was, etc. The article also talks about the top players in team history in terms of WAR. It shows a picture of the top 20 players in team history and it shows the was, starting with Mike Schmidt with a WAR of 106. Overall, this site has many stats on the Phillies for each season that they played in.


      This article is very interesting to me. It provides the stats from the NBA game (Boston Celtics vs. Detroit Piston) on Sunday, February 26th. It’s interesting because it provides many different kinds of data from the game. It gives us the team stats, individual player stats for both teams, field goal percentage, three point percentage, free throw percentage and more. It even provides a play-by-play analysis.


      I have always loved award season. The Golden Golden Globes, the Screen Actors Guild Awards, the Grammy’s, and the BAFTA’s have always awed me. However, there is nothing quite like the Academy Awards. I have always like how glamorous and professional they were (although, the handling off the Best Picture award was far from professional this year). I have always been into “stuff” by the numbers. This is why I came across Guardians’s article entitled “By numbers: breaking down the key facts behind the Oscars.” In this article, you will see some interesting data on the Academy awards. Some examples are comparing television audiences from year to year and also comparing the Best Picture winners to the most successful box office film of their year.

    • U.S. students’ academic achievement still lags that of their peers in many other countries

      This article is about U.S math, science and reading scores compared with other countries. I have heard several times in the last few years that we have been generally sliding down these rankings for some time now, but I never saw the data or proof. Looking at the data in the article interested me because from 1990-2003 the U.S’s math proficiency increased greatly, then it started to become stagnate. I have many questions however on how they actually get this data, like do the schools tested stay the same for each country over these years?

      This article is about a research out of the University of Virginia, cancer incidence has declined in much of the country since 1969 — but not in rural Appalachia. There’re many factors that cause this problem, smoking, obesity, environment(mining), however, the biggest issue is lack of access to care. Looking through this data, we might figure out a way for improvement.

    • This is the artificial from The New York Times, states that the quality of customer care are reducing, and prove the idea with lot of data. The artificial declares that companies are doing all the right things the wrong way, and tell you how companies get to this situation and some tips to solve this problem.


      This infographic by Google tells a clear, interesting story about the customer journey to purchase new electronics. More people YoY have inquired about price while on mobile (+40%). Almost all of smartphone shoppers have actually changed their mind about a retailer or brand after searching more info on about them on Google.

      I can also attest to being one of the users who will go play with a tech toy at Best Buy then go home and order it online. The data also describes users who make a purchase on their smartphone while they test out the same product in store. From a digital marketing perspective, there’s tons of insights you can pull from this data to drive 2017 planning.

      This article, “Digital transformation in the insurance industry”, discusses how the Insurance Industry is centered around data. Whether the data be used for understanding risks and exposures, or submitting and reviewing claims. Data plays a huge role in understanding new exposures, such as cyber liability and defense, resulting from advancements in technology. Only from analyzing data and information will underwriters be able to price accurately and project possible losses.

    • Here is the link I found on the internet that I feel interested in reading it. By reading this article, I am able to know that 9 out 10 academics (88%) say that teaching is the source of satisfaction to them. And only 6 percent of those who claim that they are not happy about having to educate students. Out of the respondents, 29 % of them say that they find teaching more rewarding than research. In addition, this article also provides a clear pie chart that gives me an information of the respondents’ countries, their genders, their roles ( academics or professional staffs) as well as the subjects that they teach.

      This article “Which day will be the busiest for U.S. spring break travelers?” is interesting because it actually shows us the predictions based on the data from the past. We learned in class that the active power of data is the ability to predict things, rather than just to explain the past. I thought that this article showed the active power of data. Although the article is short, I could at least know which places to avoid, or how to make up my schedules.(I might have to spare some extra time just in case of the traffic jams, or other problems because of lots of people.)

      It is so interesting about this article since I like to know more about commercials. In this article, it is talking about the commercial marketing through 2013 to 2021. And it will decreasing in the next few years. If u look through the summary of article, you will be better understand about the link.

    • Social Media Update 2016

      I found this interesting because I never knew that facebook was still so widely used. Lately I hear that its been dry, and everytime I’ve checked mine, it was littered with lots of spam. It was more than double the percentage of the second most used social media site, which was instagram, which was purchased by facebook. It also shocked me that twitter came in at last place since it seems that everywhere I go, people usually promote tweets, which I would expected to be at the top of the list.


      People are so glued with smart phones and can’t stop can’t stop checking, scrolling, clicking and watching. The next probably will be virtual reality.

      This article takes a look at President Trump’s signed executive order to try to make attacks on police a federal crime. The article takes a look at the numbers behind cop deaths to see whether this would truly have an impact and protect cops. Behind the numbers, the highest cause of death for a police officer between 2011 and 2015 on average was traffic-related, which makes the proposed changes, not necessarily effective, as traffic-related deaths are accidental, and the order won’t do anything for accidental deaths. The article was interesting, because the way the media frames attacks on police officers, you would think law enforcement deaths would be on the rise, however, according to the article, they have been on a downward trend for overall for years. Hopefully something can be done to effectively protect police officers, as I am a strong supporter of law enforcement.

      It’s obvious that I am very into health sciences and medicine, so the article I chose discussed current eating habits of Americans and how we are having trouble finding balances between nutrients. According to the article, Americans have started making better choices in their diet (i.e. less beef, more chicken, less fats, more fruits and veggies, etc) however, our consumption of added sugars and fats still continue to be a major part of the American diet. Despite these healthier swaps, we are continuing to eat more food in general, which is why the obesity rates continue to climb.

    • 2016 Was a Big Year for Big Data

      This article is about how 2016 was the year for big data. Article says how big data was very useful to overcome major problems in this world. AI Advancement Rocketed, Tax Shelters Unveiled, Human trafficking taken down to notch, Better cancer research, HIC outbreaks battled than ever before.

      This article is about the new president Trump of the United States trying to build a wall between Mexico and the US, putting harsh tariffs on Mexican imports and its consequences. All of which Trump is doing will make it tough for Mexican economy and this will relatively affect badly to the US. Data in the article shows clearly that throughout time, the weaker the Mexican economy is, the more immigrants will come to the US from desperation. Therefore, this will back fire the first intention of Trump to decrease the number of immigrants coming. A weak Mexican economy will also results in many social issues that would also cause the Mexicans to be even more desperate coming to the US. Without a strong economy, Mexico would not be able to manage problems like pollution and diseases, being a neighbor, the US would then also at risk.


      Last time I was focusing on basketball players and their percentages in shooting. This time I am looking at leaders of the MVP race in the NBA and what makes them good enough to be an MVP. In this chart it shows all aspects of an NBA player. This is anything from percentages to shooting, rebounds, assists, etc. Based off of James Harden stats the number one leader in the MVP race has one of the best records as a team in the NBA. Most of his stats as really higher numbers than other players in the league and shows that he deserves the spot to be MVP this season.


      For this set of data I decided to go with all the data that is compiled for the Premier League in England on their website. The reason that this is such a good set of data is the fact that it doesn’t just focus on the few main stats like goals and assists, but has a wide range of stats that can give you more about different types of players. Not all players are solely focused on scoring goals so this is able to give you that wider range of stats that can show you far more about a player and what they are capable of.


      This article looks at one of my favorite NBA players, Demarcus Cousins. He is one of the tallest players in the NBA, but he leads the league in times his shot is blocked in a game. He gets blocked 1.6 times per game, more than players like the 5′ 9″ Isiah Thomas or Kemba Walker. One hypothesis to why he gets blocked so much is he holds the ball longer than any NBA center, he averages 2.34 seconds per touch. He also posted one of the worst verticals ever at the combine in 2010. His max vertical is 27 inches, which is less than the average by about 3 inches. Both of these factors lead to why he has his shot being blocked, despite how tall he is.

      This article describes the GCMD database, which holds more than 30,000 descriptions of Earth science data sets and services covering all aspects of Earth and environmental sciences.

    • Millennials are the largest generation in America’s workforce and they many feel they have something to prove, hence they must validate their talent, credentials and accomplishments. The impostor syndrome is when you share qualities similar to perfectionists: depression, frustration, anxiety and anger. In fact, millennials struggle with perfectionism more than any other generation. This explains why millennial professionals struggle with the Pareto principle i management (80% of a professional’s output comes from only 20% of the time spent on the project) Additionally, 20% of a professional’s output comes from 80% of the time spent on the project.

    • Report: Inaccurate Data Undercuts Business Decisions

      I thought this article was very interesting because it talks about dirty data and how it can undermine business decisions. Dirty data is something that I feel like I should be heavily aware of because I hope to become a business analyst and that involves making decisions with a lot of data. The article also talks about the need to trust your data. Not trusting your data can lead it to become dirty data. Data, even dirty data, is important for companies to interpret other information.

      This article contains only one data visualization but has an adept analysis of the relations pollution by countries in Asia and how that influences concentrations of smog in the US. I found this article particularly interesting in relation to my Environmental Ethics class, in which our current topic is climate change and the moral question of responsibility. This article, I feel, is a strong connection between the theoretical application of thought and ethics about pollution and the research and data science that leads the way for a tangible resource to create a better model for environmental sustainability.


      This article is about a polling about American voters, their age, and which party they most likely belong in. It talks about how older Americans are more likely to vote Republican than Democrat, which I figured due to having more conservative view points. With the election being very recent I thought it was a refreshing article with relevant data.

    • Data, Analytics, Probabilities and the Super Bowl

      This article is about the data analytics in American Football is quite intriguing. The article goes through the different levels of how they collect data, and use that data to predict better outcomes by including several variables. There process of analyzing the super bowl was what sparked my curiosity in the matter, in the article they compare Matt Ryan vs Tom Brady pass percentages and the areas in which the passes were thrown in. The patterns showed that Brady had a high pass percentage in the same circumstances and moving players. The article explains why they take so many measures and how those measures lead to better movements for the NFL.

    • Americans and Cybersecurity

      This article’s focus is about protecting data and cyber security. The article contains polls regarding Americans opinions on cyber security and how well different sources are protected. In general Americans feel that social media sites are the least safe when it comes to security closely followed by the government. On the other end of the spectrum most Americans trust cell phone manufacturers the most when it comes to security followed by credit card companies.

      This data is about diamond and how they are expected to peak in the next few years, I thought this was interesting because in the next few years women my age will be at the age that they may be proposed to soon. Therefore, don’t be flattered when he gives you a large diamond as a token of his love, they’ll be cheap by then. Some other suggestions may be black opal, Benitoite, or Taaffeite. These precious stones retail for thousands of dollars per carat and could replace the diamond as the new symbol of love.

  • Some quick instructions:

    You must complete the quiz by the start of class on February 28, 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 study guide for the first midterm exam.

  • Here is the exercise.

    And here is the graphic file you’ll need: Philadelphia Area Obesity Rates.png.

    Right-click on the file and save it to your computer.

  • Here is the exercise.

    Before you start, save this Tableau file and the studentloans2013 Excel workbook to your computer. Remember, to save the file right-click on the link and choose “Save As…” (don’ […]

  • It’s great to see you putting the learning to use. Awesome!

  • Leave your response as a comment on this post by the beginning of class on February 16, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]

    • I think that the “Ask Why” principle is the most important from Stephen Few on Data Visualization: 8 Core Principles. I believe that it is most important because I was always told to ask why different things are happening so I could have a better understanding of what I am learning. People can have different views from data visualization by asking why you can also learn from different opinions on what they are visualizing and that is why I believe that this is the most important principle.

    • I believe the most important principle is to “Be Skeptical”. After all of our readings on bad data, the filter bubble, and pros/cons of open data, I have a new understanding as to why it is so important to ask questions and be skeptical. It is important to analyze how the research was done and what it really covers. In class, we often talk about how different sources can come up with completely different answer solely because of different ways data was collected and other details including sample size, how representative the sample is, how it was collected, and more. Just yesterday I saw an article from Fox News that one of my Facebook friends had shared and I was intrigued to I looked at the data. Immediately I realized how different the scales were and how they didn’t include 0 on the Republican side but added it to the Democrat side. When I pointed this out to my roommate, she was shocked, but saw my point and she deleted the article shortly after.

    • The principle I feel is most important with regards to Data Visualizations is “Ask why”. I am a Risk Management & Insurance major and I feel the question “why is this happening” versus just “what is happening” is crucial. If you have an abundant amount of claims coming in you need to ask why. Is your pricing incorrect, has this individual given you incorrect data, have you missed an exposure? The field I plan to work in uses many data visualizations and it’s best to fully understand what is going on and why these things are occurring.

    • All the principles carry their own importance in different ways but the one that really I feel is the core of all science including data science is the principle of being skeptical. All science is rooted in asking questions and testing things to find out what is true. Not accepting the first answer is how we get the next more accurate answer and so on. This has been a main topic in our class discussions as well, when we are asked to look at a data set and then see if we can find another data set that argues it or sends a different message. In order to find what a series of data is trying to tell us we need to be skeptical so it allows us to ask why which then goes into the other principles of analyzing a data visualization.

    • From my point of view, each principle plays its important role and all of them support each other to make a good data visualization. Nevertheless, if I have to choose one of them to be considered the most important principle, I would say the “Be Skeptical.” The reason why I say that is because in order to have an accurate result, we definitely need to test our first answer again, and as a result, we are either 100 % sure that the result is true or we might find a better answer for it. If we do not question or test our first conclusion, it is likely that we will have a wrong analysis. There are so many cases/dates which carry two or more opposite viewpoints, so in order to state a right conclusion, we need to make sure to re-test the first result in order to have the most accurate answer. It is undoubtedly true that exploring any further is tool hard, but we do not re-test seriously and carefully, we might have a wrong statement.

    • I think that the “Be Skeptical” principle is the most important, though all carry their own values and importance. Going back to check your own work is such an important step. Aside from just business (since my knowledge of it is limited), being skeptical in general is good for discovering new information and creating hypotheses. As a science major, the majority of what I do is questioning and testing; over and over again until I have a solid support for the point that I am trying to make. I want to go into medicine, and when testing new medical technology/techniques, clinical trials are done where they test on individual patients. As time goes on, they look for errors in their work and adjust accordingly, then go back to testing. If they are not skeptical and do not adjust their methods, then lives could be lost and there would be dire consequences.

    • I think that “explore” is the most important principle. Imagine the how people lived back in 1900s’ and imagine our society today. What are some of the major differences besides racism and the change in community. The main difference in my perspective is the idea of technology or more precise the “World wide wed”. Which was introduced during the late 1900s’. And after the creation of the internet imagine how many things people were able to learn online. In today’s society, especially young adults get almost all of their education from the internet. Think about how much a person spends time on the internet. Either its social media, or news articles and etc. Explorations comes naturally these days. Considering the number of things there are on the internet advertisement, people can stay on the internet all day and can never get tired of it. Exploring is super important these days. Being skeptical or asking why is already included in the idea of exploring if you think about it. Why does one explore? The answer is Because it was out of curiosity. And curiosity is the main aspect of “being skeptical” and “ask why”.

    • I feel that “simplify” is the most important principle of the eight. Data visualizations are meant to tell a story. No one cares about extra add-ons or a bunch of fancy side notes. People want to know why you are making the visualization and what it is saying. Some people feel the need to demonstrate their intelligence by making data visualizations extremely complicated and thorough, but a lot of the time it ends up being hard to read because it is so clustered. If you simplify the data it will be more productive.

    • From Stephen Few’s 8 core principles on Data Visualization, I think simplify is the most important principle. If a data visualization is not relatively simple, more people will not look at it or use it because they can not comprehend it. Also, if a visualization is not simple, it is hard to achieve the rest of the core principles and the purpose of the data visualization is missed entirely. Being relatively simple is the keystone to a good, useful data visualization.

    • I believe that the most important principle from Stephen Few’s 8 Core Principles is the “Be Skeptical”. I believe that you should always guess your work, even if you think you’re wrong. Even if you think it is right, you should always go back and check your work instead of just using the first response you see. I feel that you should always be 110% confident in the correct answer, and being skeptical helps you to dig deeper and discover the truths. If the visualization looks off and you’re skeptical about it, odds are the visualization is wrong. Good researchers always keep an open mind, and by being skeptical you can keep an open mind. Overall, I believe that being skeptical is the most important principle in visualizing because it keeps you honest about your work and data.

    • I feel that viewing data diversely is the most important of these principles. As discussed in class, we apply bias to data, both when presenting it and when consuming it, and it is crucial to be aware of this as we seek to convert data into useful currency. We have demonstrated that one can find data to support any viewpoint they may wish to confirm, so in the application of data, we must seek to understand all perspectives and all messages in order to make sound decisions.

    • In my opinion, the most important principle is to be skeptical. All too often do people take what they are reading for face value without questioning the methods of data collection or author’s purpose for the data visualization. It is important that people know the correct facts and not just the first result that appears. As last week’s in-class assignment showed, different data visualizations can tell different stories, so looking at data skeptically is crucial to gaining the correct facts.

    • I believe that “Simplify” is the most important out of those 8 core principles when it comes to data visualization. When it comes to data visualization, people will most likely only examine it if what is presented to them is simple and easy to understand. If the representation is too complicated, people tend over-look it unless they are truly interested in that specific topic. When data is often simple and unique, it captures the attention quickly, therefore, would be likely to be more effective.

    • I think the “Simplify” principle is most important because of how relevant it is to effectively understanding data. The data should be clear enough that anyone would be able to understand it without having to ask more questions about the data. If the data was unclear, it could cause viewers to become confused and the overall story trying to be told would then be harder to tell. In addition, the principle implies that the data would be setup in a way that it would be easy to follow. Such a factor is very important, especially when there is a considerable amount of data being used during the visualization.

    • I think the most important principle of data visualization is “Simplify”. It sounds like it’s easy just an output of the essence of data, however, how to define the the essence and how to provide the right visualization that can let everyone know what your talking about is really difficult.

    • I think the most important principle is “simplify”, in the result of data visualization is the starting point of analysis and other research. We can not do well in analysis and the following step if we use the data visualization which is too complex or too simple. That is if we do not know how to simplify the data appropriately or can not choose the right form to visualize the data, we will have a bad beginning and can not do the research well in the end. This is the reason why I think “simplify” is the most important principle.

    • I think the most important of the 8 core principle is “view diversely”. When we were talking about the filter bubble, I realized that a lot of people hear only what they want to listen, and very rarely listen to the other side of the argument. Also, when we were in class, we talked about how statistics can be biased. By listening to both sides, I think that both sides could learn more from each other and form better solutions to better problems.

    • In my opinion, the most important core principle is ” simplify”. Without over doing it, it is important to simplify the data in a way that the person looking at it can clearly make out the story it is trying to tell without working too hard.

    • Out of the eight principles from the Hoven article, “Simplify” is the most important. Without being able to understand an important visualization, you won’t be able to use it. Simplicity is the key for visualizations with a lot of information in it. It needs to come across as simple, easy to read, and appealing.

    • Out of the eight principles I think the most important principle was the “ask why” principle. I feel like there is never an end to asking why because you can always keep asking the question why. How could the world’s unanswered questions be answered without being asked why first. By asking why you can answer questions and find out more about something that you want to know about.

    • I think “ask why” is the most important principle. Just pulling facts from the data is almost useless unless you really look into it and try to figure out why something is happening. Florence Nightindale didn’t just look at the data she collected, she wanted to figure out what the data meant and how she could help the world with her findings. If you don’t understand why something is happening you will never know what is actually going on.

    • I believe “Simplify” is the most important of the 8 core principles of data visualization. I think that if you are able to simplify the data, then more people can access it and interpret it. And since you are able to simplify your own data, that shows readers that you fully understand your data and can lead to the data being spread across the world more rapidly. By simplifying the data, we can help more people interpret it and then we can use the data to solve a certain problem, if the data shows one. I believe “simplifying” is the key to good data visualization.

    • I think the most important of the 8 principles is to “View Diversely. Although people look at the same data it can provide two different stories. When looking at data it is important to think of it in a different perspective because that can add different pieces to the overall story.

    • I think “Ask why” is the most important element, because the correlation of variables is a significant role of any data set. Therefor, understanding how one variable affects another is very important. Why it happens like that can be a whole story of many data sets.

    • I think out of Stephen Few’s 8 Core Principles, simplify is the most important one. It is important because people would easily loose interest in the date visualization if it is too complicated and they cannot figure out what is going on after awhile of looking at it. Also, simplifying something is harder than to make it more complicated and only people who truly understand the data would be able to simplify it. Also, simplifying a data makes it available to more and more people.

    • I think the most important principle is “Simplify”. Whenever I deal with data, I want to show how complicated it was to gather those data, and what kind of processes I went through. That’s why most of my data visualizations become really complicated. So that’s why I think the ability to make data simple, knowing what to add and what to subtract, is the most important factor of making data visualization easy to understand.

    • The most important principal, as Hoven mentioned, is “be skeptical”, from my point of view, skeptical is the embodiment of the cautious, To be precise, when i read some big data, i will first observe the reliability of the data, which can let me be more comprehensive understanding the nature of the data, because I try to know every aspect of the data.

    • Asking why is paramount. Irrespective of context, the driving force behind any decision comes down to asking why about whatever the subject. Someone could apply data science to create a presentation using data collected form decades of intensive reasearch done by business and medical professionals to develope a model for efficient and cost effective health care but they never do it just because, so why do it at all? There is always a why question involved. Why are so many people uninsured, why are fewer companies offering health care benefits, why is there a disjunction between the cost and reception of medical treatments? Why aren’t more individuals and organizations aimed towards improving a domestic epidemic? The why question is imperative to aligning effort with the desired audience in addition to the development of a function model. What’s more is to answer the questions “why is this important and why is this topic relevent” before the audience has a chance to ask it. Keeping focus on the “why” helps to maintain effort in developing the “how”.

    • Stephen Few’s principles work together to create sound data visualizations that effectively communicate what the data is saying. If I had to choose the most important principle, I would say its “Attend”. Oftentimes in data analysis, it’s very easy to overlook tiny details that you don’t even know are important until it’s too late. Also, it’s important to be able to segment data in order to identify trends in smaller samples with narrower perimeters. The second most important, in my opinion, goes hand in hand with “Attend”. The principle of, “View Diversely” is integral to gaining insights from varying perspectives and therefore attending to it by applying and testing it on related data. I feel like these two principles can be the most powerful when used in tandem.

    • I think that bit is most important to “be skeptical.” Especially with how easy it is for us to just google search things today, people tend to just believe whatever hey see on the internet. In reality, anyone is able to put whatever they want on the internet, meaning just because you find it doesn’t necessarily mean that it is true. You need to find reliable sources and other sources to back those up to be sure it is really reliable. If you believe everything you read just because you see it, you’ll realize very quickly that you would start to believe a lot of wrong information.

    • In my opinion, of the 8 core principles that Hoven talks about I feel as if “be skeptical” is the most important. Currently there is too much false data and news where it is hard not to remain skeptical from my point of view. You have to make sure your information is correct and unbiased, it is very easy to skew data to make it one-sided. I believe that one should always be skeptical and ask plenty of questions when facing data.Being skeptical helps us comprehend the true value of the data.

    • I believe that the most important important principal of the eight that are given is “ask why”. Without this principal, we are not really giving any purpose to the data, and it is merely that. If we add a narrative to the data to prove a certain point true or false, then the data actually has some meaning and is not simply data which in itself is not very helpful to an individual. It can also lead you to take certain actions based on the results will possibly make the world a much better place. None of the other principles can attest to that.

    • The most reflective principle from Hoven , in my opinion was Respond. The respond principle is the key into why the data is being analyzed and structured for a story. The response allows the data to be shared in order for “global enlightenment”, without the sharing process the work being done for the story was meaningless. The principle of Respond gives the story a purpose for action. The respond principle is what makes the data unique and also provides challenges for the production of the analysis. Without the Respond principle the other principles would cease to exist.

    • I think the most important principal of the eight is “simplify”. Whatever your data is it should be simple and sophisticated that each and every people can understand. Especially in the data world simplifying data can be very useful to others. So I think “simplify” is the most important principal.

    • I believe that the “Explore” is the most important principle of data visualization. I think this because I believe that the human brain works best when it is allowed to interpret and question free of constraints. If we wanted our data to be simply a message that the reader is supposed to understand we would use simple text, but since we are trying to get the reader to explore, we use all the nuances of data visualization that make it a data visualization.

    • I believe that the principal of , “viewing diversely ” is the most important because it highlights the most crucial part of data analysis that answers ,”how does this all fit together?”. A diverse perspective allows the creator of the visualization to make his/her project with the most relevancy in consideration of a vast audience. The ability really see how certain data sets fits together, in vast consideration of relevancy , sets up an environment where innovation can seamlessly flow.

  • Some quick instructions:

    You must complete the quiz by the start of class on February 14, 2017.
    When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to […]

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