Min-Seok Pang, Ph.D.

A KPI I use on a regular basis while at work would be how happy the employees are at my job. This is specific and measurable because happiness can be measured through surveys. It’s relevant because being happy at my job is important for your health and how hard you work depending on how happy you are at your job. And it is time-variant because I can survey my co-workers and my happiness over a shift, a week, or over a year span.

A KPI I use regularly is average minutes/per mile while running. I am training for a half marathon so my goal is to have an 11:30 min/mile (yes I’m slow) pace over the entire 13.1 miles. I know that if I am running at this pace from the beginning I cannot slow down at all for water or fatigue. So I am always checking in on my pace early on and shoot for a 9:30-10:30 min/mile pace for the first half of my long runs so I can slow down if needed in the last few miles for fatigue or walk for a brief period every couple miles to drink water without going over my target overall race pace. This KPI follows the SMART criteria because it is specific, measurable, attainable, relevant to the success of my training, and time specific.

The KPI most related to our students is GPA(Grade Point Average). It is specific and measurable since it is calculated by your letter grades and credit hours. It is achievable, one can study harder and seek for help in order to improve her grade. It is relevant to one’s future career because most of companies would look at your college GPA on your resume. It is time-relevant because you can calculate your GPA over one semester or over an academic year, even up to your whole university life.

A KPI I use on a regular basis is Lifts/Average At Strength Level. It is specific because it can set specific standards in pounds for the next gym session. It’s measurable because it uses my direct input in pounds, which are clearly labeled on weights, and compares it to a database of other users. I use it often to set attainable and measurable fitness goals for each time I’m at the gym. It’s relevant as it charts my progression through various fitness levels and gives me a clear idea of how close I am to my ultimate goal. This KPI is time-phased as I use it to set targets once or twice a week and log my actual points attained.

A KPI that could be used on a regular basis is the amount of money that I spend in a certain time period. It is specific and measurable, because there is an exact amount of money that I spend in a time period and I can measure this precisely. It is achievable, because I can either reduce or increase my spending after looking at my expenses. It is relevant, because expenses affect the amount of money I have left to spend. It is time-variant, because I can track my expenses over any given period of time whether it be a day, month, or year.

A KPI that I regularly use is keeping track of how much money that I take out of my debit account per day. PNC Bank makes this fairly easy to do through the Virtual Wallet app for iPhone. This follows the SMART criteria because I can set a specific amount of money that I want to spend per day. It is measurable because I can keep track of the funds that I deposit and withdraw from my account. It is achievable because I modify my spending habits based on how much money is left in my account or how much I received from my work in my bi-weekly check. It is relevant because my funds are very important in how I live my life. Lastly, it is time-specific because I can check my balance every minute, hour, or week if I wanted to.

A KPI that I use on a daily basis is the amount of money I spend a week using my debit card. It is specific from week to week and month to month depending on the amount of money i budget to spend. I set a specific amount of money I will be spending for that week. I use the TD Bank app as well as a personal finance app called Mint to measure and keep track of how much money is leaving my bank account and what it is going to. It is achievable because I set a realistic amount of money that can be spent on food, rent and other living costs and modify that from week to week. It is relevant because I need money to live and managing my money ensures that I won’t run out. The amount of money I have in my account dictates what I can and can’t do and what I can and can’t buy. It is time specific because I can look at it over a week, a day or a month.

A KPI that I use regularly are my current grades. It is specific to how well I am doing on the assignments in my classes. It measures point values on assignments, exams, etc. and percentages. I can set achievable goals for myself; for example I can aim to get 100% on all of my homework assignments. It is relevant to my success in school and, lastly, it is time-specific as it measures grades by semester.

A KPI that I use regularly is body fat rate. It can help me to know my body fat and regulate my body fat in a constant level when I workout. It’s measurable to use the machine to measure it. It’s useful when I want to achieve different aims in muscle-increasing period and fat-reducing period. It’s time-phased, usually one month, three month or half year depends on different goals. For instance, if my body fat rate is 15%, and I want to achieve 10%, I will set an achievable goal, usually I will set one and half month to achieve it. The KPI is an essential indicator to reach different goals.

A KPI I use on a daily basis is win rate when I play video games. I use this KPI to determine if in however long of a set of games I am playing well or struggling to perform. This is specific and measurable, on the client of the game their are KPI that calculate this information already, and furthermore there are websites that can do it as well. It is achievable because if I am under performing then I know I need to focus more and try to unpackage what I am doing wrong. It is relevant because I want to be a top performing player and win a lot of games. It is also time variant I can look at my winrates over the course of a few games, over a year, or over the entire time I have played the game.

A KPI I use on a daily basis is view count on Soundcloud. Soundcloud, is a music platform and it gives me the amount of plays I received by users in the last 24 hours. Depending on that number I can tell which days are my most successful. It allows me to improve that number by correlating it with what I did during that day.

A KPI that I use every day is an App called “Sleep Cycle” which tracks my sleep patterns and wakes me up during light sleep period. The app gives pretty good analysis of your sleep quality through graphics and different factors that impact it. You can also listen to your snoring! The app has charts that help understand your data easily, and help you improve your personal sleep cycle.

The KPI that I use every day is “Precipitation %”; provided by the weather application “Weather Underground”. The application displays a line graph which visualizes the “Precipitation %” by hour within a day or by day within a week. The likelihood of rainfall is a specif aspect of weather conditions, it is measurable as it can be expressed as a probability, and because of the application’s visualization one can gain a realistic expectation which provides achievable results i.e. should I wear a rain jacket or not?

I have made one of the mistakes listed in the articles and it was mistaking the data type. This happened when I was inputting data into excel and I was trying to input dates into Excel, but the program confused it as integers, so I had to change the way I inputted the data. Another mistake I made was not backing up my data or making a copy of my original data before making any changes. It is important to back up data, because you can use the original data to compare to the new data you have altered just in case you have made any mistakes. Also it is important to have a backup copy, because if you make any mistakes or somehow lose your new set of data then you always have the old copy to refer to.

I am guilty of hitting the “ok” button without reading the dialogue box. So far no major consequences have happened, but its only a matter of time. I also have had the issue with leading zeros. I am continually updating a contact spreadsheet and we have quite a few contacts that are located in New Jersey, which initially I was unaware that the leading zero would be a problem. So the first few times I was updating, I didn’t take notice to the fact they were dropping off until one time I looked up as I hit “enter” and saw it eliminate the zero. Which led me to have to scan through all the zip codes for New Jersey and correct the ones I had entered during the month. For our purposes I was able to just adjust the cells which needed the leading zeros to a “text” format which allows for the extra zero.

I have made a mistake listed in the article, it was missing the data type. It happens when I put in a date, but it changes to an integer. Many similar data errors have occurred, although it is a very simple fix. Another mistake I’ve made on Excel is copying formulas and miscalculation. I’ve miscalculated and copied formulas into the rows, but sometimes I miss a cell that I didn’t notice before. I’ve also made the mistake of not backing up my data or making a copy of the original before making major alterations. You should always back up your data because it is important to have the original to look at in case you need to fix a mistake in the new data.

I think that mistake 3 “Failing to do a full backup first” is the most common mistake and the easiest to avoid. It is very important to always back up all sets of data. Without a backup, you would have to start from scratch to recollect the data which can be time-consuming or very difficult to do, especially if you have a deadline. One time I made this mistake, I was working on a 5 pages paper for one of my classes, and when I was in the middle of the paper, the computer abruptly shut down, and I wasn’t able to recover the document afterward. This mistake thought me to always save whatever I m working on every couple minutes.

I think I make the mistake of using the wrong data type the most, which can be a critical error. However, I believe the most critical thing to do is to make a backup file before beginning. Anything can and will happen, as this has happened to me in the past. I am also guilty of not making checkpoint saves which can lead to a critical loss in the work I have achieved in the file being worked on. Your work is not worth anything until the finished product is completed, and if anything happens to the work flow to hinder achieving the final product could prove to be incredibly detrimental to even achieve the task at hand.

I have a horrible habit of not backing up the work that I do. In one case, I had been working on a statement of values worksheet for one of my company’s largest clients. Naturally there were thousands of rows, and the project was going to take me a few weeks to get through. There were multiple times where I realized that I had either been pulling values from the wrong database or had put the values in the wrong section of the spreadsheet only after I had saved it over the last copy. It was very time consuming to go in and have to correct all of these mistakes by hand instead of just opening up the backup copy.

Due to my lack of time with Excel prior to this course, I have not made any of these mistakes within that program. However, many of my papers within Word have been corrupted due to me not properly backing them up. For example, during a timed essay my senior year, I had to re-type 400 words because my work had not been properly backed up. I believe this is the most important step in working with data because if not properly backed up, tons of data can be lost and many things will have to done over again as I have had to do many times in Word.

I think it is important to avoid mistake #3, which is working on a database without doing a full back up first. This is a very important step because many things could go wrong that would compromise your work, such as a computer malfunction or a program malfunction. These could cause you to lose your work and if you haven’t backed it up you will have to start all over, which is very time consuming and frustrating. Starting over could also cause you to rush, potentially resulting in more mistakes.

I haven’t made any mistakes on this list, because before I start working, I noticed some of them are really important. I agree with the most of people, mistake #3 is very common and may lead to frustrating result. It won’t harm you to save and backup more! Do it as frequently as you can. There is one convenience to backup data, it is not only preventing your data being corrupted or system crashing, you can also go through the data before each processing and analyzing step (I recommend create different backup files on each steps). That could be useful because you can cancel your choice at any time, or even combine the old data and the processed data together if you want!

I have made the mistake of clicking “yes” without reading what I am agreeing to. I am also guilty of working on a database without doing a full backup first. I have not had any major issues so far, only minor errors that I could remedy easily. However doing this is a huge risk; it only takes one time to make a huge error. These are bad habits that I need to break.

I have made several mistakes in the list. The most usually mistake is put values in fields that are supposed to be pointers or references. It usually results in a confusing date. I am careless. Sometimes when I have done a spreadsheet, but I feel the data looks strange. After I review it, I find it’s because I put values in the wrong place. It’s a mistake easily to make.

I have clicked ok before without reading something before. I think this is an easily fixable mistake and something that I have to pay attention to.

One particular mistake I can remember is the time we were upgrading all the surveillance cameras in various garages from analog to IP. I was responsible all technical aspects of this project and for placing the switch ports,mac addresses ,camera locations ect into our shared spread sheet.
And I like to ascend all the columns in doc from a-z, but on this occasion I only did one column and that column happen to be the Mac address. so in the end when we went to import the info our locat data base the equipment and the locations did not match.

Luckily one of our Field tech caught the problem before the upload and I was able to clear up the issue before it became a issue.

Sourcegraph raises $20M to bring more live collaboration to coding
I found the above article pretty interesting. It is about a new development platform for programmers. It’s a way to collaborate and see what other people are working on in a way to level out the playing field for those in coding who don’t work for the major tech companies. The whole goal is to accelerate the speed of innovation and increase efficiency. This is interesting to me because I am in a application development class and this ties into that, as well as the typical idea of a programmer is someone working individually and this would be a way to collaborate on edits to code.

URL: https://www.nytimes.com/reuters/2017/10/02/business/02reuters-usa-stocks.html

The article I read discussed the analysis of the Wall Street Stock Market by utilizing data to compare the prices of stocks and bonds. The data compared the prices of current stocks and bonds to their past prices and determined that prices in the current quarter are higher than they were in the past, which means that there has been improvement in the economy. The article discussed the Dow Jones Industrial Average, S & P Index, the NASDAQ, and the NYSE and compared the respective prices of the stocks within each index to each other.

https://www.cbssports.com/fantasy/football/news/week-6-fantasy-football-rankings-injuries-dominate-the-headlines-ty-montgomery-marcus-mariota-tom-brady-improve/
Fantasy football is a very important part of my Sunday. I rely heavily on data analytics to decide who I will start or bench week in and week out. For example, companies like CBS and ESPN compile data from all facets of football to give the projected amount of points for each player. They take into account things like passer rating, defensive stops, number of field goals made, etc. I think fantasy sports are some of the best applications of data in the modern world because so many people now rely on it so they can now be part of the game also.

http://news.mit.edu/2017/first-open-access-data-large-collider-subatomic-particle-patterns-0929
Data collected from the Compact Muon Solenoid experiement (a main detector in the Large Hadron Collider) was released to the public through the CERN Open Data Portal. In the field of particle physics, open data has not been widely used for fear of amateurs looking at the data and drawing the wrong conclusions from it. After releasing the data though, Jesse Thaler an associate professor of physics at MIT, was able to use the data to, for the first time, reveal a universal feature within jets of subatomic particles, which are produced when high-energy protons collide. “This feature can be used to predict the energy imparted to each particle as it cleaves from a mother quark or gluon.”

https://www.economist.com/news/finance-and-economics/21729464-drone-insurance-showing-way-huge-volumes-data-make-real-time-insurance

I found this article from the Economist titled “Huge volumes of data make real-time insurance a possibility”. The article talks about how drone insurance startups are showing the way for the on-demand insurance. With the increase of drones use, insurance startups such as Flock and Verifly rely on their apps to gather a wide range data to offer a quote to the pilots as well as how they can reduce risks. The data range includes weather forecast, nearby aircrafts, local topography, and other factors. Also, car insurance startups such as Root uses their apps to offer insurance based on minute to minute behavior of drivers. According to the article, this type of insurance is expected to increase with the increase of autonomous activities.

https://www.washingtonpost.com/news/monkey-cage/wp/2017/10/11/politicians-take-a-negative-view-on-trade-deals-even-the-ones-they-voted-for/?utm_term=.867d92dca0c8

I found this article, (written by a Temple Political Science Professor), to be of interest. According to the data compiled by the Wisconsin Advertising Project, presidential/mid-term campaign advertisements (2002-2008) were overwhelmingly negative on trade issues, despite the fact that most congressman who ran negative ads voted for pro-trade agreements. The discrepancy between public perceptions of free-trade and the actions of politicians suggests that the political cost of running pro-trade advertisements are too high, even though research demonstrates that public opinion would be influenced positively. The research challenges existing voter-driven models of trade policy, and calls into question the salience of trade policy.

https://www.theguardian.com/environment/2017/sep/26/national-park-plastics-bottled-water-ban
This article discusses the ban on plastic water bottles in national parks and the negative effects of the reversal of this ban. I found this article interesting because it mentioned that this ban saves 2 million plastic bottles from being discarded in these parks, which equates to 111,743 pounds of plastic. This amount of plastic is the equivalent of 326 barrels of oil worth of emissions. This lifting of this ban will have serious negative impacts.

https://www.economist.com/news/business/21729781-high-growth-kind-has-rebounded-sharply-lows-after-great-recession-american

The article talks about entrepreneurial activities in United States. Data shows that in 2015 the number of new firms created in America was nearly a fifth lower than the annual average between 1998 and 2008, and just half of companies founded in 2015 will survive to 2020. However, a recent report suggests that these numbers may be masking more encouraging deeper trends. The 30,000 young and ambitious American firms have a disproportionate positive impact on employment and economic growth. Despite the overall decline in business formation, their high-growth-entrepreneurship index has now rebounded to match its pre-recession value. Furthermore, entrepreneurship is starting to spread beyond the main startup hubs. Although California, New York and Massachusetts account for 3/4 of venture capital investment, the best performer in Kauffman’s index among the country’s 50 biggest metropolitan areas was Provo, Utah, a city of 600,000 people south of Salt Lake City.

https://fivethirtyeight.com/features/is-your-dd-character-rare/
I found this interesting article. It is about the game Dungeon&Dragon. From this data we can see that most of people are tend to create the character related to themselves or at least from an existed prototype. For example, the most used type of character is Human Fighter, which is the most common character. Other common instances like “Dwarf Fighters” “Elf Wizard” or “Elf Druid”, these types of character appears in many movies and games. I think this data is deeply related to psychology stuffs. Maybe game and movie companies could use these data to create more attracting works to make more profit. Altogether, most of people are still create and imagine things based on existences and stereotypes.

https://fivethirtyeight.com/features/mass-shootings-are-a-bad-way-to-understand-gun-violence/

This article was interesting because of the perspective it took on mass shooting incidents in America versus in other countries. The article said that “Between 1966 and 2012, there were 90 such incidents in the U.S. The next four countries with the most mass shootings had 54 combined.” It was also interesting because the article talks about the reactions to these mass shootings. Other countries do not react the same way that America does to gun related incidents. In the USA, gun ownership is protected by the Second Amendment, whereas, it is not in other countries, which is why there is so many more incidents here.

https://fivethirtyeight.com/features/kelvin-benjamin-is-the-nfls-clutchest-receiver/

This acritical peeked my interest because Calvin Benjamin a big receiver 6’5 250 and he is still one of clutch wide receivers in the NFL even though he Is not the most athletic looking in his pre-game warmups. based on the number shown in the graphs in the article his numbers actually improve over the course of the game. Basically he get stronger as the game get longer, and that’s hard for someone of his size to do so its going against the norm. and that would make him interesting to watch in the future games for me.

I think the most important principle from this article is ‘Simplify’. In order to show people a good data visualization, it should not be messy and difficult to understand. The first thing people look at is the color or word that pops, and if there are too many colors and other things going on in the data visualization, it will confuse the viewer what the important part of the data visualization is.

I think the most important principle from this article is ‘Simplify’. In order to show people a good data visualization, it should not be messy and difficult to understand. The first thing people look at is the color that pops, and if there are too many colors and other things going on in the data visualization, it will confuse the viewer what the important part of the data visualization is.

I would say “ask why” is one of the most important principles. Understanding the context behind why the information is presenting itself in the manner it is, is how we are able to make decisions based on the data. One of the main objectives of data analytics and data science for businesses is to transform the data into actionable insights. So knowing why, would get you closer to this.

I think “attend” principle is the most important principle. A good data visualization should not focus on too many topics, thus there would be a most important topic in one data visualization. If the data visualization provides too much details that have a little to do with the main topic, the visualization would lost its focus. People who cannot attend the data well may lost interest or get boring about the visualization quickly, because it would be difficult to understand what the visualization want to tell the readers. In some worse situations, the data could even be misunderstood. Only if people could attend the data easily and pay attention to the main topic of the data, the visualization would be considered good.

I think “attend” principle is the most important . A good data visualization should not focus on too many topics, thus there would be a most important topic in one data visualization. If the data visualization provides too much details that have a little to do with the main topic, the visualization would lost its focus. People who cannot attend the data well may lost interest or get boring about the visualization quickly, because it would be difficult to understand what the visualization want to tell the readers. In some worse situations, the data could even be misunderstood. Only if people could attend the data easily and pay attention to the main topic of the data, the visualization would be considered good.

I think “Explore” is the most important principle on data visualization. Having the ability to create something like a heat-map or other type of exploratory data visualization can help us discover things we might have had trouble discovering otherwise. This explore principle would come in handy for problems that are hard to solve by directed analysis or problems where we can get stuck trying to find one thing when another is more important. Explore also blends into the principle of viewing diversely. Without the exploration of data, it could be very easy to always stick to the same methodologies and same viewpoints.

I think “ask why” is the most important principle because people would never be able to fix any problems discovered by data analytics if they did not know why a certain trend was persisting. The goal of data and specifically data visualization should be to answer questions and not simply place a prompt in front of you. If businesses and organizations did not know the “why’s” of their data, they would certainly fail. Knowing the “why’s” creates an outlet for more success.

I think the most important thing in data visualization is to “compare” the data. To understand a graph, it is important to compare data over a period of time or between two or more things. When comparing data, it is very helpful to have it all in the same visual to make it easy for the audience to see things in perspective.

I believe that the “Explore” is the most important principle, because it is the point where a person is able to analyze the data and gain more information from the data itself. It is one thing to gather information, but you can only do so much from just gathering data. After gathering the data you need you must explore your data and dig deeper and understand the patterns and trends that exist within the data if there are any. Being able to extract observations and facts from your data allows you to gain a deeper understanding of the situation at hand compared to if you were only glancing at your data. By exploring the data, you are understanding the story behind the information and possibly gaining new information from that data.

I think the most important of the eight principles is “ask why.” Collecting the data is easy enough but it is harder to know why the data is the way it is. The goal of data visualization is to tell the story of the data, and to do that requires an understanding of what the data is, where it comes from, and why it is the way it is. Having a knowledge of why it’s happening allows businesses to find trends and predict what will happen in the future.

I think “Be skeptical” is the most important. If blindly accept all answer form data, it’s easy to be misguided. And it will lead to a lack of innovation. Nowadays, there are too much data encompass us, it’s hard to said whether the data we get is accurate or not. Be skeptical is an essential characteristic, it encourages people to question the result and motivate people to pursue new knowledge.

I think one of the most important principles would be “ask why”. Being able to visualize data is helpful of course, but it is very important to know how and why that data came to be. Knowing the how and the why about data can lead to the ability to solve problems or make improvements.

I think the most important principle out of the eight principles is “be skeptical”. This is because as stated in the article we jump to any answer we are given immediately rather than second guess to ensure its accuracy. If the answer is not accurate than what is the point of putting in the effort to arrive at an incorrect answer, it is better to continue to ensure the accuracy of the conclusion drawn from the data by being skeptical about it.

I think the most important Principle out of the eight is to be skeptical. I think you should question information presented to you. We have to keep in mind that this same input has come from someone else’s research and it might not always line up with the facts depending on what their true adjective is rather than basing it on accuracy of data to be displayed.