Ermira Zifla

http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html —-Bad One
This is our graph that could be improved. It only ranks up to 50 films. It shouldn’t use a map, it should just use a bar graph of how many people rent netflix films ON DVD. The map is confusing because it limits itself to 50 films and the data barely changes from zip code to zip code.
http://www.nytimes.com/interactive/2009/07/31/business/20080801-metrics-graphic.html —–Good One
This is a good graph about Everything Americans do throughout their day. It’s a Time Survey.
John Nilsen, Anthony M, John W, Vincent P, & Nick

http://www.nytimes.com/interactive/2010/04/28/us/20100428-spill-map.html

Bias is something that cannot be eliminated from a data and is part of the data without even realizing it but what we can do is reduce the bias in the data. One example i can think about is choosing my major where in i found that comparing the salaries of a doctor and actuary, a doctor would make more money and help me lead a better life but i was biased towards the fact that since i liked math better i chose actuarial science and not pre-med. The way i could have counteracted my bias would have been by taking larger sample data and finding out about new variables such as people doing the majors they like vs the highest paid majors.

I learned that bias exists in all data and in not something that can be easily eliminated in researching big data. I have encountered this bias as someone who’s hobby is in making comparisons of audio equipment. Many individuals review audio equipment objectively, but there is sometimes brand loyalty involved when making comparisons between different brands. One bias I have seen is the persistent dislike for the Beats brand among the “audiophile” community. Many audiophiles pride themselves in providing audio reviews regarding home and professional audio systems and headphones. Some have been loyal to their favorite brands and have left biased reviews to make their opinions seem like the best.

Interested in the space to begin with, you might recall me bringing up Facebook’s initiative of Internet.org. While I found the ‘company campaign’ of sorts to be extremely interesting, I was continuously searching for the reason as to how Zuckerberg was able to recruit some of the brightest minds that the space has ever seen to work on such an initiative. After class, I was able to understand the reason. Facebook’s mission is to ‘connect the world’ and with the signal problem and consistent biases when researching big data, metadata and so forth… it’s impossible to fully achieve. While bias seems inevitable, this project plans to wipe it away. The takeaway here taught me not only about what entrepreneurship is all about, but that this issue is something that I would want to potentially work to solve later in my career.

During last week’s discussion, the thing that seemed most important to me was how much effect bias has on a data set. When selecting certain data or choosing variables, more often than not you may make a choice because of some biased decision and not even be aware of it. One example of how I used data to make a decision would be from my fantasy football drafts. Even after using previous years stats and “professional” opinions, some of the players I chose were because of them being on certain teams I liked or even from doing well on my previous years teams. I wouldn’t say any signal problem occurred from this and I also don’t believe there would have been any way to counteract this bias because of the circumstances.

My most notable takeaway from our discussion was learning about the filter bubble. I always thought of the tracking just in terms of shopping and browsing the internet for new products; so, I never thought it was a big deal that Facebook was suggesting the same or similar sites to those I have visited. I now am thinking of the bigger picture – when I am doing research about politics or for school, the internet can filter what I’m seeing to show me only sites/content similar to what I’ve seen in the past. This is not how I want to educate myself of broaden my knowledge. It’s interesting that there are browsers developed to specifically prevent this.

Bias is almost impossible to avoid. When asking a question for a poll or finding data you need to make sure the question is asked correctly so you don’t get biased answers and you have to have a sample size to represent the population correctly. Signal problems are undercutting some of the population and not representing them correctly in a poll and this is hard not to do. I feel like this will be the hardest to control the signal problem because its hard to represent the whole population by just polling or collecting data.

The important point to take away is that bias is inherent in data collection because where you collect the data from is biased towards the person who is collecting the data. This is not the only way data can be biased, but the general point is that even in data there can be bias. This does not mean that the data automatically becomes bad or useless, but it is important to remember this when considering certain data sets. Personally I was collecting data for my spending habits for another class. I was using taking my income, my expenses and then categorizing them and dating them. This data is useful for me, as it tells me my income and expenses in specified categories and dates, but this data would be useless for trying to categorize the entire student body’s spending habits as it is personalized for me.

The article I read was titled “Significant Digits Tuesday.” Link: http://fivethirtyeight.com/features/significant-digits-for-tuesday-sept-6-2016/

This article derives from FiveThirtyEight, and is filled with a bunch of random, unrelated, interesting and concerning data. The article briefly lists a fact that significant, and embed a link to another article which digs deeper into the topic presented. One of the “significant digits” is that teachers make 78.6% of what other workers in other fields with the same degree of education make in the US. This is concerning because I believe that it may have some detrimental implications on the economic future. For instance, teachers are responsible for the educational level of our future workforce; our future economy. I believe that teachers should be valued higher than they are because their essentially affecting how lucrative our future economy will be; teachers should be perceived as economical investments.

http://www.journalofaccountancy.com/news/2016/sep/finance-accounting-hiring-201615088.html

The information in this article explains how companies are growing and plan on expanding not only their business plans but also employment as well. According to the article, areas such as revenue, expansions, and profits have all shown an increase over the past two quarters. With a continuous growth occurring it has been stated in the article that “Two-thirds(66%) of CPA decision-makers at companies with annual revenue greater than $1 billion plan to expand business in the next 12 months, up from 56% a year earlier.” This article and information is interesting to me because as an accounting major finishing up my education it is important to know that businesses are planning to higher new and more employees.

http://fivethirtyeight.com/features/the-end-of-a-republican-party/
I really enjoyed this article from Five Thirty Eight, because I’ve been making the assumption that this years election has been putting a dent in the Republican Party for a while now and now I have data evidence to back up that my hypothesis was correct. While I don’t think that this divide necessarily will end the GOP, I do think that it is going to be very difficult to pick up the pieces. The ‘Trumpism’ reference clearly has statistical evidence behind its’ rationale and the uncertainty it provides for Republicans of the future; I’m eager to see how things pan out and if FiveThirtyEight really nailed it on the head.

http://fivethirtyeight.com/features/is-a-50-state-poll-as-good-as-50-state-polls/
This article weighs the question, “Is a 50-state poll as good as 50 state polls?” What intrigued me about this article was its relevance to this year’s presidential election, and the connection to proper sampling and polling to get the best idea of which presidential candidate the nation favors. Most polls we see are national polls that take a sample from each state in proportion to recent voter turnout. Silver makes the argument that it’s better to do 50 separate polls as opposed to the 50-state poll, in order to get a more reliable estimate about each state. While this concept hasn’t been tested, it makes sense logically, even though national polls tend to be accurate. Focusing on a sub-sample within a state allows pollsters to eliminate demographic bias. For instance, a national poll doesn’t necessarily take into account that a white voter in New York is more likely to vote Democratic than a white voter in Texas. By focusing on each state individually, demographics issues like that don’t happen. This whole concept of transforming political polling fascinates me because it could a create a more reliable way of predicting election outcomes.

http://www.journalofaccountancy.com/news/2016/sep/election-impact-on-hiring-spending-201615118.html

This article summarizes how the impending presidential election is not affecting the way companies plan, spend, or hire. Seventy-nine percent responded they would not change their hiring practices given the results of the presidential election. This is interesting, as this is quite possibly the strangest presidential election in U.S. history, and the next commander-in-chief could drastically affect fiscal policy in the country. As an accounting student, I find it fascinating how firms are not fazed at all by the impending election.

http://www.snhu.edu/about-us/news-and-events/2016/09/how-do-businesses-make-money-using-quantitative-analysis
This article has to do with how businesses can use data to gain profit and outdo their competitors. “How Do Businesses Make Money Using Quantitative Analysis” explains quantitative analysis, the growing demand, how to pursue a career, real world experience, how to start, and how to succeed. It gives you a well-rounded idea about how businesses use quantitative analysis and how people who study the data can gain much intel on the company. It also tells future students who want to major in it how they can succeed and what they can expect from majoring in it.

http://fivethirtyeight.com/features/dont-blame-a-skills-gap-for-lack-of-hiring-in-manufacturing/

The URL is about an article debunks the thought that the lack of people being hired in the manufacturing corporation is not because of the “skill-gap” that is between the people and the job qualifications standards. What is interesting about this article is that it explains how companies and managers are saying that they are not hiring because of this skills mismatch and that people are not just qualified for these specific tasks when people actually are.The writer of the article, Ben, explains his reasons for why these positions may not be filled and also points out what two economists thought about this topic. They say that manufacturing industry has become so specialized making companies making the search difficult because they are “looking for hyper-specific skills that few outside workers could be expected to have.”

http://www.thewrap.com/netflix-youtube-amazon-hulu-streaming-services-70-percent-internet-data-usage/
I read an article about how 70% of internet traffic is streaming media sites. Netflix consumed the most which was 37.1% of the traffic. This data shows how much streaming is taking over how people get their entertainment and how many people watch Netflix and Amazon Prime. These sites are becoming more and more popular. 70% of internet traffic is about streaming video. Being a film major and taking a “Filmmaking” class with film camera’s I find this astonishing because I am probably a part of the last generation to be taught how to use a “film” camera. Video is the far more popular and is now streamed all of the time.

https://www.sciencedaily.com/releases/2016/09/160913134149.htm

The article discusses a new computer language that was made by a team at MIT. It is called Milk and works as an addon to languages such as C and Fortran. In tests it was four times faster than other languages for the same queries. Milk circumvents caching restrictions and bottlenecks that current processor architectures have. With Milk, instead of requesting data in a batch, the data is added to a queue, and once the items in the queue can fill a batch, it is fetched from memory. Milk also shuffles queries around the CPU’s cache to maximize efficiency. I find this very interesting because it shows in which direction computing and data analysis might go. Eventually we might have purpose built architectures for Milk, which could not only improve data queries, but general computing as a whole.

http://fivethirtyeight.com/features/a-better-question-why-shouldnt-the-raiders-have-gone-for-2/
This article collects data from the past fourteen years of teams that have went for a two point conversion in the last minute of a football game. When analyzing this data, only eight teams in the past fourteen years have went for a two point conversion, and only four have a had success converting it. That is only 50% of teams that have converted the two-point conversion. Although it is not a big sample size, it shows that there is a 50-50 shot of converting according to the data given. For studying analytics in sports, and also being a big sports fan this is interesting to think about if a team needs a two point conversion to either tie or win the game. There is a decent shot that a team will convert which makes it more appealing to try during a football game.

https://www.washingtonpost.com/news/fact-checker/wp/2016/09/08/actuarial-math-trump-has-a-slightly-higher-chance-of-dying-in-office-than-clinton/
The article titled “Actuarial math: Trump has a slightly higher chance of dying in office than Clinton” sparked up a great interest as it deals with both data and my major. The article gathered data and then used such numbers to calculate that both Trump and Clinton have a excellent chance at completing two terms in office, however Trump has a higher chance of dying in office. An actuarial company gave Trump a 1 in 12 chance of dying compared to giving Hillary a 1 in 17 chance of dying in office. A company viewed data from public records of health about the candidates, while also gathering information from a database that had knowledge from more than twenty life insurance companies to get estimates on health and life expectancy. After examining all this and other factors such as drinking and smoking the company in the article predicts both Trump and Clinton to be healthy for years to come, predicting Trump having seventeen more years and Hillary having nineteen more years.

https://www.wired.com/2016/09/popular-color-internet/ This article discusses how the Internet’s favorite color is blue. The conclusion was made due to a mass collection of data from the world’s most popular sites regarding the color pallets they use. The top 100 most trafficked sites went under an analysis of which colors were most prominent, and then their data was recorded on a fan chart which showed which colors were used and how frequently. The man behind this data collection, Paul Hebert, wanted to do this because he is a designer and was curious of which color pallets websites use. With the information he wanted to learn which color was used the most, but also wanted to get ideas on which color schemes to use for sites of his own.

The article I found was Business Insider’s analysis of the August 2016 Jobs Report from the Bureau of Labor Statistics. The article used the data generated by the Bureau of Labor Statistics to make assessments of and predictions about the American economy. I found this article interesting because I am an economics major and understanding and processing data such as that generated by the Bureau of Labor Statistics will probably be a crucial part of my career. The article can be found here: http://www.businessinsider.com/us-jobs-report-august-2016-2016-9ew

http://jama.jamanetwork.com/article.aspx?articleid=2545691

This article is from a Harvard study which attempts to discover the reasons that medical costs are so high in the US. Having discovered this from an Economics forum, it is relevant to me given my economics major. The study theorized that the FDA had essentially created monopolies because of the high barriers to entry that it creates by trying to do extensive studies on drugs; this vastly increases costs and leaves costs rising with various legal barriers to creating generic versions of certain drugs. Most interesting to me was this quote from their findings: “Although prices are often justified by the high cost of drug development, there is no evidence of an association between research and development costs and prices; rather, prescription drugs are priced in the United States primarily on the basis of what the market will bear.” This is concerning because it shows that there is really no reason for the high prices other than the way the market is designed because of our legal system for medicines. It raises the question of where the line is where it is best to have the FDA approving drugs for safety and where the prices are not too high.

http://fivethirtyeight.com/features/gun-deaths/
The article I read was a visual of Gun Deaths within America, and it was able to really highlight how much of it we ignore. Within American Culture we mainly focus on Terrorism and or Mass Shootings. But the majority of gun deaths go completely ignored. Highlighted in this article was the fact that nearly 2/3 of deaths related to shooting were suicides and then another 12,000 of the yearly deaths were homicides. This was able to bring to light how much we ignore within the world. Not every shooting is related to a police officer, not every shooting is a mass shooting, we turn the blind eye far too often in America.

http://qz.com/780582/african-governments-still-wont-let-their-citizens-get-data-to-support-accountability-and-fight-corruption/ this article focuses on the barriers preventing journalists from accessing basic data necessary to effectively do their jobs in Africa. There is gradual change occurring in the right direction, with open data slowly becoming more accessible. This opened my eyes to just how fortunate we are to have a government with somewhat more of an open-book policy. It is scary to think that there are governments that withhold public information from its people.

I think this article is very interesting because it tells us a lot about how many people think trading can be very beneficial but in reality it can be very harmful. I am currently a marketing major but looking to do something involving sales as well and i think its very important to make sure trading does not interfere with the economy and the money made or lost by a country. America does a lot of trading with foreign countries and moves manufacturing outside of the country and it really affects the income of the people of america and also the cost of producing and selling products

http://www.forbes.com/sites/susanadams/2014/01/22/the-college-degrees-that-get-the-most-job-offers/#4d53494f7509
This article shows statistics of majors wit the most job openings. this is very beneficial information because you can see if your major is a good career path to stick to based on security of finding a job.

One example of Conventional Wisdom is that if you work hard you will succeed. Although most people think that working hard will ultimately lead to the success of a given task I believe that this varies depending on the activity. For instance some physical aspects such as hitting a baseball may never become an easy for a person no matter how much they try or practice . Others however, can be natural gifted and may not require much effort and still succeed better than those who put in countless hours. Overall, while putting in the time and work to achieve something it doesn’t necessarily mean that you would be successful.

It is a commonly held belief breakfast is the most important meal of the day, as it is meant to infuse energy before work even begins. But how does breakfast hold up against meals such as lunch, or even continuous snacking or eating. Would the creation of energy be more or less efficient in the various forms of consumption, and at what time would this energy be most useful. To test this I would set out a four groups of people. The first group would eat breakfast and work. The second group would work and then eat lunch and work again. The third group would not eat one meal, but would snack as they work. The last group would eat breakfast, work, eat lunch, and work to be tested as a control. Each group would be set upon a task such as an exam, and their performance and scores would judge the merits of each type of consumption. The scores would therefore be the data, and their positions and rank would be used to determine which type of consumption is the most efficient.

I was always told that watching too much TV could damage your eyes. I do not really know if the statement is true, but as I was growing up. I was told this way too often. A way to test this is to compare many groups of people and see if there is a correlation between their eyes are getting damaged by watching TV. The data from this study would show if watching too much TV could damage your eyes.

One example of Conventional Wisdom is that teachers personal appearance affect the way students will treat them and learn from them. As an education major a ran in to this Conventional Wisdom that says teacher with a more “put together” look (dressed clothes, makeup, organized hair, etc.) gain more respect and attention from their students,and therefore their students learn more. To find out if that is correct I will perform an experiment and collect data on students achievements and progress in the classrooms with the same teacher when at some classes the teacher is more “put together” than others and look for patterns and drew conclusions.

There is an old saying that goes like this: an apple a day keeps the doctor away. To test this theory I would gather a large, diverse group of people and have a group of them eat an apple every day, a group of them not eat any apples throughout the study, and a group that would only eat an apple once a week. The data I would gather would be which group of people got sick the most and had to go to the doctor’s office for an illness and which group of people did not.

One piece of conventional wisdom I learned from my high school AP US History teacher is that physically writing out your notes helps you retain information 3 times better than typing them, reading them a few times, or taking pictures of class notes. I have always used this method since, and although more time consuming, the method did seem to help my retention of the subject matter. To test if this rule of thumb is generally effective we can compare test scores of students who physically wrote out their notes to those who collected the information in another fashion.

An interesting example of Conventional Wisdom is that black people jump higher than white people. This idea is usually held up as truth, however it would be interesting to assess the actual statistics regarding jump height vs race. Recently, at the 2016 Summer Olympics, a white man won the high jump. Was this man simply an outlier, or is there something to be said of the convention as a whole. It would be very interesting to take random samples of men and women of different ethnicities and see if the data yields any comprehensive results.

A piece of conventional wisdom that I have heard is that “The early bird catches the worm.” This theory means that you achieve can more when you start your morning earlier than you do if you start your day later. To test this theory, I would find out the GPA’s of students that start with early classes versus student who start classes later in the day, and see which of the GPA averages are higher. Depending on my results I can determine when it is the best time to schedule class for students in order to optimize your grade point average.

A piece of conventional wisdom I have always heard is that eating breakfast leads to a more productive day. To test this may be rather difficult, as what qualifies as a more “productive” day may vary from person to person. You would gather data from two groups: those eating breakfast and those not. Each person could rate their day on a scale of 1 to 10 on how productive they felt they were, and less subjective factors such as test scores, grades, and amount of work completed by the individual could also be considered.

A piece of conventional wisdom I always was told is “You need a college degree to make money in life.” I tend to find that not true because a tradesmen such as a carpenter or a plumber can also make tons of money from having there own companies. I would test this by collecting salaries from people with a collect degree and also people without a degree and compare the two sets of data against each other.

Every since early childhood, I never ate breakfast in the morning, and was always scrutinized for passing up on “the most important meal of the day”. This statement of conventional wisdom has been recited to me time and time again, yet I continue to function completely fine without eating breakfast. To test this statement, I would collect data from one group of people that does not eat breakfast and one group of people that does. Multiple factors could be tested such as overall happiness, energy level, health complications, and a number of others.

A piece of conventional wisdom I have heard is “do what you love and you will never work a day in your life.” While I think it is important to find work that you can enjoy, or at least tolerate, I believe work is work and it will be no matter what your profession is. To test whether this is true, I would survey a random group of people, asking them different questions regarding things such as job satisfaction, and whether they pursued their passion as a career or not. I would look at situations for different people, and see if people who pursued their passions enjoy their jobs more than people who did not.

One piece of conventional wisdom I have often seen preached is the concept of catharsis, or essentially “taking your anger out” on something to then reduce their anger (or other emotions). Some people apparently punch pillows to release their anger, some people act mean, but all people who do this have the same goal. In order to test its efficacy, we could gather data from subjects who are irritated by bad news or some catastrophe, and then provide them a means of taking out their anger. They could then report whether or not and to what degree it helped them. Perhaps they could play a violent video game or some other action and report how they feel when they finish.

A piece of conventional wisdom I’ve been told is to get involved in as many clubs/organizations as possible in order to gain a better chance of landing an internship. Generally, juniors and seniors receive the most internship opportunities based on what qualifications employers are looking for in a possible intern. To test the piece of conventional wisdom, I would make a scale of clubs/organizations categorizing students based on how many activities they are involved in. These groupings, ranging from 0 activities, 1-3, 4-6, and 6+, will then be given a percentage of success based on how many students polled applied for an internship and were accepted for that internship. To receive the best possible results, I would attempt to poll at least 100 students for each grouping of activities.

An example of conventional wisdom that I have often heard throughout my academic career is, “an extra hour of sleep is more beneficial than an extra hour of studying.” This is a statement that I would love to believe, considering most people would enjoy that additional hour of sleep as opposed to studying. Testing this could be somewhat difficult, considering there may be multiple other factors that affect a student’s test scores. In order to test this, though, I would gather test scores and survey people to determine the amount of hours each person spent studying, what time they were up studying until, and how many hours of sleep that each person received. I would compare the test scores of those who got more sleep with those who got less and determine if this piece of conventional wisdom stands to be true.

Waiting 20 minutes after eating to go swimming would be an example of conventional knowledge. I believe this would be dependent on what and how much you consume; for instance, if you consume an excessive amount of food, you’re more prone to vomiting regardless of the activity you engage in. To test this, maybe have a group of different individuals who consume various amounts/types of foods right before swimming, record any symptoms that these individuals experience after eating and swimming, and use this data to support or disprove the hypothesis.

One piece of conventional wisdom I have heard before is to wait at least 30 minutes after eating before swimming. To test this I would gather information from two groups. One group would contain swimmers who would eat a normal meal portion than immediately go swimming after for a certain period of time and see if any symptoms or effects occur. The other group would be devised of swimmers who would consume the same meal as the other group of swimmers then wait 30 minutes before swimming and record the results of any effects or symptoms and compare the two groups to see if this convention wisdom is true or not. However i would only confirm this piece of conventional wisdom only after having at least 3 trials for more accurate results.

A piece of conventional wisdom I have heard is that there are no more “original” movies made anymore and movies today are all remakes, reboots, and sequels. Typically, the highest grossing films are sequels to previous successful works, however there are actually more original films than remakes, reboots, and sequels. To test this, I would make a table containing the number of original films and sequels/remakes/reboots that came out each year and use that data to determine whether or not there are more original films or sequels/remakes/reboots.

One piece of “conventional wisdom” that I have heard is that men are better drivers than women. Most men will agree with this statement and most women will probably disagree and say they are equally as good or better at driving than men. I would test this by looking at the driving records of groups of men and women and comparing numbers of accidents and tickets to see which group is statistically the better drivers.

One example of conventional wisdom that I have come across is that if you study right before you fall asleep, you will better retain information better for a test the next day. To test this, I would collect data by studying participants that study right before they fall asleep and compare their test scores with participants who study earlier in the day or earlier in the week, ect. Comparing this data would let me know if the idea that the time at which one studies has an influence on the retention of information for the exam, and therefore their results. After analyzing this information, we would create knowledge on the relationship between the time period at which one studies and how well they retain information for an exam.

A piece of advice my parents always gave me was that even on one way streets, you should look both ways because although the street is one way, someone could make a mistake and you could be hit. To test this I would look up the number of pedestrians hit by cars going the wrong way on a one way street per year with a sign that says “Look both ways” as opposed to a one way street that does not have a sign. The Pennsylvania Department of Transportation may be interested in putting more “Look both ways” signs to reduce incidents of people getting hit by cars.

Some conventional wisdom I have heard is that cramming for tests doesn’t really help. A way to test this would be to collect exam scores and record time spent studying before the exam. This would be a bit difficult to do as some people would do a lot better than others simply because they are smarter or have a better general understanding of the topic but I think it would be interesting.