Shana Pote

A data driven service I use regularly would be my activity tracker for my apple watch/phone. Some of the columns I would expect to see might be Date, Move Calories, Exercise Minutes, Stand Hours, Steps, Distance, Workout type, Workout duration, workout metric. The rows I would expect to be each record or day or instance that this data is collected. (it is typically presented by the day so I would assume by day vs. hour or week)

A data service that I use daily would be Spotify. Spotify lets you listen to music, make playlists, and see what your friends are listening to. The rows and columns of data I would see would be my playlist titles; song, artist, album, duration of the song, and how often it is listened to. I can also see another row which shows what my friends are listening to: my friends name, the song, album, and artist; and how long ago they were on Spotify.

A data driven service I use regularly is Adobe Kuler. Kuler allows people to create and share 5 color schemes and is very useful for graphic artists. The rows in the spreadsheet would represent each color scheme that is created. The columns would represent things like what 5 colors make up the scheme, how many times the scheme is clicked, how many times it’s downloaded, and then the timing of each of those two activities.

A data driven service that I use daily is Snapchat. Snapchat is an app that lets someone send pictures or texts that only last a certain amount of time. Some columns would be amount of snaps sent and received, amount of stories posted, and number of friends. The rows would be each person represented by their specific username in Snapchat.

A data-driven service I use daily is Amazon.com. The first column would be dates I have bought goods from Amazon. The rows would be what I have bought from Amazon in different dates and how many time I shopped in a day. Another column can also be good names, the rows can be how many times I bought the same item and price of the item.

A datadriven service I use daily is Blackboard. Blackboard lets you see your grades, course content, assignments and announcement for each all of your courses. The columns would be course name, CRN, professor’s name, grades, assignments, calendar and announcements.

A data-driven service that I use daily would be Apple Music, which is a music streaming service. Some of the columns I would expect to see on a spreadsheet of the data from this would be things such as artists, song titles, length of songs, number of playlists and their titles, the lengths of playlists. Another spreadsheet may include data unrelated to the music, but rather payment totals for each month and the date they were paid.

A data-driven service I use is Netease Music, which is a online music service in China. They provides daily music recommendations to me based on what types of music I have already listened. If let me to design the database, each row would represent a record of music playing. The columns would be how many times you have already listened to this music, the duration of this record(to show if you listened the full music or just half-way through and turned to another music), whether you added the music into “My Favorite” playlist(Boolean value), and hundreds of genre columns, each column represent one genre, and the boolean values inside means if the music is belong to this genre or tag. I think using more boolean values instead of strings would make the data analyzing be much easier.

A data-driven service I use every day is the app Sleep Cycle, which track my sleep patterns using my phone and wakes me up during light sleep period. the rows in the spreadsheet would be the time I went to bed, time spent in bed, time woke up, snore, sleep quality. For the columns, I would have day of the week, the location, the moon, and the weather.

I think one of the most important things to take away from this course is the awareness of data in all that we do and how powerful it can be if leveraged properly. Close second to that, is the ability to learn to work with tools such as tableau because in business you will need to learn technology at a faster rate than ever before. Technology is changing at such a quick pace. Having the aptitude to learn how to work with these tools will set you apart and help you to add value to your role wherever you end up.

I think an important aspect of this course was how to use tools like Tableau and Excel to their full extent-technology is constantly changing and learning how to use these tools helps me grasp a better understanding of the relationship between data and technology.

The most important takeaway for me was the use of the tools throughout the course (Excel and Tableau). If I were to explain this course to a future student, I would say it’s a very interesting course with an application and/or relation in your daily and business life.

An important takeaway from this course is the realization that data is everywhere and will only continue to expand in our generation. I would explain this course to a future student as a class that teaches you the many tools and applications of data in our life. I think it is very important to learn how to utilize data because of the massive effect it now has on businesses.

The most important takeaway for me is that big data is powerful. I realized that it is not as scary and overwhelming as it may seem, especially with tools like excel and tableau to manage data and incorporate it into every day life. Big data has a huge impact on many fields especially in business which makes this a valuable class to take.

Data science is a course that helped me understand data as a whole, analyze it, and sort through it. Managing data is everywhere, no matter what field of interest a person has. This course taught me how to represent data to an audience in a persuasive way by using data analyzing tools such as Tableau, Excel, and Piktochart.

I think the most important takeaway from this semester is how influential visualizing data can be for establishing relationships between variables. Familiarity with tableau and access to a user license for the rest of my time at Temple was an added bonus. I am excited to be able to continue to work with open sourced data from local municipalities to generate informative visuals in future political science research.

I think the most important takeaway from this semester is how important data and understanding it is. This course has given me basic but important skills that I’ve already been able to utilize in group projects and at my job. I would say this course is about introducing basic computational style thinking and methods. Having gone through this course, I can now see a problem and then think about how to fix it using the software and methods we discussed in class.

The subject of our group project is the relationship between population growth and employment growth. The scholars in our group are Austin, Alex, and Melissa.

1. The subject of our group project will be Business and Economics.
2. The scholars in our group include Bryan Hua, Linghang Peng, and Ziran Zhao.

https://www.vox.com/policy-and-politics/2017/10/16/15657512/cambridge-analytica-trump-kushner-flynn-russia

Here is an explanation of how Cambridge Analytica used ‘big data’ to influence the 2016 US presidential election.
if the Department of Justice can prove that the Trump Campaign colluded with Russian Hackers and that Cambridge Analytica was involved, it will demonstrate the vulnerability of the US electoral process and hopefully lead to ethical regulations for how companies use data.

Moneyball or moneypit? When big data infatuation meets unintended consequences

This article talks about the effect that big data has had on Major League Baseball. Prior to the early 2000 Oakland As team, many baseball teams had not utilized data to develop strategy in games. Instead, they would just pick players based solely on pure talent. However, many teams have adopted this method due to that As team huge success. Many people are now arguing that baseball is now too strategic and that it is taking the excitement and of talent out of the game. Even in the World Series, I can see how big data is being taken advantage of through things such as infield shifts and pitching changes.

This article was important to me, because the data presented in the article shows how our economy has grown in the third quarter of this year. The writer explains that our economy has been growing modestly, but consistently since last year and he believes that this will allow the government to raise interest rates. In addition to this, he explains how exports and higher oil prices are igniting businesses and industries and spurring more growth. The article also explains that the unemployment rate has been slowly declining as well.

http://www.bbc.com/news/world-41880153
I found this article to be interesting because it is about a huge data leak (about 1.4TB) of confidential information about offshore dealings for some major companies and wealthy individuals. One large company mentioned was Apple, Bono (from U2) was mentioned, and many more. Obviously the people utilizing Appleby’s services, the law firm who was hacked, had an expectation of privacy and now are exposed and could face serious consequences. What interested me most about this was the implications of how difficult it is to secure our digital information and how much more difficult it is going to become with the rapid changes in technology and users capabilities.

https://www.theguardian.com/news/datablog/2017/oct/31/terror-trends-what-are-the-most-common-phobias-among-us-adults
I found this very interesting article. The survey took in 2007 shows that most of people develop their fear at about 9 years old. It also shows that most of people have at least one type of fear, and for the types of fear, the fear about animals is most common. The height and weather fear is also very common.

https://www.economist.com/blogs/freeexchange/2014/12/working-hours?fsrc=scn/fb/wl/bl/workinghours
I thought this article was interesting because it gives fairly good evidence that working longer hours does not always relate to increased productivity. The British munitions factory workers had about 44 good working hours a week before productivity began to decline. I think it would also be interesting to see, as the article mentions, what productivity looks like for more white collar, knowledge based work, and how many hours worked is optimal. I imagine that this would be a much more pronounced relationship than for service sector jobs.

https://www.forbes.com/sites/sarwantsingh/2017/11/06/are-car-companies-going-to-profit-from-your-driving-data/#5cae9609143c
I found this interesting article about how car companies can profit from your driving data. The article discusses a report done on “data monetization in cars”. This report found that vehicle manufacturers have an opportunity that amount to $33 billion by 2025 if they capture certain car data types. With more than 200 data points that exist in cars today, usage based insurance hold the highest potential with an opportunity of $25-40 per car per year with real time data services. Other car data customers can range from energy companies to retailers such as McDonald’s and Starbucks who want to know when you are likely to buy your next coffee or sandwich.

https://fivethirtyeight.com/features/declaring-opioids-a-health-emergency-could-make-treatment-more-widely-available/
This article talks about the recent declaration of opioid abuse a public health emergency and how this could make treatment more widely available for those addicted. I found it interesting because medication-assisted treatment drugs are difficult for doctors to prescribe outside of treatment centers. The article also focused on a drug used for medication-assisted treatment other than methadone, which is most commonly used but must be taken every day. Buprenorphine is harder for doctors to prescribe outside of addiction centers but it is much longer lasting than methadone, so it may not need to be taken every day. Additionally, most treatment centers are focused around urban areas, whereas only 9% of treatment areas are located in rural areas, limiting the access to those needing treatment in rural areas.

https://projects.fivethirtyeight.com/trump-approval-ratings/?ex_cid=rrpromo

This is found from FiveThirtyEight website. It illustrates presidents’ approval rate, accounting for each polls’ quality. It shows that since February to now, Trump’s approval rating decrease continuously. Compared to past presidents, like Obama, Bush, Clinton and others, Trump’s approval rating is lower than past presidents in most times.

https://fivethirtyeight.com/features/gang-stats-arent-remotely-reliable-but-voters-keep-hearing-about-them-anyway/

This article talked about how politicians use gang statistics in relation to crime as a major facet of their campaigns. The article talks about how these statistics are incorrect but are still used anyway. It talks about how it is impossible to accurately measure how is in a gang or how many gangs there are when there is no definition for what constitutes as a gang. Every source that has tried to measure how many are in a gang has come up with different estimates due to a lack of a clear definition of gangs, inconsistency with reporting these stats, and inconsistency with where they store this data. It’s interesting because I have always wondered, especially in today’s politics, where politicians get their data on crime and gangs and how accurate or not that information is. It is important to know where this data is coming from because the claims made that are supported by this data could determine who wins elections.

http://theconversation.com/how-data-is-transforming-the-music-industry-70940

This article presents how data can be used in music. For example the billboard top 50 uses data to find the best fitting music and apply ratings to it. Data can help better understand who is buying music and why