Section 004, Instructor: Larry Dignan

Weekly Question #5: Complete by March 12, 2018

Leave your response as a comment on this post by the beginning of class on March 12. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, not so much particular “facts” from the class!

Here is the question:

Just like you did about a month ago, find another online article dated within last two weeks from a credible source that has something to do with data and is interesting and relevant to you. Copy and paste the URL directly into your response followed by a few sentences that explain what is interesting about it.

33 Responses to Weekly Question #5: Complete by March 12, 2018

  • https://www.theverge.com/2018/2/27/17058268/facebook-facial-recognition-notification-opt-out
    This article is about Facebook’s new facial recognition feature. It enables Facebook to search for pictures you are not tagged in. I think this is interesting because that means that the company must have ample amount of data on every single person that is registered with them. This amounts to almost 2 billion people.

  • https://insidebigdata.com/2018/03/01/octo-telematics-transforms-insurance-industry-machine-learning-analytics-platform/

    This article explains how Octo Telematics, a leader in telematics for insurance companies, is improving the insurance industry through machine learning. The article goes into detain about how Octo Telematics’ platform, using Cloudera Enterprise, works and how it can process previously impossible volumes of data, aggregating over 11 billion data points from 5.4 million cars every day. It also incorporates Apache Spark, which can analyze over 20 million miles of driving per minute and calculate location, acceleration, braking, idling, collisions, cornering, etc. This article is beneficial to me because as an actuarial science major, my job is all about risk management and calculating prices based off of risk. With this new technology, I am able to present more accurate information and give more accurate prices to further minimize risk.

  • http://money.cnn.com/2018/02/22/technology/airbnb-property-types-new-experiences/index.html

    This article is about how Airbnb wants to engage in an expansion effort. The company will start to offer more ways to search for properties, adding new tiers, called Airbnb Plus and Beyond by Airbnb, for a luxurious experience, and collections for family and work trips, and will expand to include weddings, honeymoons, and group getaways. With this new effort, Airbnb will expand out into a new market attracting more people into the Airbnb guest data base.

  • For this week, I found an article about the data breach. Many companies have failed to protect their data from hackers and paid a significant amount of ransoms. This time, it’s Uber. Uber was unable to defend the drivers’ information back in 2016 but stayed quiet. Pennsylvania sued Uber under data breach notification law that requires companies to notify a data breach within a specific time frame. The maximum penalty can be $13.5 million.
    I think companies should, especially these data sensitive times, comply carefully with the laws that deal with data. Beforehand, the companies deal with big data should put a protective measure that can detect a data breach of their consumers or communicate with the society as soon as possible when the events like this happen.

    Source:
    https://www.insurancejournal.com/news/east/2018/03/07/482613.htm

  • https://www.economist.com/blogs/graphicdetail/2018/03/daily-chart-2

    Since 1950, plastic that has not been recycled or burned has amounted to roughly 4.9 billion tons. Most of that plastic could have been dumped on land in an area the size of Manhattan. Instead, most of that plastic is in the ocean and is not easy to clear. Some computer models estimate that there could be up to 51 trillion micro plastic particles floating around the world. Most of this waste comes from developing eastern Asian countries such as China, Indonesia, and the Philippines; mostly due to their limited laws and regulations with respect to waste removing. This is interesting to me because it is such a massive problem for every nation and it seems as if this problem is often overlooked.

  • This article talks about how most people are getting rid of their social media accounts. In a survey by origin, founded that half of young people age of 18-24 had enough of social media, and 34% have deleted all their accounts. Facebook and twitter are trying to fix this by making their apps more friend based and more interactions. Also, some people are seeing that social media ruining their sleep. As many of the users stay up late using social media. https://www.google.com/amp/s/nypost.com/2018/03/11/maybe-its-time-for-everyone-to-give-up-on-social-media/amp/

  • https://www.forbes.com/sites/michelleevans1/2018/03/12/why-data-is-the-most-important-currency-used-in-commerce-today/#517aff0654eb

    This article discusses what, as a marketer, I have long believed about data – specifically consumer data. Data has become a kind of currency that companies will pay top dollar for as they try to forge strong relationships with their consumers. It also explains how power has now shifted back to the consumer – they are able to make very informed buying decisions and compare product pricing, benefits, etc. The most powerful companies used to be oil empires, they are now companies like Google and Apple that have the most (and best) data about millions of people.

  • https://www.cnbc.com/2018/03/09/cryptocurrency-scammers-of-giza-make-off-with-2-million-after-ico.html

    This article discusses the insecurity of cryptocurrency investments. A company called Giza claimed to be creating a secure storage device for cryptocurrency and was raising funds in cryptocurrency. The funds were raised using a cryptocurrency called Ethereum. Ethereum is somewhat similar to the most commonly known cryptocurrency, bitcoin, although over the past few months, Ethereum has been significantly more stable than bitcoin.
    This article shows that with relative ease, scammers are able to take investors money and have the trail be untraceable, once investors come to the realization of the scam. In this case a fake work history and pictures taken from an Instagram user in Dubai were used as credentials. From my understanding of the article, investors appear to have been overly eager. If a proper background search was done and the profile pictures were reverse image searched, I believe that this situation could have been prevented. This was a reminder to me that the LinkedIn connection requests from unknown users without messages could be fake users attempting to create false credentials and that a certain level of skepticism can be beneficial in the long term.

  • https://www.forbes.com/sites/forbestechcouncil/2018/03/12/cyber-insurance-analysis-of-problems-related-to-it-risk-insurance/#730a9f6c49bd
    This article discusses the challenges of insuring cyber risk. It has become necessary for companies to protect their data, but the insurance sector has not been developed enough to have the products that offer the most appropriate coverage. This is due to the inability to accurately assess cyber risk and the potential damages. The insurance industry can be slow to adapt, which is concerning because the need for data protection is growing exponentially.

  • https://insidebigdata.com/2018/03/05/data-science-can-take-company-next-level/

    I found this article applicable to our class. It lays down the foundation for the successful use of Data Scientist in any filed. The article states that if Data Scientists are being used effectively, the company/ organization will see tremendous growth. I found it interesting how the article notes the correct structure to the use of Data Scientist. A hierarchal scale will limit the innovations of the Data Scientists, the article suggests a flat structure. I think this shows that the field known as Data Scientist has a lot more to do with creativity than I initially thought.

  • https://insidebigdata.com/2018/03/11/artificial-intelligence-big-data-technologies-drive-business-innovation-2018/
    This article talks about the progress of big data and artificial intelligence in the business world. This year might be the year that AI will gain meaningful traction within Fortune 1000 organizations. 93% of executives identify AI as a disruptive technology but there is an agreement that they should use cognitive technologies to stay ahead of their competitors. As an MIS major, I will certainly work with a lot of data in the future. If AI can make predicting outcomes easier then it is also narrow down the jobs pool for data analysts.

  • https://fivethirtyeight.com/features/the-phillies-rebuilt-like-the-cubs-and-astros-can-they-win-like-them/

    This article looks at the progress of Phillies rebuild compared to how other clubs have set up their rebuilds. It looks at baseball data model from Fangraphs that analyzes how teams have gotten younger, improved their farm systems, and cut payroll over a rebuilding period of five years. Through this data, each team is assigned quality points (1-4) regarding the ability in which they were able to do each of these three things. It gives the Phillies 11 quality points (3: younger, 4: improved farm, 4: cut payroll) which are ranked second in quality points, and is ranked only 1 point lower than the top spot which is held by the defending world champion Houston Astros’ rebuild from 2010-14. The article draws parallels between the progress of the Phillies rebuild and the rebuilds of the previous two World Series-winning clubs the Houston Astros and Chicago Cubs when their rebuilds began to turn a corner. This article was published two days prior to the Phillies agreeing to deal with free agent starting pitcher Jake Arrieta but has foresight in projecting the Phillies as a potential dark horse this season, and a rising contender in years to come.

  • This article includes predictions based on Men’s college basketball. March madness is a huge event that occurs every year. This being said, people tend to “build brackets” and they use data and statistics and rankings from the season, and with this information, they bet on who will end up on top. Data, in this case, is very important because there are many ways to make this prediction. This percentage of winning is based on avg number of points scored per game, how many rebounds, % foul shots made, # of points scored in 4th quarter, etc. Overall, march madness would not be possible if data was not collected throughout the regular season!

  • https://fivethirtyeight.com/features/why-does-everyone-hate-the-media/
    Very similar to my first article, FiveThirtyEight examines the seemingly unfavorable news media and mistrust towards it. Using several set of data, Nate Silver has a chat with both a political editor and writer about what caused this, and who feels this way. They argue that the 2016 Presidential Election, the 2008 recession, and the Iraq war all contributed to this media climate. It also examines how certain groups, like political parties view media, as republicans trust in media has declined about 30% since the year 2000, while democrats are still trusting it at around a 70% rate. and as 85% of republicans see the national news media having a negative impact on the country. One thing I noticed, was the wording of some of the data. I believe that in our society generalizes too much, and in the case of media, I believe that is also the case. The problems people have are with the national news media, but often lump it together as just “the media,” which is why the wording in collecting the data is important, and that is what these data sets show.

  • https://9to5mac.com/2018/03/12/how-to-download-your-facebook-data/

    Facebook collects and stores all of the data of all of its users. This data can now be easily downloaded, and users can now see all the data that Facebook collects such as emails, messages, friends (requests, removed), and even pokes. I downloaded the data to see how I could see the data, and the data comes in a zip file with html links that go to a simple Facebook page with the listed information. Facebook’s advantage in this data can be used to predict consumer behavior and even profile generations of people’s online activity.

  • https://medium.com/the-new-york-times/your-data-is-crucial-to-a-robotic-age-shouldnt-you-be-paid-for-it-85431decb76e

    This idea elaborates why big tech companies should pay their users for the data that they generate. Since companies process data from and about their users, they should pay the users from the value of this data as commodity(especially as AI becomes more widespread). An interesting idea is that companies like Facebook(that generate high levels of user data) can make money from training AI systems and pay royalty stream to many people whose data helped train the models.
    There already exist social media platforms (like steemit.com) that are blockchain based and pay their users in crypto-currencies. Blockchain technology provides a good backbone to the necessary data security/immutability needed for such next generation applications.

  • https://insidebigdata.com/2018/03/11/artificial-intelligence-big-data-technologies-drive-business-innovation-2018/

    This article touches on the many intersections of the two most popular technology innovations of today, Big Data and Artificial Intelligence. Many service providers are utilizing these tech innovations to keep up with competition, as well as provide their customers with highly effective solutions. This is interesting to me personally because the article talks about how much money is being invested by Fortune 1000 organizations to improve big data and artificial intelligence. This can only mean more meaningful and more accessible data for the public.

  • https://insidebigdata.com/2018/03/02/opportunities-big-data-improve-energy-usage-across-industries/
    This article talks about how data can be used to improve energy usage all across the country. Today, there are tons of places where data comes from, and people take this data and see how they can use it. I believe that there is data about energy efficiency and it can be used to benefit our country. This can be done by analyzing the data, and then formulating a plan that improves energy usage. This would have a huge impact on industries so they can operate the same or better with using less energy.

  • http://tcbmag.com/news/articles/2018/april/small-ball-meets-big-data-inside-the-twins-swing
    The article chronicles the Twins endeavor into the field of using data analytics and sabremetrics to help them make baseball decisions (which was long overdue). The Twins were an organization that relied heavily on old-fashioned baseball decision-making processes with respect to scouting, free agent signings, trades, and player development, with Terry Ryan at the helm. The transition into analytically minded decision making is no fad; teams are becoming increasingly analytical, as each team tries to gain a competitive advantage in an unfair game. In the article, it talks about new philosophical shifts within the Twins organization, specifically citing the swing changes that Twins’ coaches are teaching the players (in order to lift the ball in the air more, which in and of itself is an engaging topic), and the emphasis on framing pitches (catchers trying to make balls look like strikes), among others.

  • https://www.forbes.com/sites/amitchowdhry/2018/03/12/clairvoyant-the-story-behind-this-big-data-and-enterprise-security-company/#98fe07414b88

    This article explains clairvoyant a company that started with a unique method of collecting and storing sensitive information for big companies.They went on to have many different uses from educational to helping with the space program.

  • http://www.businessofapps.com/data/snapchat-statistics/
    This article shows a data set for the usage of Snapchat in 2017. This interests me because of how often kids use social media nowadays. These numbers show the profit, too. This shows how they are able to gain monetary values through so many different ways.

  • http://adage.com/article/digital/weed-people-cannabis-delivery-meets-mobile-data-science/312683/

    Out of many new innovations coming out in the data universe, this article stuck out to me due to the potential of growth with the Eaze data. Basically, Eaze is an app for medical cannabis patients. They can have their medicine delivered to them within 20 minutes in the state of California. The company began using its data information it collected from Eaze Insights, a data analytics program. This was set in stone to work cooperatively with local policymakers and shot callers.
    .

  • https://www.nytimes.com/2018/01/30/opinion/strava-privacy.html
    This article talks about a data privacy debacle at a point where people who exercise while using a database that compares their workouts with others tells us a lot more than what they do in their daily routine. The app they are using is an exercise app called Strava. The article also talks about the common conflicts through data privacy with increasing technology and unethical ways to get through consumers data.

  • http://www.dataversity.net/driving-data-quality/
    I found this article interesting because when I think about data, I think about its quantity, and how hard it is to find quality data. according to the article, solving Data Quality issues to improve Data Analytics starts with having the business users articulate not what they want. It is interesting that technology isn’t really the problem in these cases, it’s the training provided to data scientists.

  • https://www.cnn.com/2018/03/07/us/applenews-march-madness-perfect-bracket/index.html
    This article talks about the chances that someone will have a perfect March Madness bracket. Since there are so many teams and so many outcomes it would be almost virtually impossible to predict every game correct. In the past no one has ever had a perfect bracket and I would not be surprised at all if I never see someone have one in my lifetime. This data about the NCAA tournament interests me because it is all based on statistics that can be proven to be complete irrelevant depending on how two teams play against each other that day.

  • https://www.cbssports.com/college-basketball/news/2018-ncaa-bracket-tournament-march-madness-predictions-expert-picks-winners-upsets

    I chose an article about the NCAA mens basketball tournament, also known as March Madness. The data here is each teams rankings going into the tournament, which is used as an indicator to project the team most likely to win based off how they performed during the regular season. Despite the fact that 64 teams are spread out into 4 brackets and ranked 1-16 accordingly there has never been a tournament where every higher seed has beaten the lower seed. Upsets happening every year in the one and done tournament style have given in the nickname march madness and therefore using this data to make predictions on what those upsets will be to a basketball fan like myself is beyond interesting!

  • I found this article interesting because the author discusses ways to improve data literacy within organizations, which is a topic we’ve discussed in this course and also one that aligns with my personal career goals. I like the author’s simple approach to easing into becoming a “data organization,” as he says. He advises organizations start small, with one person or team, and to teach them slowly to build data literacy overtime. He also emphasizes the importance of trust and transparency which I believe are important in dealing with data, because it’s important that people feel comfortable working with the data and presenting it to others.

    https://www.cmswire.com/digital-experience/how-to-improve-data-literacy-among-the-non-quants-in-your-organization/

  • https://fivethirtyeight.com/features/everything-you-need-to-know-about-the-pennsylvania-18th-special-election/

    This article is about the Special Election for Pennsylvania’s 18th Congressional District being held on Tuesday. I find it interesting for a few reasons. First, as a Pennsylvania resident this affects me. Second, this is the first time within eight elections that Representative Tim Murphy will not be running for office. Murphy was so well liked in the significantly Republican district that he had no Democratic opposition in the last two elections. The third and final reason I find this article interesting is because it uses collected data and recent polls to show just how close this race is, and what the outcome of the election means for the upcoming midterm elections. In a field so complicated and sometimes down right crazy as politics, it’s nice to have straightforward data to give an insight into the political sphere.

  • This article is about how Artificial Intelligence (AI) is finally being utilized in fortune 1000 companies, with 97.2 percent of executives reporting investing is AI. Three-fourths of a executives report that they “achieve measurable results from robots artificial intelligence and big data investments”. I found this extremely interesting, I had commonly thought that AI is more used for things such as robots and not so much used for companies to sort data and use it to their advantage. That the collaboration of data and AI seems to be the future in all major companies across the globe, and right now is only the tip of the iceberg for its future.

  • https://www.forbes.com/sites/bernardmarr/2018/03/12/forget-data-scientists-and-hire-a-data-translator-instead/#362b28b6848a

    I found this article intriguing because it offers a different role in Data. Communication is key for every business and hiring a data interpreter might help bypass data propelled speed bumps in any given industry.

  • https://finance.yahoo.com/news/data-breach-victims-sue-yahoo-united-states-judge-143243547–finance.html
    I found this article interesting because it talks about a breach in Yahoo’s data system which happened after the purchase from Verizon. The system was breached three times. This is similar to an article we read for class in the beginning of the semester.

  • http://www.dataversity.net/building-future-women-data/
    I loved this article because it talked about numbers of women in the data field and brought up the problem of women being underrepresented in the industry. The author suggests raising awareness about the opportunities in the tech field (that is not really female-friendly) for women. For example, change the current situation in schools where tech and IT classes are not meant for girls, and girls are not encouraged to take them. However, according to statistics, women drastically surpass men in careers like Data analyst.

Leave a Reply to Anastasiia Mazurok Cancel reply

Your email address will not be published. Required fields are marked *

Office Hours
Larry Dignan lawrence.dignan@temple.edu Alter Hall 232 267.614.6467 Class time: 5:30-8pm, Mondays Office hours: Monday hour before class, half hour after class or by appointment.