In April 2017, I competed nationally in the Association for Information Systems Data Analytics Competition with two fellow Temple MIS students, Zoe Weiner and Run Zhu. Our selected challenge focused on the global diabetes epidemic and had us analyze data from the International Diabetes Federation, WorldBank, and other sources to provide data driven and country specific solutions. Our entry landed us in first place for analysis. Feel free to look at our entry below.
It sounds like science-fiction. Your car senses the bridge ahead is icy, and determines an alternate route (Burrus). Your house knows you are coming home, so starts to preheat the oven for dinner. If your dog leaves the yard, his dog tag will alert you. Yet, as you may have noticed, none of these things sound far-fetched. Many of them are available to a small market, and others are in production. These ideas belong to a trend commonly called the Internet of Things (IoT). Many other, possibly more precise titles exist, such as the Sensor Revolution, or the Programmable World, but Internet of Things is the one that is sticking (Wasik).
The Internet of Things is about sensors, data, and the objects in our everyday lives. The hope is that one day, every object will have sensors transferring data to us, utilizing cloud technology (Morgan). Technically, the smartphone is an innovation of the Internet of Things, but the smartphone is also fueling the Internet of Things. Before, although people wanted to make an automated system where your house knew where you were or what the temperature outside was, there was no default interface to interact with that technology. With the smartphone, almost everyone has a device that gives their location and can communicate with countless other devices (Wasik). Smartwatches are another innovation in the Internet of Things, one that uses the smartphone as its connection to the user.
For data scientists, the Internet of Things could be a blessing. Since the Internet of Things is based around sensors collecting data at all times. there will be information on things that previously had been impossible or very difficult to capture information on. Currently, we can analyze receipt data to see what items are bought together and use association rule mining to see the likelihood that one item will be bought with another. Perhaps in the future, the cart itself will detect in what order items are added, the exact path of every customer through the store, and when items are removed. This application is small scale; only the future will tell how far the Internet of Things can be taken.
Burrus, Daniel. “The Internet of Things Is Far Bigger Than Anyone Realizes.” Wired. CNMN Collection, n.d. Web. 27 Apr. 2017.
Morgan, Jacob. “A Simple Explanation Of ‘The Internet Of Things’.” Forbes. Forbes Magazine, 20 Apr. 2017. Web. 27 Apr. 2017.
Wasik, Bill. “In the Programmable World, All Our Objects Will Act as One.” Wired. Conde Nast, 14 May 2013. Web. 27 Apr. 2017.
In Data-Centric Application Development, our professor recommended that we try taking on the Fibonacci Sequence in order to practice loops in PHP.
I have finished BA 2104: Excel for Business Applications. This course is required for any Temple University student attempting to obtain a degree from the Fox School of Business. This was an accelerated 7 week course done online.
I am currently in MKTG 3596: Consumer Buyer Behavior. This course is required for any Temple University student attempting to obtain a Bachelor’s degree in Marketing. This is a writing intensive course, where several papers are written and critically evaluated.
In Data Science, the final project is to analyze a data set of your choice. My group chose to analyze a data set released by the Guardian(“1000 Songs to Hear Before You Die”).
The data contained the theme of the song, the title, artist, year it was produced and possibly a link to the piece. These data fields directed what my team analyzed.