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Research Paper

This project taught me how much technology has been advancing over the past few decades. For example, computers are now able to understand human languages and carry on a full conversation with them. This project explains how scientists were able to make this type of technology and how this technology relates to the material we have learned in our Data Analytics class. I was also able to provide examples of this type of technology that almost everyone knows and experiences on a daily basis.

I created a application called SONNY where the user enters how many washes they want and the application displays the total cost along with a list of previous orders given by the user.

MIS Extra Credit

 

MIS2502: Data Analytics
Extra Credit Assignment (and Professional Achievement Points!)

 

 

Link to post:  http://community.mis.temple.edu/ngonzalez/2018/04/28/artificial-intel…-is-it-important/

Link to home page: http://community.mis.temple.edu/ngonzalez/

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Works Cited

“Artificial Intelligence – What It Is and Why It Matters.” What It Is and Why It Matters, SAS,

www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html#used.

 

When a person hears of Artificial Intelligence, also known as AI, their first thought resembles something of human-like robots or a robot revolution, but it’s span is much larger than the stereotypes it faces. According to SAS, a leading company in Analytics, Artificial Intelligence provides a gateway for machines to learn from experience, adjust to new inputs, and perform human-like tasks. These advancements in technology make it possible to increase the efficiency of a system for a multitude of reasons, including but not limited to, the analysis of more and deeper data, an increase in accuracy, automating repetitive learning and discovery, etc. There are many ways that Artificial Intelligence is implemented within a classroom setting, more specifically in MIS 2502: Data Analytics. The purpose of Data Analytics is to find or draw meaningful information or conclusions from the raw data facing examination.  AI helps facilitate these findings and relates to many of the topics discussed in MIS 2502, including “The Things You Can Do With Data” and “Advanced Data Analytics.” The first topic discusses the transformation of data into information, something that can be done efficiently using Artificial Intelligence. The next topic goes into more detail about the implications of the data interpreted and how it can be used to make predictions for future decisions. AI analyzes deeper data, allowing for decision trees, cluster analysis, and association rules to utilize all information to provide the most accurate results. Artificial Intelligence can also be applied outside a classroom setting. Many companies utilize Artificial Intelligence as a means of increasing the quality of their machinery, and as a result, the lives of their customers. For example, the Health Care industry uses AI to personalize medicine and X-ray readings, giving their customers access to personal health care assistants that help facilitate healthy living. This and many other practices of Artificial Intelligence are changing the world into a more technologically efficient place to live. The world is becoming more progressive with its leading technology and AI should continue to development, rather than being feared.

The Internet of things is a concept many industries around the world are adopting today. The concept of IoT is very simple to understand but tremendously powerful. IoT is anything that can be connected to the internet, like devices, appliances, machines, and cars, who exchange data between each other (Morgan, 2014). It is almost like a network of devices. IoT is important because the data that is generated between all these devices, which is data analytics in real-time, can be a live network that can produce deeper insights and improve business decisions (Goerlich, 2016).

IoT relates to many of the concepts I learned in 2502 Data Analytics. One of them is, MySQL. In class, we were able to see trends of what customers bought and where, and use this data to build a customer profile. IoT generates a tremendous amount of data, but where will we store all this data? The potential answer to this question is, MySQL. Data that is collected from IoT can be stored in a database management system, like MySQL. Another concept I learned in class was, what we can do with data. Everything starts with data.  With the data that has been created from IoT, we need to make sense of it by gathering, storing, retrieving, and interpreting it. This is important because businesses can use the data to make business decisions and future predictions as well.

In practice, IoT is used every day, to the point where it is a part of our lives. One example is smart homes. IoT has enabled us to turn off lights in our homes when we are far away, or unlock the door for a family member or friend when you are not home. This helps homeowners save time and money.

 

References:

Goerlich, K. (2016, June 20). Live Business: The Importance of the Internet of Things. Retrieved from http://www.digitalistmag.com/executive-research/live-business-the-importance-of-the-internet-of-things

Morgan, J. (2017, April 20). A Simple Explanation Of ‘The Internet Of Things’. Retrieved from https://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-understand/

The goal of this project was to research a Data Analytics topic which corresponds with MIS 2502. In our research of the topic we were to include a brief overview, how it relates to the course, and a real world example of it implementation.

You can click here to view my Research!

Through the research I learned about cloud based analytics and it implementation in the professional sphere.  A popular cloud based analytics platform is  IBM’s Bluemix.  Bluemix is used by companies to quickly and accurately create applications that can scale to millions of users.

Deep learning is a subset of machine learning that uses artificial neural networks to create accurate associations and dynamic predictions. Artificial neural networks are layered structures of algorithms that were developed based off of the biological human brain (Gossfeld). These layers train the model to learn on its own by recognizing patterns in the data (Tanz). It is able to learn through its own method of computing which is comparable to a child learning and processing information through trial and error (Hof). This is important because the neural networks utilized can greatly improve many problems we have in image recognition, speech recognition, and natural language processing (sas.com). On a broader term, deep learning and data analytics are similar in that they both try to make sense of large amounts of data. Specifically, deep learning builds on the clustering analysis concepts we learned in class. Although a deep learning model is more complicated, clustering analysis has an identical process of training the model to group data that is similar to one another. An example deep learning in practice is recommendation systems. We can see that companies like Amazon and Netflix have popularized the usage of a recommendation system where they are able to closely predict what you might be interested in next, based on past behavior. Deep learning can also be used to enhance recommendations in environments such as music interests or clothing preferences (sas.com). We can see this in music applications such a Spotify or Pandora where they create playlists that try to accurately predict the songs that the user would most likely enjoy.

 

 

 

 

 

 

 

 

 

 

Bibliography

Gossfeld, Brett. “A Simple Way to Understand Machine Learning vs Deep Learning.” Zendesk, 18 July 2017, www.zendesk.com/blog/machine-learning-and-deep-learning/.

Hof, Robert D. “Is Artificial Intelligence Finally Coming into Its Own?” MIT Technology Review, MIT Technology Review, 29 Mar. 2016, www.technologyreview.com/s/513696/deep-learning/.

Tanz, Ophir, and Cambron Carter. “Neural Networks Made Easy.” TechCrunch, TechCrunch, 13 Apr. 2017, techcrunch.com/2017/04/13/neural-networks-made-easy/.

“What Is Deep Learning?” What Is Deep Learning? | SAS, www.sas.com/en_us/insights/analytics/deep-learning.html.

Wrote an essay on Natural Language Processing and how it related to this class and how it is used in the real world.

James Gallo

MIS 2502

Extra Credit Assignment

4/28/18

 

For my assignment on a current topic relating to data analytics, Phil Goldstein about the increasing use of data analytics in the world of baseball.  Just as the business world has been using data analytics to help make decisions for their companies, the sports world is beginning to use these same techniques for decisions on the field.  Just like we have been doing in class, they are able to take large amounts of big data and find the meanings within it through a database.  This article talks about just as successful it has been in baseball especially through the Chicago Cubs and Houston Astros winning the past two World Series titles as pioneers of the data analytics world in baseball.  These teams are using the analytics for a broad host of things including shifting defenses to where players are more likely to hit the ball and who to pitch or bat in key situations.  Even Major League Baseball as a whole has now introduced what they call the “Statcast” system into all stadiums to combine internal and external sources of information to cover all aspects of the game.  While right now it is mainly baseball that has delved into the world of data analytics, it is only a matter of time before other sports join in as well.  As they continue to see how successful it has been in baseball at giving teams an advantage on the field, it is only a matter of time before they are looking to gain that sane advantage for their team as well.

 

 

Citation:

• Goldstein, Phil, and New York Yankees. “Baseball Is Bringing Sports Analytics to the

Forefront.” Technology Solutions That Drive Business, 10 July 2017, biztechmagazine.com/article/2017/07/baseball-bringing-sports-analytics-forefront. BizTech Magazine