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Research a current topic on Data Analytics that we have not covered extensively in class. You will create a write-up, and the purpose of this assignment is to give you an outlet to display your ability to understand and describe an aspect of data analytics to your current and future employers.

The MIS capstone course was basically every previous MIS course intertwined into one, a perfect way to close the book in the MIS program. There were quite a few similarities to the project done in MIS 3506, such as preparing a charter, scope, and prototype. Documentation as far as status reports like MIS 3535, process model and data model from MIS 2502, and the systems architecture from MIS 2501.

My team and I developed a prototype for a new reservation application called Dibs. Dibs is a holistic service provider that attempts to enhance the dining experience for customers. Dibs will enable users to make reservations at their favourite restaurants, browse the menu and pre-order their food, and provide their preference for the type of service they receive.

The project was very challenging because compared to MIS 3506, we had to start this project from scratch and brainstorm our own ideas. For the project to be successful we had to spend a lot of time developing the scope of the project and perfecting our business case to insure we had a viable solution to our idea. We had a team of 5 and split up the work to make the project run seamless and effectively and in the end it was very successful as we were picked to present at the MIS Capstone showcase.

Seong Beom Cho

Bonus Assignment: Social Network Analysis

Social network analysis is the relationship between the incoming traffic of people, organizations, groups, and etc. It looks at what the incoming flow of traffic of people share with each other and how they are related. Social network analysis/analytics tries to analyze big chunks of data from a source and looks for what the similarities and differences are within the traffic and what they work looking for. This is very important to online businesses or platforms, because by analyzing their flow of traffic the business or company can optimize themselves to better suit their flow of traffic. How do they optimize their business to be better suited to their customers? By analyzing their traffic data, they can see what people were searching for on their site/business and the keywords they used. And, by using those keywords they can see if their customers were successful on getting they wanted; however, if they didn’t, then they can add ads to suggest certain products or change their product tags to better suit those keywords.

It is clear that social network analysis is very useful, because it can find the relationship between the flow of traffic. This relationship is very useful for various companies and platforms, because by knowing the relationship they can find out why they are looking for certain products and can help the customer find their product more efficiently. But, to analyze any sources of large data, we need the right tools to organize and compile the data into meaningful data for it to be useful. That is why MIS2502 is very important and closely related to social network analysis, because MIS2502 is all about data analytics. This entire course is about organizing large amounts of data into useful data with a meaning. Then we can use this useful data for many purposes, whether that may be to suggest certain products for specific traffic or optimize a platform to better help their flow of traffic.

One example may be how Walmart organizes their store shelves to optimize product assortment to help customers get what they want quicker. According to Walmart, “Through analysis of customer preferences and shopping patterns, Walmart can accelerate decision-making on how to stock store shelves” (Walmart Today). As shown, this is just one example of a big corporate, but by analyzing large data through data analytics/social network analytics, we can turn these meaningless data into useful data for many purposes.

 

Work Cited

“5 Ways Walmart Uses Big Data to Help Customers.” Walmart Today, Walmart Inc., 7 Aug. 2017, blog.walmart.com/innovation/20170807/5-ways-walmart-uses-big-data-to-help-customers.

“Social Network Analysis: An Introduction.” Social Network Analysis: An Introduction by Orgnet,LLC, www.orgnet.com/sna.html.

 

 

 

Analytics and the NFL

Here is a link to my project.

 

Goals of project: The goals were to incorporate analytics and data analytics we learned in class with something in the real world.

Through completing this project I learned how the importance of question asking applies to the real world outside of class. Our SQL assignments required us to ask the correct questions to the data in order to produce the correct results. This is exactly what I described the independent contractor Warren Sharp accomplished in his research of NFL data. Seeing these parallels highlights how useful the skills learned in class can be and how they can be applied. Perfecting these skills is essential to increase employability in the workforce.

The goal of this project was to give me an understanding of some applications of data analytics that are related to what we have covered in class but were not specifically covered. The results of my project are available on the community website at http://community.mis.temple.edu/jkerrigan/projects. I have learned about the basics of text mining and sentiment analysis and specifically how both of those subjects directly relate to our MIS 2502 class.

Recommender Systems; Predictive Data Analysis

“Recommender” or “Recommendation” systems refers to a type of information filtering that aims at predicting a users choice or outcome in a decision. These systems use various classification algorithms and techniques; such as decision trees for precise item choices or clustering for similarities it items to be chosen (which will be discussed shortly). Recommender systems are typically split into content-based or collaborative filtering systems:

• Content based focuses upon “properties of the items recommended”[1]. An example being ‘recommending’ content to users such as Netflix surfing or Amazon.com shopping.
• Collaborative filtering “recommend[s] items based on similarity measures between users and/or items”[1]. This type of recommender system is closely related to clustering.

Predictive Analytics stems from recommender systems which is “used to make predictions about unknown future events”[2]. This type of data analytics is anticipatory; relying on data mining and statistical techniques in order to develop these underlying insights. Predictive analytics allows for organizations to be much more forward-thinking and proactive in their business decision-making.

Recommendation system models relate closely to the topics of Decision Trees, Clustering, and Association Rule Mining that we have learned in Data Analytics (MIS2502). Multi-tiered decision tree analysis is used when recommending the best choice for an end user. Clustering is used in collaborative filtering methods in order to best group similar items with end users. Finally, association rule mining–as well as data mining in general–is used to best predict an end users ultimate choice.

As part of my job as a prospect research analyst, it is my job to determine who are the best candidates for outreach to support the school of medicine. If a recommender system algorithm were implemented, I would be able to more accurately determine which patients and doctors are more willing to contribute than others through logical decision-making analytics.

 

References

[1]Recommendation Systems, Chapter 9 – Stanford InfoLab n.d.. April 28 2018 http://infolab.stanford.edu/~ullman/mmds/ch9.pdf

[2]Imanuel. “What Is Predictive Analytics ?” Predictive Analytics Today, Bigtexts.com, 12 Apr. 2018, www.predictiveanalyticstoday.com/what-is-predictive-analytics/

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

The deadline is 11:59 PM on Apr 28, 2018. No late submissions will be accepted!

Overview and Purpose:

Research a current topic on Data Analytics that we have not covered extensively in class. You will create a write-up, and the purpose of this assignment is to give you an outlet to display your ability to understand and describe an aspect of data analytics to your current and future employers.

Possible topics:

• Big data
• Distributed data technologies (i.e., Hadoop)
• Natural Language Processing
• Text mining and sentiment analysis
• Virtual data cubes and/or in-memory analytics
• The Internet of Things (IoT)
• Cloud-based analytics/Cloud computing
• NoSQL databases
• Deep learning
• Artificial Intelligence
• Social network analysis/analytics

You don’t have to choose one of these, but if you’re unsure of your topic choice ask me first.

For all students: Everyone who successfully completes the assignment will receive 30 points extra credit added to the last assignment. If you are a MIS major, you can also receive 50 “Professional Achievement Points.” [1]

Deliverable:

For MIS majors who would like to receive Professional Achievement Points:

For non-MIS majors, or MIS majors who do not want to receive Professional Achievement Points:

·       A page on your Community Site E-Portfolio that summarizes your research. (Your write-up should be formatted as an HTML page and posted to your E-Portfolio. In other words, DO NOT POST A PDF OR WORD DOCUMENT.) ·       On Canvas->Assignments>To-Do, submit the links to the front page of your Community Site E-Portfolio and to your write-up page.   If you are an MIS major and do not have an E-Portfolio, instructions to make one are here: http://community.mis.temple.edu/professionalachievement/e-portfolios/

·       On Canvas->Assignments>To-Do, submit a write-up as a word document ·       You do not need to have an E-Portfolio

 

Content:

Your write-up should be between 300 and 400 words, not including references.

You should cover the following points in your writeup:

• A brief overview of the topic (i.e., what is it and why is it important).
• How the topic relates to the material we have covered in MIS2502. How does it build on the concepts covered in the course? What are the related topics in MIS2502?
• An example that describes how this tool or technique has been applied in practice.
• Citation Guidelines: If you use materials (text, figures, data, etc.) in the writeup that was created by others, you must identify the source and clearly differentiate your work from the materials that you are referencing. Failure to do so will be considered plagiarizing. There are many different acceptable formats that you can use to cite the work of others. The format is not as important as the intent. You must clearly show the reader what is your work and what is a reference to someone else’s work.

Evaluation:

To receive credit, your deliverable must satisfy these criteria:

• The three areas described in the “Content” section must be covered.
• The text must be in your own words. Paraphrase your sources; do not quote directly from them.
• You should cite all the sources you use in a professionally formatted bibliography at the end of your write-up.
• If you are a MIS major and would like to receive points, your write-up should be formatted as an HTML page and posted to your E-Portfolio.
• The write-up should look professional and be free of grammatical errors and typos.

There is no partial credit! If you don’t meet all of these requirements, you will receive no credit for this assignment.

 

[1] For more information about Professional Achievement Points for MIS Majors, see
http://community.mis.temple.edu/professionalachievement/

MIS2502-Extra-Credit-and-PRO-Assignment

The assignment was to research a topic within Data Analytics and connect it to the concepts we learned this semester. Below is the link to my e-portfolio to the project embedded in HTML to my profile.

Project Link: https://community.mis.temple.edu/nmarino/class-projects/

E-porfolio link: https://community.mis.temple.edu/nmarino/

Please see my post at the link below.

http://community.mis.temple.edu/makena/2018/04/27/distributed-data-technologies-hadoop/

New students, transfer students, foreign exchange students, and visitors of Temple University often have a difficult time locating academic buildings and other destinations within or surrounding Temple’s main campus. A PDF map of the campus is provided through the university website but is outdated and confusing to use by many. This project will develop an easy to use, interactive map of Temple University’s main campus and the immediate surrounding area. The finished product will allow anyone to conveniently locate and navigate to their desired destination through the app.

 

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