Search Results for: --------

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.

 

Home

Created an application that will enable users to make reservations and order food ahead of time so it’s ready when they arrive.

This project required groups to develop a prototype as a solution to an existing problem, while also creating a logical business plan.

This assignment was a research essay on a current relevant topic on data analytics.  I chose to write about cloud computing and how it is used for data management within organizations

MIS2502_research