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The goal of the project was to research a current topic in Data Analytics that was not covered extensively in class and provide a brief summary of research, how this topic relates to other subjects covered in class, and an example of how it has this tool has been applied in practice. I learned how data cubes work and how they can take a 2-D data store (rows and columns) and analyze it in much higher dimensions. This expands upon the information we learned about relational databases along with providing a solution to obtaining better association mining. As well, I learned about the ‘roll up’ and ‘drill down’ features available to cubes because of their allowance for hierarchical relationships. In all, it really changed my thinking of how dimensions relate to databases, especially how rows and columns can not only limit one’s data but as well, there perspective. http://community.mis.temple.edu/cbreece/articles/

The goals of this project were to comprehend sentiment analysis and summarize the concept in an informational and succinct manner, relate sentiment analysis to topics covered in MIS 2502, and display a real-world example of sentiment analysis in action.  My research revealed sentiment analysis is crucial for organizations to gauge public perception, and although sentiment analysis does not require machine learning or artificial intelligence, the advent of these technologies will improve it greatly.

Project URL :  http://community.mis.temple.edu/asmuszkiewicz/mis-2502-sentiment-analysis/

This is a short essay on how Big Data has significantly helped businesses in many ways including new revenue opportunities, better marketing strategies, and competitive advantages over rival organizations. 

The URL is http://community.mis.temple.edu/cross/the-use-of-big-data/

1. The goal of this project was to research a topic that relates to our coursework in 2502, and add a write-up it as a page on our e-portfolio. We had to explain the topic, explain how it builds upon our curriculum, and a real-world application of the topic. I chose NoSQL databases, because I was curious about databases that are designed to work with unstructured data. Here’s the link: https://community.mis.temple.edu/ecripps/nosql-databases/

The goal of this class project was to determine the relationship between the Internet of Things (IoT) and one concept we studied in our Data Analytics course. As an avid user of smart light bulbs, it was very  insightful to further understand the back-end architecture of smart lights through the lens of the IoT. After completing this project, I learned how data is transmitted from my smart light mobile app to my IKEA smart light bulbs. Please read more about my interesting findings here. How the IoT relates to Data Analytics

The short research was about Sentiment analysis, it’s application and it’s relation to what we studied in our class. Sentiment analysis is an analysis that extracts subjective information from the source material. This way of contextual mining is primarily used to classify the source material as positive, negative or neutral. My goal was to research about the topic to connect it to data analytics. I learned about how unstructured data can be converted into semi-structured data (CSV, JSON, XML) and perform analysis on that to draw inference. With the use of APIs, data analysts perform sentiment analysis and it’s used in many enterprises to learn about customer behavior, purchase patterns and  public opinon to implement new strategies, introduce a product, revise a commercial.etc.

Link to the project: http://community.mis.temple.edu/ashlinbc/research/

 

 

Include the goals, results, project URL (if applicable), and what you learned in a brief paragraph.Once approved, the description is automatically displayed in a post on your e-portfolio

1. Include the goals, results, project URL (if applicable), and what you learned in a brief paragraph.
2. Once approved, the description is automatically displayed in a post on your e-portfolio.

The goal of this project was to write a short essay about a data analytics topic. I chose Big Data. I learned that Big Data is only getting bigger. Data is being collecting through everything nowadays and businesses must learn how to store and analyze it in order to get a competitive edge. Businesses who do not learn how to work with data will be left in the dust.

Rich Wagner

The goal of this project was to create a write-up with the purpose of displaying my ability to understand and describe an aspect of data analytics to any future employers. The aspect I chose to describe was text mining and sentiment analysis. I was also able to describe a real world example of the 2016 presidential campaign to show how these techniques were used in regards to the media’s attitudes toward the two presidential candidates. By completing this project, I learned how text mining and sentiment analysis are related to data mining as well as learning that these two techniques can help see trends that are not clearly seen or known from the raw data.

For my capstone class, my team was assigned the project of Python versus R for Data Analytics.

http://https://www.slideshare.net/AndreaBehler/python-versus-r-for-data-analytics-142132013

MIS

 

Emily Horensky

MIS 2502 Zhe Dong

TU ID: 915513671

MIS Extra Credit

Artificial Intelligence and It’s Advancements

AI or artificial intelligence is a field of study that relates to artificial or computer intelligence generated by machines and not the human brain.  It is designed to mimic the human brain to solve problems and generate ideas.  Artificial Intelligence is a controversial topic in this day and age with some fearing that computer generated intelligence will exceed the human mind and eventually surpass any human capabilities. However, with the help of AI we are now capable of having computer driven cars, and many other technology based items that help make life easier.  Management Information Systems and artificial intelligence go hand in and because much of AI is based off of data analytics and predicting outcomes based on data.  In class we are taught how to predict outcomes based on previous data collected and we also analyze and take into account any hazards that could affect that data outcome.  Analyzing the risks and predicting while knowing why such things happen is a key part in MIS and AI.  An example of AI in real life would be Tesla’s driving and driving assist cars.  What these cars do is predict actions taken on the road by other drivers while detecting and adapting to hazards ahead.  These computers use cameras and sensors around the car to be able to drive and see a full 360 degrees of the road.  The cars programmers have programmed the computer in the car to spot hazards ahead and adapt to them accordingly.  For example, if a child were to run across the street after a ball, the car would detect the child as a hazard and stop the car.  These self-driving and driving assisted cars help reduce the number of accidents on the road while helping drivers navigate the road more smoothly and without any accidents.  With the invention of AI, humans are now capable to exceeding the boundaries of human capabilities while helping ease day to day life of the average human being.  As time goes on and technology becomes more advanced, humans everyday life will continue to become easier and computers will become more incorporated in a person’s daily life.