Additional study must be performed on the interactions between each big data characteristic, as they do not exist separately but naturally interact in the real world.
The scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.
New techniques and algorithms must be developed in ML and NLP to handle the real-time needs for decisions made based on enormous amounts of data.
More work is necessary on how to efficiently model uncertainty in ML and NLP, as well as how to represent uncertainty resulting from big data analytics.
Since the CI algorithms are able to find an approximate solution within a reasonable time, they have been used to tackle ML problems and uncertainty challenges in data analytics and process in recent years.
Your paper should meet these requirements:
Be approximately six pages in length, not including the required cover page and reference page.
Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.
Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.
Questions:
1. Explain the relationship among data mining, text mining, and sentiment analysis.
2. In your own words, define text mining, and discuss its most popular applications.
3. What does it mean to induce structure into text-based data? Discuss the alternative ways of inducing structure into them.
4. What is the role of NLP in text mining? Discuss the capabilities and limitations of NLP in the context of text mining.
Exercise:
3. Go to teradatauniversitynetwork.com and find the case study named “eBay Analytics.” Read the case carefully and extend your understanding of it by searching the Internet for additional information, and answer the case questions.
Internet Exercise:
7. Go to kdnuggets.com. Explore the sections on applications as well as software. Find names of at least three additional packages for data mining and text mining.
How do you describe the importance of data in analyt- ics? Can we think of analytics without data? Explain.
Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
Where do the data for business analytics come from?What are the sources and the nature of those incomingdata?
What are the most common metrics that make for analytics-ready data?
Exercise
12. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to edu- cation, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
Chapter 4 – Discussion question #1, #2, #3, #4 & #5 &
Exercise #1
Discussion
Define data mining. Why are there many names and definitions for data mining?
What are the main reasons for the recent popularity of data mining?
Discuss what an organization should consider before making a decision to purchase data mining software.
Distinguish data mining from other analytical tools and techniques.
Discuss the main data mining methods. What are the fundamental differences among them?
Exercise #1
Visit teradatauniversitynetwork.com. Identify case studies and white papers about data mining. Describe recent developments in the field of data mining and predictive modeling.
Paper 1: Answer the following questions
What is the difference between data, information and knowledge?
Explain in your own words how the data component related to the hardware and software components of information systems.
What is the difference between quantitative data and qualitative data? In what situations could the number 42 be considered qualitative data?
What are the characteristics of a relational database?
When would using a personal DBMS make sense?
What is the difference between a spreadsheet and a database? List three differences between them.
Describe what the term normalization means.
Why is it important to define the data type of a field when designing a rational database?
Name a database you interact with frequently. What would some of the field names be?
What is metadata?
Name three advantages of using a data warehouse.
What is data mining?
In your own words, explain the difference between supervised learning and unsupervised learning. Give an example of each (not from the book).
Instructions:
Need minimum 500 words
Need 3 APA References
Use side headings to address the content according to questions.
Paper 1: Answer the following questions
What is the difference between data, information and knowledge?
Explain in your own words how the data component related to the hardware and software components of information systems.
What is the difference between quantitative data and qualitative data? In what situations could the number 42 be considered qualitative data?
What are the characteristics of a relational database?
When would using a personal DBMS make sense?
What is the difference between a spreadsheet and a database? List three differences between them.
Describe what the term normalization means.
Why is it important to define the data type of a field when designing a rational database?
Name a database you interact with frequently. What would some of the field names be?
What is metadata?
Name three advantages of using a data warehouse.
What is data mining?
In your own words, explain the difference between supervised learning and unsupervised learning. Give an example of each (not from the book).
Instructions:
Need minimum 500 words
Need 3 APA References
Use side headings to address the content according to questions.
Our assignment is to take this case study and create a PowerPoint Presentation of the Final Sponsor Presentation, as described in Chapter 12 of your text. (Make sure to use the format presented in Chapter 12.)
You will submit a PowerPoint Presentation file (ppt or pptx) with at least the following slides: (You can submit more, but I’m mainly looking for these six slides)
Title (Your name + other info)
Situation and Project Goals
Executive Summary
Approach
Method Description
Recommendations
Note: There is no direct information in the case study that covers the specific analytics methods used. You will extrapolate from the provided information in the case study and provide your own interpretation of the method (or methods) the Data Science team used. Draw from what we have covered this semester and map what you learned to what that team probably did. There is no rubric for this assignment. I want you to be as creative as possible. Tell the story. Inspire your sponsors.
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