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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?
Create a simple class diagram containing three classes: Vehicle, Car, and Truck. Provide two attributes for each of these three classes.
Discuss the risk and costs of compromised data integrity breaches. Focus on integrity not confidentiality. Look at military, education, science, medicine, etc.
When thinking about data visualization, it important to understand regular expressions in data analytics. Discuss difference between two types of expressions.
Create one instrument that could be used to gather data for your mock dissertation topic.
On average what location will she be able to pay off her house first based on average housing prices and income she will receive?
Discussion Post: Pet Food Customer Orders Data Insights. Which customers order and reorder the wet food, and when are they likely to try it?
Discuss the scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.
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.
Define your site structure and the file naming rules. How would you take advantage of Search Engine Optimization.
Discuss the characteristics of relations that make them different from ordinary tables and files.
Distinguish data mining from other analytical tools and techniques. Discuss the main data mining methods. What are the fundamental differences among them?
Identify the common terms found in insurance contracts that specify exactly what risks an insurer will cover.
What challenges does your organization face in ensuring that the data mining models are receiving clean data?
What is an attribute and note the importance? What are the different types of attributes? What is the difference between discrete and continuous data?
You will design a qualitative instrument that could potentially answer your topic/research question if it were to be applied to a qualitative study.
What are the sources and the nature of those incoming data? What are the most common metrics that make for analytics-ready data?
If you have no experience with either language, discuss how you foresee using either/both of these languages in visualizing data when analyzing big data.
List the strong entity types in the ER diagram. Is there a weak entity type? If so, give its name, its partial key, and its identifying relationship.
Write at least 700 words paper on what Big means in Big Data. What exposure have you had to Big Data?
Demonstrate how this "Analyzing and visualizing data" course research has connected and put into practice within their work and career?
What is knowledge discovery in databases (KDD)? Note the difference between predictive and descriptive tasks and the importance of each.
Identify the benefits and challenges associated with that concept. Do not simply list the benefits and challenges but detail them in a substantive.
What did the Snowden incident teach people about government surveillance? Be sure to include the ethical issues of your discussion.