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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.
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?
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.
Describe how the chosen knowledge system (beyond those discussed by your peers) can improve an organization or a department within an organization.
Create the basic design for the research DBMS that you propose including the main tables in the database, using Microsoft Word Shapes.
Which DBMS Architecture would you choose from Section 2.5? Why? Why would the other architectures not be a good choice?
Using search engines and find two different recent articles involving data mining. Describe the role of data mining in the story using your own words.
What is knowledge discovery in databases (KDD)? Note how data mining integrates with the components of statistics and AL, ML, and Pattern Recognition.
Using data governance techniques like data cleansing and de-duplication. Why is this effort necessary? Briefly explain and support from your readings.