14 Nov Select one key concept that we’ve learned in the course to date and answer the following: Define the concept. Note its importance to data science. Discuss corresponding concepts that a
Select one key concept that we've learned in the course to date and answer the following:
- Define the concept.
- Note its importance to data science.
- Discuss corresponding concepts that are of importance to the selected concept.
- Note a project where this concept would be used.
The paper should be between 2-3 pages and formatted using APA 7 format. Two peer-reviewed sources should be utilized to connect your thoughts to current published works.
Running head: BLOCKCHAIN TECHNOLOGY 1
BLOCKCHAIN TECHNOLOGY 3
n week one we will discuss the introduction into data mining concepts. We focus on the importance of data algorithms and how different methods can derive different results.
Objectives:
1. Define the importance of understanding the differences in different data algorithms and the output variance.
2. Explain how different output can occur when managing different data algorithms.
3. Comprehend the various motivating challenges with data mining.
4. Understand how data mining integrates with the various components of statistics, AL, ML, and Pattern Recognition.
5. Explain the difference between predictive and descriptive tasks and the importance of each.
In week two we will review a use case on traditional data collection methods and the downfalls. We also discuss data attributes and classification this week.
Objectives:
1. Comprehend the traditional methods of data collection and the challenges of traditional methods compared to automated methods.
2. Discuss the concepts of optimization and performance measurement in a real-world example.
3. Understand the key components of attributes including the different types and the importance of each.
4. Explain the difference between discrete and continuous data.
5. Compare the pitfalls and benefits of model selection and evaluation.
6. Explain the concepts in data classification.
n week three we discuss the various types of classifiers used in data mining. We also utilize a real-world example and discuss how opinion mining is used in information retrieval and is used with NLP techniques.
Objectives:
1. Define the various types of classifiers.
2. Understand the key components to logic regression.
3. Compare and contrast nearest neighbor and naïve Bayes classifiers.
4. Discuss a real-world example on opinion mining and how it is used in information retrieval.
5. Explain the various components and techniques of opinion mining and the importance to transforming an organizations NLP framework.
Week 4
1. Understand the concept of the association rule in data mining.
2. Explain how the association rule is important in big data analysis.
3. Interpret how the association rule allows for more advanced data interpretation.
4. Utilize the lessons learned up to date in this course to complete the midterm.
5. Examine how all of the work to date builds within the data mining framework.
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