Computer Science Homework Help

Week3:Read:

1. ch. 2 in textbook: Data and Classification: Basic      Concepts

Chapter 2:

1. What is an attribute and note the importance?
2. What are the different types of attributes?
3. What is the difference between discrete and continuous      data?
4. Why is data quality important?
5. What occurs in data preprocessing?
6. In section 2.4, review the measures of similarity and      dissimilarity, select one topic and note the key factors.

In an APA7 formatted answer all questions above. There should be headings to each of the questions above as well. Ensure there are at least two-peer reviewed sources to support your work.

• Should return Main Discussion      Content in 15hrs.
• once I receive the main      discussion content then I will post the peer discussion in chat
• peer response should be      returned in next 10hrs.

Week4:

Read:

1. ch 3 in textbook: Alternative Techniques
2. Capri, H. (2016). Data mining?: principles,      applications and emerging challenges . Nova      Publishers. Chapter 1.

Discussion:

After completing the reading this week answer the following questions:

Chapter 3:

1. Note the basic concepts in data classification.
2. Discuss the general framework for classification.
3. What is a decision tree and decision tree      modifier?  Note the importance.
4. What is a hyper-parameter?
5. Note the pitfalls of model selection and evaluation.

In an APA7 formatted answer all questions above. There should be headings to each of the questions above as well. Ensure there are at least two-peer reviewed sources to support your work.

• Should return Main Discussion      Content in 15hrs.
• once I receive the main      discussion content then I will post the peer discussion in chat
• peer response should be      returned in next 10hrs.

Week 4 Essay work

In an essay format answer the following questions:

In essay format answer the following questions:

After reading the chapter by Capri (2015) on manual data collection.  Answer the following questions:

1. What      were the traditional methods of data collection in the transit system?
2. Why      are the traditional methods insufficient in satisfying the requirement of      data collection?
3. Give      a synopsis of the case study and your thoughts regarding the requirements      of the optimization and performance measurement requirements and the      impact to expensive and labor-intensive nature.