Blockchain Technology: Week 1 Introduction

Blockchain Technology 3 n week one we will discuss the introduction into data mining concepts
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, AI, 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 organization’s 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. Each journal should be a minimum of 250 words. The purpose of this reflective journal is self-reflection regarding the role in the process of self-reflection as a PMHNP provider. Through reflective practice, the student will evaluate their own emotional health and recognize one’s own feelings as well as one’s ability to monitor and manage those feelings. The point of the exercise is to learn yourself, your triggers, the types of cases you end up getting overly involved with, and those you’d rather refer to someone else. The idea is to be able to personally reflect on your behaviors/thoughts/decisions and how those impact you in the role of PMHNP. · Describe a new experience, significant event/patient interaction, never seen before diagnosis, etc… you have experienced in clinical this week . You can choose the same case you used for your SOAP Note this week if that case had an effect on you. Discuss the impact this had on you in terms of increasing your understanding of the PMHNP role, psychopathology, and/or the provider-patient relationship. Explore your personal strengths and limitations and their effect on the provider/patient relationship. Include reflection on your therapeutic use of self. Your discussion should reflect your specific learning/insight. Identify something specific that you learned from reflecting on this event/interaction and how will you apply that learning in your future practice?