In this assignment, submit your topic and preliminary refere…
In this assignment, submit your topic and preliminary references, in APA format, that you will use when completing your final research paper. Your submission should include the following elements: Your final research paper will be due in week 8. part 2: discussion: Discussion 1 (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics. part 3: Complete the following assignment in one MS word document: Chapter 3 –discussion question #1- 4 ( this means 1,2,3 & 4) & exercise 12 When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
The research paper on this topic aims to explore the reasons why original/raw data is not readily usable by analytics tasks, as well as the importance of data preprocessing steps in analytics. A comprehensive review of the literature will be conducted to gather information on these topics. In this preliminary stage, I have compiled a list of relevant references in APA format that will serve as the foundation for the research paper.
References:
1. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Morgan Kaufmann.
This book provides a comprehensive overview of data mining concepts and techniques. It covers topics such as data preprocessing, data cleaning, and data integration, which are all critical steps in preparing data for analytics tasks. The book also discusses the challenges and strategies in handling raw data and extracting meaningful insights from it.
2. Witten, I. H., Frank, E., & Hall, M. A. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
This book offers practical guidance on data mining techniques and tools. It covers various aspects of data preprocessing, including data cleaning, attribute selection, and data transformation. The authors highlight the importance of these preprocessing steps in improving the quality and usability of data for analytics tasks.
3. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37-54.
This article discusses the process of knowledge discovery in databases, which encompasses data preprocessing as an essential step. The authors explain the different data preprocessing techniques, such as data cleaning, data integration, and data transformation, and their significance in uncovering valuable insights from raw data.
4. Batista, G. E., & Monard, M. C. (2003). An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence, 17(5-6), 519-533.
This study examines the impact of missing data on supervised learning algorithms and evaluates four different methods for handling missing data. The authors emphasize the importance of data preprocessing techniques, such as data imputation, in dealing with missing values to ensure accurate and reliable analysis results.
These preliminary references provide a solid starting point for the research paper on data preprocessing and its role in analytics. They encompass a broad range of topics, including data cleaning, data integration, attribute selection, and data transformation, which are all crucial steps in preparing data for analytics tasks. In the final research paper, these references will be further explored and integrated with additional sources to build a comprehensive argument and provide a deeper understanding of the topic.