Read Chapter 3 In Analytics Data Science Artificial Intellig

Read Chapter 3 In Analytics Data Science Artificial Intelligencecr

Read Chapter 3 in Analytics, Data Science & Artificial Intelligence Create a discussion thread (with your name) and answer the following question: Discussion (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. Note: The first post should be made by Tuesday11:59 p.m., EST. I am looking for active engagement in the discussion. Please engage early and often. Respond to two postings provided by your classmates. There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations.

Paper For Above instruction

The utilization of raw data in analytics tasks is often impeded by its unprocessed nature, which renders it unsuitable for immediate analysis. Raw data typically contains inconsistencies, missing values, noise, and irrelevant information that can distort analytical results if not properly addressed. Consequently, data preprocessing becomes a critical step in the analytics pipeline, transforming raw data into a clean, structured, and high-quality dataset fit for accurate and insightful analysis.

One fundamental reason raw data is not readily usable is its inherent inconsistency. Data collected from multiple sources or systems often differs in formats, units, or coding schemes, leading to discrepancies that complicate analysis. For instance, categorical variables may be labeled differently across datasets, or numerical data might be recorded in various units. Such inconsistencies need standardization to ensure that the analysis is valid and meaningful.

Another common issue with raw data is missing values. Incomplete data entries can bias the analysis, reduce statistical power, and lead to unreliable models. Data preprocessing involves strategies like imputation or deletion to handle missing data effectively, preserving the integrity of the dataset. Additionally, raw data often contains noise—random variations or errors—that can obscure true patterns. Techniques such as smoothing or filtering help reduce this noise, thereby enhancing the clarity of the data.

Furthermore, irrelevant or redundant features in raw data can hinder model performance. Feature selection and dimensionality reduction are essential preprocessing steps that identify and retain only the most informative variables, simplifying models and improving computational efficiency. Data transformation methods, such as normalization or scaling, are crucial for algorithms sensitive to data magnitude, ensuring that variables contribute proportionally to the analysis.

The importance of data preprocessing in analytics cannot be overstated. It ensures data quality, enhances model accuracy, reduces computational complexity, and ultimately leads to more reliable insights. Proper preprocessing facilitates the extraction of meaningful patterns, aiding in decision-making, predictive modeling, and other analytics applications. Without these steps, analytics efforts risk being compromised by flawed or biased results, underscoring the necessity of meticulous data preparation.

References

• Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
• Kotu, V., & Deshpande, B. (2019). Data Science: Concepts and Practice. Morgan Kaufmann.
• García, S., Luengo, J., & Herrera, F. (2015). Data Preprocessing in Data Mining. Springer.
• Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
• Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.