I’m working on a computer science discussion question and need an explanation to help me learn.

Answer 1:

Data Analytics

It is difficult to use original data in analytical tasks since the data is not usable due to errors and lack of classification. Analytical strategies rely on detailed and definite data, which ensures an effective decision-making process (Sharda et al., 2020). Raw data may contain errors and may not be clean enough to use in analytical processes.

The first step of data preprocessing is data consolidation, which is an important step in data collection, collection, and integration. The process is important in analytical since it creates a framework to determine the data to use in the decision-making. For instance, Stieglitz et al. (2018) supported the utilization of social media framework is to provide data to use in analytical processes. It helps lay down a strong foundation of quality data to use in future analysis. The second step is data cleaning, which involves eliminating errors and reducing noise in the data. It is important in analytics due to eliminating errors that can affect the analytical process (Krishnan et al., 2016). It is an essential step since it reduces some critical errors and weaknesses in the data and promotes effective decision-making.

The third step is data transformation, which involves normalizing, creating attributes, and data normalization (Sharda et al., 2020). Data transformation is essential since it helps adjust the data to the high result and format. It makes it easy to ensure effective data analysis since the data analyst has a comprehensive data set to use in the decision-making. The fourth step is data reduction, which involves adjusting the dimensions, balancing the data, and reducing the volume. It is an important step that guides to ensure the data has a specific classification to engage in the analytical process. It generates an effective framework for strategic decision-making related to business development.

References

Krishnan, S., Wang, J., Wu, E., Franklin, M. J., & Goldberg, K. (2016). Activeclean: Interactive data cleaning for statistical modeling. Proceedings of the VLDB Endowment, 9(12), 948-959.

Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science, & artificial intelligence: Systems for decision support. Hoboken, N. J.: Pearson.

Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.

Answer 2:

Raw data is data that hasn’t been processed for use. Often a difference is drawn between information and data, implying that information results from data processing. Cooked data is a term used to describe raw data which has been processed. There is the various reason of not using the raw data by analytics tasks

• Raw Data that is denormalized, out of date, or poorly structured is common.
• Consistency, collaboration, and version control are not built-in features.
• Black boxes are frequently found in all-in-one solutions.
• It’s generally misaligned, dirty, unnecessarily complex, and incorrect.
• Data processing is essential for converting raw data into refined-data that is analytics-ready.
• In data analytics, the data have to be summarized and aggregated (Leapfrogbi, 2019).

Data Preprocessing Steps

Moreover, Data preprocessing is a process of converting raw data into a format that can be understood. Data in the real world is frequently inconsistent, incomplete, noisy, and redundant. Data preprocessing entails many steps that aid in converting raw information into a usable format.

Data Cleaning

It is the process of identifying corrupt data and incorrect records from a database table or recordset.

Data Integration

Data integration is concerned with unifying data from various sources and presenting a complete view of that data. Data from various representations are combined, as well as any conflicts that arise are resolved.

Data Transformation

Data transformation performs a crucial role in unprocessed data conversion into clear form. It contains data aggregation, generalization, and normalization.

Data Reduction

The process of converting digital data into a simplified and ordered format is known as data reduction. The majority of this data is provided through experimental and empirical methods.

Data Discretization

When we have a large volume of numeric data but want to interpret it based on nominal values, data discretization is critical. The continuous data is divided into discrete types in this case, and the values of such discrete sets are referred to as the nominal value (Agarwal, 2015, pp. 30-31).

Importance of Data Preprocessing Steps in Analytics

In analytics, the steps listed above are critical. Data cleaning is the process of identifying data that is inaccurate, incomplete, irrelevant or inconsistent, and then using techniques to change or delete it. Data integration is the process of combining data from various representations and resolving any conflicts that may arise. Data transformation enables users to obtain data in the most suitable way, making work easier when concluding. Data reduction is a technique for breaking down vast amounts of data into smaller, more meaningful chunks. Furthermore, with minimum information loss, data is discretized by transforming continuous data features into a finite set of intervals.

References

Agarwal, V. (2015). Research on data preprocessing and categorization technique for smartphone review analysis. International Journal of Computer Applications, 131(4), 30-36. doi: 10.5120/ijca2015907309

Leapfrogbi. (2019). Five reasons your data analyst can’t analyze. Retrieved from leapfrogbi: https://www.leapfrogbi.com/five-reasons-your-data-…