Select 1 Data Action And Elaborate On The Actions Performed ✓ Solved

Select 1 data action and elaborate on the actions performed

Welcome to your forth week discussion. As you all read, according to Kirk (2016), most of your time will be spent working with your data. The four following group actions were mentioned by Kirk (2016): Data acquisition: Gathering the raw material; Data examination: Identifying physical properties and meaning; Data transformation: Enhancing your data through modification and consolidation; Data exploration: Using exploratory analysis and research techniques to learn. Select 1 data action and elaborate on the actions performed in that action group.

Paper For Above Instructions

In the realm of data analysis, one of the fundamental stages involves data acquisition, a crucial process that sets the foundation for subsequent analytical actions. Data acquisition refers to the gathering of raw data, which serves as the primary source for analysis. This process can encompass a variety of data types, including structured, unstructured, quantitative, and qualitative data. The importance of effective data acquisition cannot be overstated, as the quality and relevance of the data collected significantly influence the outcomes of any analysis undertaken.

Data acquisition typically begins with the identification of the information required to address a specific question or objective. This involves defining the research question clearly to ensure that the data collected aligns with the goals of the analysis. For example, if a researcher is interested in understanding consumer behavior, they may seek data from various sources such as surveys, purchase histories, social media interactions, or web analytics. Each of these sources provides valuable insights that can be interpreted to inform business strategies or academic inquiries.

Once the necessary data sources are identified, the next step in the data acquisition process involves gathering the raw data. There are several methods to collect data, including automated data collection tools, manual data entry, web scraping, and data extraction from databases. For instance, using programming languages like Python, analysts can automate the process of data scraping from websites, effectively compiling large datasets that would be cumbersome to collect manually. In addition, software tools such as SQL can be utilized to pull data from databases, ensuring accuracy and efficiency in the acquisition process.

After acquiring the data, it is essential to ensure its quality. Data quality encompasses aspects such as accuracy, consistency, completeness, and reliability. Analysts must verify that the data collected is free from errors and biases. This may involve cleaning the data to remove duplicates, correct inaccuracies, or handle missing values. Data cleaning is a vital step in the data acquisition process, as it directly impacts the reliability of the subsequent analysis. For instance, if a dataset contains numerous errors, it may lead to misguided conclusions and flawed decision-making.

Furthermore, data acquisition must also consider the ethical implications and regulations surrounding data collection. Issues such as data privacy, consent, and data ownership should be at the forefront of any data acquisition effort. Analysts are responsible for ensuring that they collect and handle data in compliance with relevant laws and regulations, such as the General Data Protection Regulation (GDPR) in Europe. This involves obtaining informed consent from individuals whose data is being collected and ensuring that data is stored securely to prevent unauthorized access.

In addition to ethical considerations, the technological aspects of data acquisition have evolved significantly with advancements in tools and methodologies. The rise of big data has introduced challenges and opportunities for data acquisition. Organizations now have access to vast amounts of data generated from various sources, including IoT devices, social media platforms, and transaction records. The challenge lies in effectively capturing and harnessing this data for analytical purposes. This often requires sophisticated data management systems and strategies to streamline the acquisition process and ensure that relevant data is continuously monitored and updated.

To summarize, data acquisition is a critical step in the data analysis process that lays the groundwork for meaningful insights. By focusing on effective data collection methods, ensuring data quality, and adhering to ethical standards, analysts can equip themselves with the necessary tools to navigate the vast landscape of data effectively. The actions involved in data acquisition are multi-faceted and require careful planning, execution, and continuous evaluation to ensure successful outcomes in data-driven projects.

References

• Kirk, A. (2016). Data Visualization: A Successful Design Process. CRC Press.
• Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media.
• Sharma, S. (2021). Data Acquisition in Data Science: Importance and Techniques. Journal of Data Science.
• Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
• Harris, D. (2019). The Basics of Data Science: Data Mining and Data Analysis. Springer.
• Choudhury, A. (2020). Ethical Issues in Data Collection. Ethics and Information Technology.
• IEC 27001 (2013). Information technology - Security techniques - Information security management systems - Requirements. International Organization for Standardization.
• Gill, D., & Power, D. J. (2021). Big Data and the Future of Data Acquisition. Journal of Business Analytics.
• Knorr-Cetina, K. (2009). Epistemic Cultures: How the Sciences Make Knowledge. Harvard University Press.
• O'Reilly, T. (2013). Data, Data Everywhere: Use Redshift, Hive, and More to Unify Business Data. O'Reilly Media.