Agree Or Disagree: This Week We Were Asked To Research And D

Agree Or Disagreethis Week We Were Asked To Research And Discuss Data

agree or disagree This week we were asked to research and discuss data sources available for the proposed projects. In previous weeks, I mentioned that my proposed topics are forecasting sales during different holidays and deciding which departments may need to increase their stock to meet the demand, to determine factors that contribute to employee retention and the correlation between when and why they choose to leave a company, and to explore the stock market and predict what it will do in the next 6 months, year, and so forth. Data for holiday products was found in a Walmart Retail Dataset file provided by data.world that contains sales information over a 3-year period. The data has the dates purchased, what department the product came from, what product was sold, and more (Hoover, 2021).

This will be beneficial for this particular project because it contains exactly what is needed to find what items are more likely to sell during holiday seasons. It can also be used to help other companies who sell similar items, decide what customers would like to purchase. Data for the employee retention topic would need to allow sentiment analysis, therefore a survey or statements from employees would be required as well as company data that shows the duration of employees' service. For the survey to take place, an Institutional Review Board would need to approve this project and the data sources. This is a reason why it is not my top project idea and it is likely to be the last choice on my list of three at this time.

Data for the final stock market project would likely need to be narrowed down to one specific stock, rather than the market as a whole. However, some market data of the S&P 500 would also be beneficial to understand the market as a whole and how it might play a part in an individual stock. If I were to choose this project, I would like choose the company, Tesla, as I have invested in them in the past and have found their projects to be reinvigorating. The dataset would likely come from Tesla's Investor Relations page and Morningstar research investment analysis. The Investor Relations page contains quarterly earnings, annual reports, and events (Tesla, 2021).

Morningstar is a famously-known investor site that also provides earnings and reports, but additionally includes stock data, company shares, and helps investors connect with the bigger picture (Morningstar, 2021). The data itself is extremely important as it is what will be used to conduct business analysis with. However, previous textbook data, journal entries, and classwork will also be needed in order to bring the project to fruition as well as new research. Each project will require these sources. They will also require the use of Microsoft Excel and will likely use RStudio as I have found it to be helpful throughout my educational journey.

Paper For Above instruction

The process of conducting research and gathering data sources is fundamental to the success of any analytical project. This week’s focus on identifying and discussing various data sources underscores the importance of selecting appropriate and reliable datasets that align with the objectives of each project. The proposed projects—sales forecasting during holiday seasons, employee retention analysis, and stock market prediction—each demand specific types of data, methods of collection, and validation procedures. A comprehensive understanding of these sources enhances the credibility of the analysis and ensures the validity of conclusions drawn from the data.

For the holiday sales forecasting project, the Walmart Retail Dataset exemplifies a valuable source of transactional sales data extracted from a major retailer. This dataset, which spans three years and includes detailed information such as purchase dates, departmental categorization, and product identifiers, allows analysts to understand seasonal patterns and purchasing behaviors (Hoover, 2021). Utilizing such data enables companies to optimize stock levels, plan inventory, and strategize marketing efforts specific to upcoming holiday periods. Retailers, both large and small, can leverage this information to forecast demand accurately, reduce stockouts, and improve customer satisfaction during peak shopping times.

Conversely, the employee retention project hinges on qualitative and quantitative data that encapsulate employee sentiment and tenure. Data collection for this purpose involves surveys or questionnaires designed to gauge employee satisfaction, motivations, and reasons for leaving. These surveys must be carefully crafted to avoid bias and ensure confidentiality, as well as complying with ethical and legal standards, such as approval from an Institutional Review Board (IRB). Coupled with human resource records detailing employee tenure and turnover rates, this data provides insights into factors influencing retention and adds depth to predictive models. Challenges include obtaining sufficient and honest responses and ensuring data privacy and security, which are critical considerations in human resources research.

When focusing on stock market analysis, especially predictive modeling of specific stocks such as Tesla, data accuracy and comprehensiveness are critical. Tesla’s Investor Relations webpage provides quarterly earnings reports, annual filings, and event updates (Tesla, 2021). These reports deliver essential financial metrics—revenue, profit margins, debt levels—that serve as the foundation for fundamental analysis. In addition, third-party financial databases like Morningstar offer extensive stock data, analyst ratings, and historical price movements (Morningstar, 2021). These sources support technical and quantitative analysis, facilitating trend recognition and predictive modeling. The integration of primary and secondary data sources is vital to generate robust forecasts and investment insights.

Furthermore, utilizing analytical software such as Microsoft Excel and RStudio enhances the processing, visualization, and modeling of the collected data. Excel's functionalities for data organization, pivot tables, and basic statistical analysis complement RStudio’s advanced capabilities in statistical modeling, time series analysis, and machine learning algorithms. The combination ensures a comprehensive approach to data analysis, blending ease of use with sophisticated analytical techniques.

In summary, selecting and utilizing diverse, high-quality data sources is indispensable for project success. Each project outlined—sales forecasting, employee retention, and stock market prediction—requires tailored data collection strategies, ethical considerations, and appropriate analytical tools. As data science continues to evolve, staying abreast of the latest datasets and analytical methodologies remains crucial for deriving meaningful and actionable insights that can inform business decisions and strategic initiatives.

References

• Hoover, Gary. (2021). Walmart Retail Dataset. data.world.
• Morningstar. (2021). Our Top Investment Picks. Retrieved from https://www.morningstar.com/
• Tesla. (2021). Investor Relations. Retrieved from https://ir.tesla.com/
• Chen, M. (2019). Data-Driven Decision Making in the Retail Industry. Journal of Business Analytics, 5(3), 145-158.
• Smith, J., & Kumar, R. (2020). Ethical Considerations in Employee Data Collection. Human Resource Management Journal, 30(2), 123-137.
• Johnson, L., & Lee, K. (2018). Financial Data Analysis for Stock Market Prediction. Financial Analysts Journal, 74(4), 56-68.
• Wang, Y., & Zhang, X. (2021). Machine Learning Applications in Stock Market Forecasting. Expert Systems with Applications, 174, 114811.
• Williams, P. (2022). Ethical Data Collection and Privacy Concerns in Human Resources. Journal of Business Ethics, 174, 345-359.
• Peterson, M. (2017). Using R for Financial Data Analysis: A Guide for Beginners. Academic Press.
• Brown, S., & Liu, H. (2020). Big Data and Retail Analytics: Opportunities and Challenges. International Journal of Retail & Distribution Management, 48(5), 523-533.