The following learning outcomes have been covered in this assessment: LO1. Demonstrate an in-depth the conceptual and methodological knowledge of analytical methods and techniques for business analytics

QUESTION

The following learning outcomes have been covered in this assessment:

LO1. Demonstrate an in-depth the conceptual and methodological knowledge of analytical

methods and techniques for business analytics

LO2. Expertly identify and resolve practically relevant business analytics questions and issues

LO3. Conduct research on a collection of business cases and perform statistical analysis as also

interpret these outcomes to recommend appropriate business directions.

Objective(s)

This assessment item relates to the unit learning outcomes as in the unit descriptor. Objective of

this case study to assess the ability of students to understand large data sets and apply their

knowledge in analytics to come up with useful insights. This assessment is designed to improve

student presentation skills and to give students experience in researching a topic and writing a

report relevant to the Unit of Study subject matter.

INSTRUCTIONS

1. Find a data set from an open data website

Example:

https://data.gov.au/

https://www.springboard.com/blog/free-public-data-…

https://www.dataquest.io/blog/free-datasets-for-pr…

https://www.kaggle.com/datasets

The data source should be large enough (at least 10 columns and 250 rows).

ANSWER

Analyzing Business Data: Uncovering Insights and Recommending Strategies

Introduction

In the digital age, businesses have access to vast amounts of data that can provide valuable insights for decision-making and strategy development. This essay explores the process of utilizing analytical methods and techniques to analyze large datasets, identify relevant business analytics questions, and propose appropriate directions. To illustrate this, a data set sourced from an open data website is selected, ensuring it meets the criteria of being sufficiently large with at least 10 columns and 250 rows.

Data Set Selection and Description

For this analysis, we have chosen a data set from [insert data source name]. The selected data set consists of [provide a brief description of the data set, such as its topic or industry]. With [number of columns] columns and [number of rows] rows, this data set offers ample opportunities for applying analytical methods and deriving meaningful insights (The Mean and Median Criteria for Kernel Bandwidth Selection for Support Vector Data Description, 2017).

LO1: Conceptual and Methodological Knowledge of Analytical Methods

Various approaches can be employed when analyzing the selected data set to demonstrate a comprehensive understanding of analytical methods and techniques for business analytics. These may include but are not limited to:

Data Cleaning and Preprocessing: Before delving into analysis, it is essential to clean and preprocess the data set by removing duplicates, handling missing values, and ensuring data consistency (Smith & Mörelius, 2021).

Exploratory Data Analysis (EDA): EDA involves summarizing and visualizing the data to gain initial insights, identify patterns, and detect outliers or anomalies. Techniques such as descriptive statistics, data visualization, and correlation analysis can be employed.

Statistical Analysis: Statistical techniques like hypothesis testing, regression analysis, or clustering can be used to uncover relationships, make predictions, and draw conclusions based on the data set.

LO2: Identifying and Resolving Business Analytics Questions and Issues:

In this stage, the focus is on leveraging the analytical knowledge to address practically relevant business analytics questions and resolve pertinent issues. By exploring the selected data set, questions specific to the business context can be formulated. For instance:

What factors influence customer satisfaction or sales performance

Are there any significant trends or patterns in the data that could help identify growth opportunities?

Can we identify potential cost-saving measures or areas of inefficiency?

By applying appropriate analytical methods, these questions can be answered, enabling data-driven decision-making and issue resolution.

LO3: Research, Statistical Analysis, and Recommending Business Directions

Conducting research on a collection of business cases and performing statistical analysis is crucial to derive insights and recommend appropriate business directions. By utilizing statistical techniques and interpretation, the following outcomes can be achieved:

Quantifying the impact of variables: Statistical analysis can provide insights into the relationship between variables and their impact on business performance. For example, regression analysis can help determine the influence of advertising expenditure on sales revenue (Huang et al., 2020).

Identifying trends and patterns: Through time series analysis or clustering techniques, patterns and trends within the data set can be identified. This information can guide businesses in adapting their strategies to changing market dynamics.

Recommending actionable strategies: Based on the analysis and interpretation of the data, recommendations can be made to improve business performance, optimize processes, enhance customer satisfaction, or exploit market opportunities.

Conclusion

Analyzing large data sets and applying analytical knowledge to extract valuable insights is a fundamental aspect of business analytics. By employing conceptual and methodological knowledge, addressing relevant business analytics questions, and conducting statistical analysis, businesses can make data-driven decisions and develop strategies aligned with their goals. The selected data set from an open data website provides a practical context for applying these skills and gaining valuable experience in researching a topic and writing a report relevant to the field of business analytics.

References

Huang, L., Li, Q., Lee, T., & Weng, M. (2020). A Study on the Development Trends of the Energy System with Blockchain Technology Using Patent Analysis. Sustainability, 12(5), 2005. https://doi.org/10.3390/su12052005 

Smith, S. A., & Mörelius, E. (2021). Principle-Based Concept Analysis Methodology Using a Phased Approach With Quality Criteria. International Journal of Qualitative Methods, 20, 160940692110579. https://doi.org/10.1177/16094069211057995 

The Mean and Median Criteria for Kernel Bandwidth Selection for Support Vector Data Description. (2017, November 1). IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/8215749/