代做MGMT6016 Strategic Supply Chain Management Assessment 2 Semester-1, 2024代做留学生SQL语言程序

Criteria

80 – 100%

Exceeds Expectations (High Distinction)

70 – 79 % Exceeds Expectations (Distinction)

50 – 69 %

Meets Expectations (Pass/Credit)

0 – 49%

 Below Expectation (Fail)

Data Quality -Identification of Issues (5 marks)

Accurately identifies all major data quality issues (missing values, inconsistencies, formatting errors, etc.) in the dataset. Provides clear explanations for each identified issue.

Identifies most major data quality issues, but may miss some minor inconsistencies or formatting errors. Explanations for identified issues are mostly clear.

Identifies some data quality issues, but may miss several important ones. Explanations for identified issues are lacking or unclear.

Fails to identify any significant data quality issues or provides inaccurate explanations for identified issues.

Data Quality -Cleaning Techniques (5 marks)

Applies appropriate data cleaning techniques (e.g., filling missing values, correcting inconsistencies, formatting data consistently) to address the identified issues. Documents the cleaning process clearly and provides rationale for chosen techniques.

Applies most appropriate data cleaning techniques to address major issues, but may overlook minor inconsistencies or formatting errors. Documentation of cleaning process is partially complete or lacks rationale for chosen techniques.

Applies some basic data cleaning techniques to address identified issues, but may not address all issues effectively. Documentation of cleaning process is lacking or unclear.

Fails to apply appropriate data cleaning techniques or provides incorrect solutions to identified issues. Documentation is missing.

Data Exploration - Descriptive Statistics (7.5 marks)

Calculates and interprets relevant descriptive statistics (e.g., mean, median, standard deviation) for key variables. Clearly explains how these statistics help understand the data distribution.

Calculates some descriptive statistics for key variables but may not interpret them fully or explain their significance clearly.

Calculates limited descriptive statistics or fails to interpret them effectively.

Fails to calculate or interpret any descriptive statistics.

Data Exploration - Visualizations (7.5 marks)

Creates a variety of informative and well-designed visualizations (e.g., histograms, boxplots, scatterplots) to depict the distribution of important variables. Clearly explains how each visualization contributes to understanding the data.

Creates some visualizations but may not choose the most appropriate types or fail to explain their significance clearly. Visualizations may lack clarity or contain errors.

Creates limited or poorly designed visualizations that do not effectively represent the data.

Fails to create any visualizations or creates visualizations that are irrelevant or misleading.

Data Exploration - Patterns and Outliers (7.5 marks)

Effectively identifies and interprets patterns, trends, and outliers in the data. Explains the potential reasons behind these observations and their potential impact on analysis.

Identifies some patterns or outliers but may not fully explain their significance or provide potential reasons for their occurrence.

Identifies limited patterns or outliers or fails to provide meaningful explanations for them.

Fails to identify any significant patterns or outliers in the data.

Data Exploration - Relationships between Variables (7.5 marks)

Explores relationships between key variables using appropriate techniques (e.g., correlation analysis, scatterplots). Clearly explains the methods used and interprets the results, highlighting any significant correlations or relationships.

Explores some relationships between variables but may not choose the most appropriate techniques or fully explain the results or their significance.

Explores limited relationships between variables or fails to interpret the findings effectively.

Fails to explore any relationships between variables or uses irrelevant or inappropriate techniques.