IT475: Decision Support Systems Assessment

Assignment Overview 

Instructions

• You must submit two separate copies (one Word file and one PDF file) using the Assignment Template on Blackboard via the allocated folder. These files must not be in compressed format.
• It is your responsibility to check and make sure that you have uploaded both the correct files.
• Zero mark will be given if you try to bypass the SafeAssign (e.g., misspell words, remove spaces between words, hide characters, use different character sets, convert text into image or languages other than English or any kind of manipulation).
• Email submission will not be accepted.
• You are advised to make your work clear and well-presented. This includes filling your information on the cover page.
• You must use this template, failing which will result in zero mark.
• You MUST show all your work, and text must not be converted into an image, unless specified otherwise by the question.
• Late submission will result in ZERO mark.
• The work should be your own, copying from students or other resources will result in ZERO mark.
• Use Times New Roman font for all your answers.

Project Tasks & Rubric 

Evidence rule: every task must include screenshots/figures and a 2-4 sentence interpretation (what it shows, why it matters for the decision).

Task 1: Problem & Data Understanding 

• Decision context: Who is the decision-maker? What decision will the analysis support? Define KPIs (2–4).
• Dataset description: source reliability, collection method, size, time span, unit of analysis.
• Data dictionary: list key features, types, and expected roles (predictor/target/ID).
• Hypothesis: one testable relationship between two numerical variables (directional, with rationale).

Task 2: Data Quality & Preparation 

• Show tests and fixes with before/after evidence for:
  • Missing values
  • Duplicates
  • Outliers
  • Noise/irregularities (e.g., inconsistent categories, types, units)
• Include a Data Quality Log table: issue → method → action → impact.

Task 3: Descriptive Statistics & EDA 

• Central tendency (mean/median/mode) and distribution shape (variance, SD, skewness, kurtosis).
• Appropriate visuals (histograms/boxplots/density, bar/line where relevant).
• 2–3 insightful questions you posed from trends/patterns (and brief answers).

Task 4: Hypothesis Testing & Relationship Analysis 

• Correlation analysis (numeric pair; comment on strength/direction.
• Simple linear regression (or appropriate alternative): equation, R⊃2;, residuals check, and practical interpretation linked to KPIs.
• Conclusion: accept/reject hypothesis; implications for the decision.

Task 5 : Visual Analytics for Decision-Makers 

• A small, coherent visual story (3 - 4 charts) with correct chart types, clear labels, and callouts.
• Each chart must answer a stakeholder-relevant question; include a 1–2 sentence takeaway.

Task 6: Predictive/Descriptive Modelling 

• Choose 1 – 2  models suitable for your data/task (e.g., Decision Tree, k-NN, Random Forest, SVM, k-means for segmentation if classification/regression is not applicable).
• Document training setup (feature set, split).
• Evaluation:
  • For classification: confusion matrix, accuracy, precision/recall, and 1 key trade-off.
  • For regression: MAE/RMSE and an error plot.
  • For clustering: silhouette (or WCSS elbow) + business interpretation of clusters.
• Brief model selection rationale tied to the decision.

Task 7: Interactive Dashboard & Decision Support 

• Excel or Power BI dashboard with 3–5 tiles: KPIs, filters/slicers, and at least one “what-if” (e.g., price, volume, threshold).
• One paragraph on how a manager would use this dashboard to make or justify a decision.

Assessment Requirements Summary

The IT475 Decision Support Systems Assessment is a comprehensive evaluation designed to assess a student’s ability to apply analytical, statistical, and data visualization techniques to support managerial decision-making. The task requires students to demonstrate competence in problem understanding, data preparation, hypothesis testing, and model development using suitable decision-support tools such as Excel or Power BI.

The assessment is divided into seven major tasks, each contributing to a holistic understanding of decision-making processes through data analysis. Below is a brief summary of the key requirements:

1. Task 1: Problem and Data Understanding

   • Define the decision context (decision-maker, purpose, and KPIs).

   • Describe the dataset, including source, collection methods, and key features.

   • Formulate one hypothesis between two numerical variables, stating the rationale.

2. Task 2: Data Quality and Preparation

   • Detect and correct data issues such as missing values, duplicates, outliers, and inconsistencies.

   • Provide before-and-after screenshots and maintain a Data Quality Log summarizing actions and impacts.

3. Task 3: Descriptive Statistics and Exploratory Data Analysis (EDA)

   • Compute central tendency and distribution measures (mean, SD, skewness, kurtosis).

   • Create meaningful visuals such as histograms and boxplots.

   • Pose 2–3 analytical questions based on trends and patterns.

4. Task 4: Hypothesis Testing and Relationship Analysis

   • Conduct correlation analysis and linear regression to test the proposed hypothesis.

   • Interpret statistical results and determine whether to accept or reject the hypothesis.

5. Task 5: Visual Analytics for Decision-Makers

   • Create 3–4 visual charts that tell a coherent story, each with clear interpretation and decision relevance.

6. Task 6: Predictive/Descriptive Modelling

   • Develop and evaluate predictive or clustering models (Decision Tree, SVM, k-means, etc.).

   • Present model performance using metrics such as accuracy, MAE, or silhouette scores.

7. Task 7: Interactive Dashboard and Decision Support

   • Build an interactive dashboard (Excel or Power BI) with KPIs, filters, and “what-if” analysis.

   • Explain how the dashboard aids managerial decisions.

Additionally, students are expected to submit two versions (Word and PDF) of the assignment following the provided template, ensuring clarity, integrity, and originality. Any attempt to manipulate file content or SafeAssign checks results in a zero grade.

Mentor’s Step-by-Step Guidance in Completing the Assessment

The academic mentor provided structured support throughout the assessment, ensuring that the student not only completed each task correctly but also understood the underlying logic and learning outcomes. The mentor’s guidance followed a systematic and pedagogical approach.

Step 1: Understanding the Decision Problem and Dataset (Task 1)

The mentor began by explaining the importance of contextual understanding in Decision Support Systems (DSS).

• The student was guided to identify who the decision-maker was (e.g., a marketing manager, sales director, or operations analyst).

• The mentor emphasized defining 2–4 relevant KPIs—for example, sales growth, customer retention rate, or defect rate reduction.

• The dataset was then analyzed for source credibility, data type, and time span to establish analytical reliability.

• Finally, the mentor helped formulate a clear hypothesis, such as “Sales Revenue increases with Marketing Expenditure,” ensuring it was testable and directional.

This task strengthened the student’s skills in problem framing and data comprehension, both fundamental in decision-making systems.

Step 2: Data Cleaning and Preparation (Task 2)

The mentor highlighted that data quality is foundational to any DSS analysis. The student learned to perform:

• Missing value treatment using imputation or removal methods.

• Duplicate elimination to avoid data bias.

• Outlier detection using boxplots and z-scores.

• Data type consistency checks to standardize variables (e.g., converting text to numerical).

The Data Quality Log was prepared in tabular format, detailing the issue, corrective method, action taken, and resulting impact. Screenshots before and after data cleaning served as evidence of work authenticity.

Through this step, the student developed a deep appreciation for data governance and preprocessing protocols, aligning with the learning objective of ensuring data readiness for analysis.

Step 3: Conducting Descriptive Analysis and EDA (Task 3)

At this stage, the mentor introduced the student to statistical summarization and visualization. Using tools like Excel, Python, or Power BI, the student:

• Calculated mean, median, mode, variance, skewness, and kurtosis to understand data distribution.

• Created visuals (histograms, boxplots, and trend charts) to uncover patterns.

• Framed insightful questions, such as “Does customer satisfaction vary by region?” or “What patterns are visible across sales cycles?”

The mentor guided the interpretation of each finding, emphasizing how descriptive analytics supports evidence-based decision-making.

Step 4: Hypothesis Testing and Relationship Analysis (Task 4)

The mentor taught the student to apply correlation and regression analysis to test hypotheses.

• Correlation coefficients were used to measure relationship strength and direction.

• Simple linear regression helped establish predictive equations (e.g., Sales = α + β × Marketing Spend).

• The mentor explained R⊃2; values, residual diagnostics, and p-value interpretation to determine significance.

The student concluded whether to accept or reject the hypothesis, linking the findings to practical KPIs. This step met the objective of connecting statistical inference with managerial insight.

Step 5: Visual Storytelling for Decision-Makers (Task 5)

The mentor introduced visual analytics techniques, showing how to transform raw analysis into actionable insights.

• The student designed 3–4 business-friendly charts (trend lines, clustered bars, or pie charts).

• Each chart was accompanied by a short takeaway, explaining what the visual revealed and why it mattered.

The mentor emphasized principles of clarity, relevance, and consistency—essential in executive reporting. This reinforced the DSS concept of transforming data into decision-centric narratives.

Step 6: Predictive Modelling and Evaluation (Task 6)

In this phase, the mentor guided the student through model selection and evaluation.

• Depending on the data, the student selected models like Decision Tree for classification or Linear Regression for prediction.

• The mentor helped configure training/testing splits, evaluate performance using MAE, RMSE, or accuracy, and interpret results.

• The rationale for choosing each model was tied to the business decision context, ensuring academic and practical alignment.

This stage strengthened the student’s competence in data-driven forecasting and prescriptive analysis, key components of modern decision support systems.

Step 7: Interactive Dashboard Development (Task 7)

Finally, the mentor assisted the student in creating an interactive Power BI dashboard.

• The dashboard included 3–5 tiles featuring KPIs, slicers, and a “what-if” scenario (e.g., pricing sensitivity).

• The mentor ensured the design allowed managers to simulate decisions dynamically.

• The student wrote a short explanation of how the dashboard enables effective business decision-making.

Through this task, the student demonstrated mastery of visual analytics integration within DSS environments.

Outcome and Learning Achievements

By the end of the assessment, the student produced a complete decision-support analysis report meeting all academic and technical requirements. Each task contributed to specific learning outcomes, summarized as follows:

Task 1: Problem definition and KPI identification
Task 2: Data quality assessment and preparation
Task 3: Descriptive analysis and visualization
Task 4: Hypothesis formulation and testing
Task 5: Effective data storytelling
Task 6: Predictive modelling and evaluation
Task 7: Decision dashboard creation and interpretation

The final submission included both Word and PDF formats, demonstrating professionalism and adherence to submission protocols. The student not only learned how to execute each analytical step but also understood the strategic relevance of Decision Support Systems in enhancing business performance and managerial efficiency.

Conclusion

The IT475 Decision Support Systems Assessment exemplifies a balanced integration of analytical rigor, technical skill, and strategic thinking. Under the mentor’s structured guidance, the student progressed from understanding raw data to generating actionable insights that could influence business decisions.

Ultimately, the assessment helped the student achieve the core learning goals of the unit: critical data interpretation, decision modeling, and analytical reasoning, thereby equipping them with practical competencies applicable in modern data-driven organizations.

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