1. Tests and Quizzes

1.1. Sample Multiple-Choice Quiz

Subject: Introduction to Data Science
Topic: Basic Statistics and Probability

Instructions: Choose the correct answer for each question. Each question is worth 1 point.

1. What is the mean of the following data set: {5, 8, 10, 12, 15}?
   • a) 8.0
   • b) 10.0
   • c) 12.0
   • d) 11.0
2. Which of the following is NOT a measure of central tendency?
   • a) Mean
   • b) Median
   • c) Mode
   • d) Variance
3. What is the probability of flipping a coin and landing heads?
   • a) 1/2
   • b) 1/3
   • c) 1/4
   • d) 1
4. Which distribution is typically used to model the number of successes in a fixed number of independent Bernoulli trials?
   • a) Normal Distribution
   • b) Poisson Distribution
   • c) Binomial Distribution
   • d) Exponential Distribution

---

1.2. Sample True/False Question

Subject: Machine Learning
Topic: Supervised vs. Unsupervised Learning

Instructions: Write “True” or “False” next to each statement.

1. In supervised learning, the algorithm learns from labeled data to make predictions. _______
2. Unsupervised learning is used when the data is already labeled. _______
3. K-means clustering is an example of unsupervised learning. _______
4. In supervised learning, the output variable is known during the training process. _______

---

2. Assignments

2.1. Programming Assignment

Subject: Python for Data Science
Topic: Data Cleaning and Preprocessing

Instructions:
Using the provided dataset (CSV format), complete the following tasks in Python:

1. Load the dataset and inspect the first 10 rows.
2. Handle missing values by using appropriate techniques (e.g., imputation, removal).
3. Remove duplicates from the dataset and explain how they were identified.
4. Normalize the data columns to ensure they are on a similar scale (use StandardScaler or MinMaxScaler).
5. Provide a summary of your findings and discuss how the data was prepared for analysis.

Submission Format:
Submit the Jupyter Notebook file (.ipynb) containing the code, along with an explanation for each step you performed.

---

2.2. Research Paper Assignment

Subject: Ethical Data Science
Topic: Bias and Fairness in Machine Learning Algorithms

Instructions:
Write a 1500-word research paper on the following topic:
“The Impact of Bias in Machine Learning Algorithms and How to Mitigate It.”

Your paper should include the following:

1. A brief introduction to machine learning and its applications.
2. An explanation of the different types of bias that can occur in machine learning models (e.g., data bias, algorithmic bias).
3. Examples of real-world cases where bias has impacted machine learning models (e.g., facial recognition, hiring algorithms).
4. Techniques and methods for mitigating bias in machine learning algorithms.
5. Ethical considerations regarding algorithmic fairness.

Assessment Criteria:

• Clarity and coherence of writing.
• Depth of research and understanding of ethical issues in AI.
• Application of concepts to real-world scenarios.
• Proper citations and referencing.

---

3. Project-Based Assessments

3.1. Data Science Capstone Project

Subject: Advanced Data Science Applications
Topic: Predictive Modeling for Business Problem

Project Brief:
You are provided with a business dataset (such as sales data, customer demographics, etc.) and tasked with creating a predictive model to forecast future sales for a retail company.

Instructions:

1. Data Exploration: Analyze the data and clean it as necessary (handling missing data, feature selection).
2. Modeling: Choose at least two machine learning models (e.g., Linear Regression, Random Forest, XGBoost) and evaluate them using appropriate metrics (e.g., RMSE, MAE).
3. Feature Engineering: Create new features that could improve the model’s performance.
4. Model Evaluation: Compare the performance of the different models and choose the best one based on evaluation metrics.
5. Final Report: Prepare a final report (maximum 3000 words) that includes the following:
   • Problem definition and data exploration.
   • Data preprocessing steps.
   • Model selection and evaluation.
   • Recommendations based on the model’s output.
   • A discussion of ethical considerations related to predictive modeling in business.

Submission Format:

• Python code and Jupyter notebooks.
• A detailed project report (PDF or Word format).
• Final presentation slides for the defense.

---

4. Peer and Self-Assessment

4.1. Peer Review

Subject: Data Science Group Project
Topic: Team Collaboration and Model Development

Instructions:
Evaluate your peers based on the following criteria. Use a 1-5 scale for each aspect (1 being poor, 5 being excellent).

• Collaboration: How well did your team members communicate and share responsibilities?
• Problem-Solving: How effectively did the team approach the data science problem?
• Creativity: How innovative were the solutions proposed for data analysis and modeling?
• Presentation: How well did the team present their findings, both technically and non-technically?
• Code Quality: How clean, well-documented, and efficient was the team’s code?

---

4.2. Self-Assessment

Subject: Data Science Individual Reflection
Topic: Self-Evaluation of Learning Progress

Instructions:
Reflect on your learning journey throughout the semester. Answer the following questions:

1. What do you believe are your key strengths in data science at this point in your learning journey?
2. Which areas do you feel you need to improve on and why?
3. How did you contribute to your group projects, and what skills did you develop through that collaboration?
4. What challenges did you face during this course, and how did you overcome them?

Submission Format:
A 500-word reflection document.

---

5. Rubrics and Grading Criteria

To ensure transparent grading, detailed rubrics are provided for each assessment. Below is a sample rubric for a Programming Assignment.

Programming Assignment Rubric

Criteria	Excellent (5)	Good (4)	Satisfactory (3)	Needs Improvement (2)	Unsatisfactory (1)
Code Quality	Well-organized, efficient, and fully functional code with proper comments	Clear code with minimal errors and good organization	Code works but with some inefficiencies and minor errors	Code is hard to follow with significant issues	Code is not functional
Completeness of Task	All tasks are completed with high attention to detail	All tasks completed but with some minor gaps	Most tasks completed but lacking some details	Several tasks incomplete or missing	Many tasks not completed
Data Processing	Excellent handling of missing data, outliers, and feature engineering	Adequate handling with minor issues	Basic data cleaning, but some issues remain	Inadequate data handling	No data cleaning or incorrect processing
Report and Explanation	Clear, concise, and comprehensive explanation with insights	Good explanation with some gaps in detail	Sufficient explanation but lacking clarity	Poor explanation with significant gaps	No explanation or unclear