Grading & Assessment

Basic Data Science in Economics and Business

Grade Breakdown

Component Details

Attendance & Participation (10%)

Evaluation Criteria:

• Roll call attendance (4 points)

  • Present and on time: Full credit
  • 3+ unexcused absences: Reduced credit

• Homework completion (4 points)

  • Quality and timeliness of assignments
  • Effort and improvement demonstrated

• In-class engagement (2 points)

  • Active participation in discussions
  • Contributing to group activities
  • Asking relevant questions

Note: Students must achieve at least 5 points in this category to be eligible for the final exam.

Knowledge Check 1 (20%) - Week 8

Format: In-class assessment combining quiz, coding, and/or presentation

Topics Covered:

• Introduction to Data Science (Weeks 1)
• Python Programming (Weeks 2-3)
• NumPy and Pandas (Weeks 4-5)
• Data Input and Storage (Weeks 6-7)
• Data Preprocessing (Week 8 lecture)

Assessment Types:

• Written Quiz: Multiple choice and short answer questions on concepts
• Coding Exercise: Practical Python programming task (30-45 minutes)
• Presentation (if applicable): Brief presentation on data analysis results

Grading:

• Conceptual understanding: 40%
• Code correctness and efficiency: 40%
• Code documentation and style: 20%

Knowledge Check 2 (20%) - Week 15

Format: In-class assessment combining quiz, coding, and/or presentation

Topics Covered:

• Data Transformation and Feature Engineering (Weeks 10-11)
• Data Visualization (Weeks 12-13)
• Machine Learning Modeling (Weeks 14-15)

Assessment Types:

• Written Quiz: Conceptual questions on visualization and ML
• Coding Exercise: Build and evaluate a simple ML model
• Presentation (if applicable): Present findings from data analysis project

Grading:

• Conceptual understanding: 30%
• Code correctness: 40%
• Model evaluation and interpretation: 30%

Final Exam (50%)

Format: Computer-based multiple-choice exam

Duration: As per university examination schedule (typically 90-120 minutes)

Coverage: Comprehensive coverage of all course material from Weeks 1-15

Question Types:

• Conceptual questions on data science principles
• Python syntax and programming logic
• Data manipulation and transformation scenarios
• Visualization interpretation
• Machine learning concepts and evaluation metrics

Preparation:

• Review all lecture slides and textbook chapters
• Practice with weekly quizzes
• Review homework assignments and feedback
• Attend review session (announced closer to exam date)

Letter Grade Scale

Final course grades are assigned according to university standards:

Note: Exact grade boundaries may be adjusted based on course performance distribution and university policy.

Grading Philosophy

Homework & Assignments

• Emphasis on learning: Homework is designed to reinforce concepts and provide practice
• Iteration encouraged: You can revise and resubmit some assignments for improved scores (when specified)
• Late penalty: 1 point deduction per day late

Knowledge Checks

• Application-focused: Tests your ability to apply concepts, not just memorize
• Open-book/notes (when specified): Some assessments allow reference materials
• Time-limited: Designed to assess proficiency within realistic constraints

Final Exam

• Comprehensive assessment: Validates mastery of all learning outcomes
• Standardized format: Ensures fairness and consistency across all students
• Closed book: Tests retention and understanding without external aids

Academic Integrity

All assessments are subject to university academic integrity policies:

• Collaboration: Encouraged for learning, but all submitted work must be your own
• Code reuse: You may reference course materials and textbooks, but must cite sources
• Plagiarism: Copying code or text without attribution is prohibited
• Exam conduct: Individual work only; no communication with other students during exams

Violations will result in:

• Zero score on the assignment/exam
• Potential course failure
• Referral to university disciplinary committee

Grade Inquiries

If you have questions about a grade:

1. Review the rubric and feedback provided
2. Wait 24 hours before contacting the instructor (allow time for reflection)
3. Submit a written request explaining your specific concern
4. Meet with the instructor during office hours if needed
5. Deadline: Grade inquiries must be submitted within one week of receiving the grade

Tips for Success

✅ Attend all classes and participate actively

✅ Complete homework on time to reinforce learning

✅ Practice coding regularly - data science is a skill developed through repetition

✅ Use office hours when you need help or clarification

✅ Form study groups to discuss concepts and solve problems together

✅ Start assignments early to avoid last-minute stress

✅ Review feedback on graded work to understand mistakes and improve

← Back to Syllabus | View Policies → | Back to Home