The Associate Degree (AD) in Statistics is a two-year undergraduate program designed to equip students with foundational knowledge in statistics, mathematics, and information technology. The program emphasizes statistical analysis, data management, and applied research to prepare students for entry-level careers or further studies in statistics and related fields such as data science, healthcare analytics, and economics.

Program Structure

The Associate Degree program is structured to cover two years, consisting of four regular semesters and a total of 65 credit hours. Its primary objective is to introduce market-driven subjects that cater to the needs of local and regional communities and industries. As per HEC guidelines, the program should include 60 to 72 credit hours. The recommended course load per semester ranges from 15 to 18 credit hours. Upon completing the associate degree, graduates will have the opportunity to enroll in the fifth semester of a related undergraduate or equivalent degree program, with exemptions for courses already completed in the Associate Degree.

Significance of the Program

The Associate Degree in Statistics addresses the increasing demand for professionals proficient in data analysis and statistical methodologies. In an era dominated by big data, the ability to extract meaningful insights from vast datasets is critical across multiple industries, including healthcare, finance, government, and technology. This program provides a steppingstone for students aiming to work in data-centric roles or pursue higher education in statistics, data science, or related disciplines. The program also emphasizes entrepreneurial thinking, critical for students who aspire to apply their statistical expertise to innovate in business and research settings.

The major aims and objectives of the Associate Degree in Statistics are aligned with the Higher Education Commission (HEC) standard for Curriculum Guidelines for Undergraduate Education Policy. 

These main objectives are enlisted below:

1. To provide students with a strong foundation in statistical theory and applied methods   for data analysis.
2. To develop proficiency in using statistical software and programming languages for data management and analysis.
3. To prepare students for research roles or further education by building their problem-solving and analytical skills.
4. To foster an entrepreneurial mindset and encourage innovation in statistical research and data-driven decision-making.
5. To build a sense of social responsibility and ethical conduct in all aspects of data analysis and research.

The following criteria are applicable for admission in BS Statistics program:

1. Candidates who have passed 12 years of education with Mathematics/Statistics having minimum 45% marks (Intermediate / A -level / I. Com / DAE or equivalent relevant qualification).
2. Candidates who have passed 12 years of education with Mathematics/Statistics having less than 45% marks but have more than 50% marks in Mathematics/ Statistics (Intermediate / A -level / I. Com / DAE or equivalent relevant qualification).
3. Candidates who have passed 12 years of education with Mathematics/Statistics having less than 45% marks but and even less than 50% marks in Mathematics (Intermediate / A -level / I. Com / DAE or equivalent relevant qualification) shall be eligible subjected to the qualification of customized zero semester with CGPA (2.5).
4. Candidates who have passed 12 years of education without Mathematics/Statistics having minimum 45% marks (Intermediate / A -level / I. Com / DAE or equivalent relevant qualification) shall be eligible but they have to study an extra course of Statistics as deficiency offered by the department.

Assessments play a crucial role in the educational process, providing a comprehensive evaluation of student learning.  The program's assessments are aligned with the Open and Distance Learning (ODL) guidelines, ensuring a comprehensive and structured evaluation of student progress. The assessment framework covers various components of the study scheme, including theoretical courses, lab work or practicals. Each component follows specific ODL-approved methods to ensure fairness and consistency in measuring student learning and performance.

Below is the assessment mechanism for each component:

Assessment mechanism of Theoretical courses:

The students’ study progress evaluation mechanism is based on continuous assessment throughout the semester by giving assignments, quizzes, Graded Discussion Boards (GDBs), mid-term, and final term examinations. The following table shows further details of the assessment.  

This evaluation structure is designed to ensure that students develop strong theoretical knowledge. The blend of different activities provides flexibility and fairness in assessments.

Assessment Mechanism of Lab work / Practical(s):

The final evaluation for this course is designed to assess students' practical skills and understanding of statistical tools through a combination of online and hybrid hands-on exercises, as well as a final exam conducted at designated exam centers.

This evaluation structure is designed to ensure that students develop strong practical skills alongside theoretical knowledge. The blend of online and hybrid hands-on exercises provides flexibility, while the final exam and attendance requirements ensure academic rigor.

Criterion 1:     Total Semesters = To successfully complete the Associate Degree in Statistics, students must pass the 65 credit hour courses in a minimum of 4 regular semesters. The maximum time allowed is 8 semesters.

Criterion 2:     CGPA                          =         2.00/4.00

The practical course is designed to give students hands-on experience with statistical software, focusing on the practical application of techniques for data analysis, interpretation, and visualization. Students will be supervised through weekly tutorials, live sessions, synchronous or asynchronous guidance. The course is aligned with HEC’s inclusion of practical components in statistical education.

Structure of Lab work / Practical(s):

The course is structured into several phases, but the process and tasks may be adjusted based on the specific software or student needs.

Phase 1: Introduction to Software (Week 1-2)

• Overview of the statistical software and its key functionalities.
• Live or recorded tutorials, supplemented by additional resources.
• Assessment: Initial software usage.

Phase 2: Basic Data Analysis and Functions (Week 3-5)

• Practical use of basic functions such as data import, cleaning, and summary statistics.
• Guided assignments provided for independent practice, followed by submission for review.
• Assessment: Weekly tasks and exercises.

 Phase 3: Advanced Analysis Techniques (Week 6-9)

• Students explore advanced techniques like hypothesis testing, regression, and other statistical methods.
• Weekly progress updates and feedback from supervisors.
• Assessment: Weekly tasks and exercises.

Phase 4: Visualization and Reporting (Week 10-12)

• Application of data visualization methods and report writing.
• Detailed guidance provided on visualization techniques and how to interpret data.
• Assessment: Draft report and visualizations.

 Phase 5: Final Practical Exam (Week 13-15)

• The final exam will test software proficiency, data analysis, and visualization skills.
• Conducted at VU exam centers.
• Assessment: Final exam

Ongoing Support:

• Weekly live sessions, pre-recorded tutorials, and written guidelines will provide continuous guidance.
• Students can reach out to their supervisors via online tools like Google Meet, Skype, or Adobe Connect for additional support.
• Extra material such as tutorials, articles, and videos will be provided for enhanced learning.

 Assessment Mechanism of Lab work / Practical(s):

The final evaluation for this course is designed to assess students' practical skills and understanding of statistical tools through a combination of online and hybrid hands-on exercises, as well as a final exam conducted at designated exam centers.

Below is the proposed breakdown, though adjustments may be made based on the specific nature of the course.

Key Evaluation Components:

The breakdown of the activities is given below:

Hands-on Practical Exercises (Online) - 20%

• • Description: Students will complete regular online practical exercises using statistical software (e.g., R, SPSS, Python, etc.). These exercises will be designed to reinforce the theoretical concepts covered in the course and provide opportunities for students to apply these concepts to real-world data.
  • Mode: Asynchronous online submission through the learning management system (LMS).
  • Evaluation: Performance will be graded based on accuracy, analytical approach, and interpretation of results.

Hands-on Practical Exercises (Hybrid) - 15%

• • Description: A portion of the course will involve in-person or hybrid practical sessions, where students can receive real-time guidance and support from instructors. These sessions may include live demonstrations of software tools and data analysis techniques.
  • Mode: Blended mode, combining face-to-face sessions at the campus or online interactive sessions using platforms like Zoom or Google Meet.
  • Evaluation: The practical exercises completed during these sessions will be evaluated based on student engagement, the application of learned techniques, and the ability to work collaboratively in group settings (if applicable).

Practical Notebook - 10%

• • Description: Students will maintain a practical notebook to document their hands-on experiences throughout the course. This notebook should include detailed records of each practical exercise, outlining the steps taken, methods applied, and results obtained. It serves as a comprehensive log of their practical learning journey.
  • Mode: The notebook can be in hard copy, soft copy, or online digital format (such as Google Colab), allowing students the flexibility to choose the method that best suits their workflow.
  • Evaluation: The practical notebook will be assessed based on completeness, clarity of documentation, accuracy of recorded steps and results, and the overall organization of the content. Regular updates and reflections on their learning process will also be considered in the evaluation.

Final Term Exam (In Exam Center)

• • Description: The final term exam will be a comprehensive test of both theoretical knowledge and practical skills. It will assess students' overall understanding of the course material, including statistical methods, data analysis, and interpretation.
  • Mode: The exam will be conducted in exam centers to ensure a controlled and standardized testing environment.
  • Evaluation: The final exam will consist of both theoretical questions (short answers, problem-solving) and practical tasks that require students to analyze data and interpret results using statistical software.

Attendance

• • Students are required to attend both online and hybrid sessions.
  • Students are required to have a minimum of 70% attendance in practical sessions.
  • Failure to meet the attendance requirement may result in ineligibility to appear for the final term exam.

Attendance Structure:

• • 30% of practical sessions will be conducted on campus or in hybrid mode, offering direct interaction and real-time support.
  • 70% of practical sessions will be held online, providing flexibility and accessibility for all students.

 This evaluation structure is designed to ensure that students develop strong practical skills alongside theoretical knowledge. The blend of online and hybrid hands-on exercises provides flexibility, while the final exam and attendance requirements ensure academic rigor.

 Note/Disclaimer:

The University reserves the right to modify the structure and assessment criteria of the capstone project, internship and lab work/practical as needed. These adjustments will align with program requirements and/or supervisor recommendations and will be communicated through official channels to ensure academic standards are maintained.

Lab Work/Practical (Generic)

Use of Virtual Tools and Platforms

• • Software Access: Ensure that students have access to the required software (e.g., SPSS, EXCEL, R, Python, or any other relevant tools) via online platforms. Provide detailed instructions on how to download and install the software or access it through cloud-based platforms (such as Google Colab or Anaconda for Python).
  • Learning Management System (LMS): Utilize LMS to upload tutorials, lecture videos, and assignments related to practical tasks. Tools like Moodle, Canvas, or Google Classroom can also be used to manage submissions, grading, and feedback.
  • Online Labs/Simulations: For some courses, where applicable, make use of online simulations and virtual labs. Websites like WolframAlpha, GeoGebra, or DataCamp’s online tools can serve as alternatives for physical lab work.

Weekly or Bi-Weekly Practical Sessions

• • Recorded Tutorials: Upload pre-recorded tutorials or live demonstrations explaining how to perform specific tasks, such as do data cleaning, analysis using software or conducting simulations.
  • Live Sessions (Optional): Offer live Q&A sessions via platforms like Zoom, Google Meet, or MS Teams to address student concerns about the lab work. These should be scheduled weekly or bi-weekly.
  • Asynchronous Guidance: Provide written step-by-step guides and PDFs, supplemented by video explanations, allowing students to complete the tasks at their own pace.

Submission of Practical Work

• • Assignments/Tasks: Assign regular practical tasks (e.g., small coding/data problems in R, Python, matrix operations, or simulations in R) for students to complete after each session. These tasks should be relevant and achievable with the tools they have access to from home.
  • Documentation and Reporting: Students should be required to submit reports (e.g., PDFs or Word documents) detailing their process and findings for each lab session. Screenshots of code, simulations, or problem-solving steps can be included in these reports.
  • Timely Feedback: Assignments should be reviewed by the instructor, and feedback should be provided within a short timeframe (1-2 weeks) to allow students to make improvements.

Online Assessments and Quizzes

• • Weekly or Bi-Weekly Quizzes: Implement short quizzes to assess students’ understanding of the practical work. Quizzes could involve multiple-choice questions, small problem-solving tasks, or coding snippets.
  • Auto-Graded Assignments: Where possible, make use of auto-graded assignments (such as coding tasks on platforms like Jupyter or Python Notebooks) to provide instant feedback.

Peer Support and Collaboration

• • Online Discussion Forums: Encourage students to engage in online discussions about their lab work via forums or discussion boards within the LMS. Peer support is particularly useful in an online setting to foster collaborative learning.
  • Group Work (Optional): For larger projects or more advanced labs, students can be assigned into groups to complete tasks together, using shared online spaces like Google Drive, GitHub, or Microsoft Teams to collaborate.

Final Practical Exam at Exam Centers

• • Designated Exam Centers: Students will perform the final practical exam at their designated exam centers. Ensure that they are familiar with the format of the practical exam through mock tests or detailed exam guidelines.
  • Proctored Environment: The practical exam should be conducted under supervision at the exam center, testing their ability to use the software independently to solve mathematical problems or simulations.

Evaluation Method

• • Regular Assignments: 40%. These assignments are based on weekly or bi-weekly tasks completed during online lab sessions.
  • Final Practical Exam: 60%. This will be conducted at the designated exam center, focusing on problem-solving skills, proficiency in the software, and their ability to complete tasks independently.

Additional Considerations

• • Technical Support: Provide resources or hotlines for students facing technical difficulties with software installation or use.
  • Flexibility: Recognize the challenges of online learning, especially in remote areas. Allow for flexible deadlines and submission methods where internet access may be limited.