The Bachelor of Science in Statistics (BS Statistics) is a comprehensive 4-year undergraduate degree designed to meet the growing demand for statistical expertise in various sectors. This program emphasizes both theoretical and applied statistics, preparing students for careers as professional statisticians. Students will gain a deep understanding of data analysis, statistical modeling, and the use of modern programming tools, empowering them to apply statistical methods to solve real-world problems.

Offered in a blended mode, this program is accessible to students across Pakistan and abroad, allowing them to pursue their education regardless of their location. This flexibility makes it ideal for individuals looking to balance their studies with other commitments. Graduates of this program can pursue careers in academia, the freelance market, the job sector, or further research in advanced statistical fields.

The Department of Statistics, with its 20 years of experience in online education, is confident in delivering a quality learning experience. Building on Virtual University’s success in launching online BS programs, this degree provides access to video lectures by esteemed academicians, offering students a robust and flexible educational experience that will prepare them for the academic, industrial, or research fields.

Structure of the Program

The BS Statistics program is structured in line with the Higher Education Commission (HEC) guidelines, comprising a total of 128 credit hours, spread across 8 semesters over four years. It offers a balanced mix of general education, interdisciplinary, and major courses to equip students with both foundational knowledge and advanced statistical skills. The program includes 32 credit hours of general education courses, as mandated by HEC, covering a range of topics essential for well-rounded intellectual development. Additionally, there are 12 credit hours from interdisciplinary courses that offer insights from fields like computer science and mathematics, further enhancing students' analytical capabilities.

.At the core of the program are the major courses, accounting for at least 72 credit hours, with a strong emphasis on statistical theory, methods, and applications. By the time students graduate, they will have completed 96 credit hours related to major statistics courses, ensuring a deep and thorough understanding of the subject. To prepare students for real-world challenges, the program includes an internship of 3 credit hours, providing hands-on experience in a professional setting. In addition, students undertake a capstone project worth 3 credit hours, where they apply their knowledge to solve a real-life statistical problem.

In the final semester, students can choose from one of three specialized streams Data Analytics, Biostatistics, or General Statistics depending on their interests and career aspirations. This flexibility allows students to tailor their learning to emerging trends and demands in the statistical field.

The major aims and objectives of the BS Statistics Program are adapted from the Higher Education Commission (HEC) standard for Curriculum Guidelines for Undergraduate Programs in Statistical Science.

These are enlisted below:         

1. To provide a sound footing of the subject matter of statistical theory with applications, so that the students can pursue higher degrees and research in the field of statistics.
2. To train the students in the use of statistical software and techniques of data collection and analysis so that they can avail opportunities in the job market.
3. To involve the students in the research projects so that they can be better trained in the field of research.
4. To prepare students to explore and enhance their knowledge about various interdisciplinary fields of life.

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.

5. Applicants with a 2-year Associate Degree in the same discipline (from an HEC-recognized institution) will be able to join the 5th semester of a BS program. Those with an Associate Degree in a different discipline or a conventional BA/BSc degree (annual system) must complete a bridging semester/deficiency semester to address academic deficiencies. The minimum admission eligibility is a 2.00/4.00 CGPA for Associate Degree holders (semester system) or 45% marks for BA/BSc or equivalent (annual system), along with meeting the University’s general admission criteria based on 12 years of education. The University may set higher standards for specific programs. (NA for Fall 2025)

The Open and Distance Learning (ODL) policy framework provides guidelines for assessment and credit hours to ensure structured and consistent evaluation of student progress. This program follows these ODL guidelines, which define the assessment methods, and the allocation of credit hours based on the learning mode.

Assessment and Credit Hours Structure:

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 practical, a capstone project, and an internship. 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.

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.

Attendance: Students are required to have a minimum of 70% attendance in practical sessions.

Assessment Mechanism of Capstone Project

The evaluation of the capstone project is designed to assess students' ability to apply their statistical knowledge to a real-world problem. The following breakdown outlines the key components of the project evaluation, which may be adjusted based on the specific nature of the project.

Note: It mandatory for the students to pass the final presentation; viva of their project.

Assessment Mechanism of Internship:

The internship evaluation structure is designed to assess the student's practical experience and application of statistical knowledge in a real-world work environment. This assessment ensures that students demonstrate the ability to adapt theoretical learning to professional settings. The breakdown of the evaluation includes key phases of the internship, from the selection of an organization to the final presentation and viva.

Note: It mandatory for the students to pass the final presentation; viva of their project.

To become eligible for the award of BS Statistics degree, the student is required to complete

            ·  128 Credit Hours with Single Major

·  Must have earned CGPA (Cumulative Grade Point Average) of at least 2.0 on a scale of 4.0.

However, as per HEC guidelines, students enrolled in BS Program can convert their respective BS program to Associate Degree (AD) after the completion of four semesters (2 years) of their BS program. In such scenario, students are required to fulfill the following requirements:

·  Student has studied/passed all the courses to fulfill the respective AD requirement.

All the study changes rules defined by the university shall be applied

The final semester project in the BS Statistics program is a 3-credit hour course where students will apply their statistical knowledge to a real-world problem. The project will include key phases such as problem identification, data collection, data cleaning, hypothesis formation, analysis, visualization, and presentation of results. Students may work with either primary or secondary data and will receive supervision at each stage to ensure proper guidance.

Structure of Capstone Project

The project can be broadly divided into several key phases, which provide structure and guidance. However, these phases may be modified as needed, depending on the specific requirements of the project.

Phase 1: Project Proposal (Week 1-3)

• Students identify a research problem and submit a detailed proposal, including the objective, scope, and methodology.
• Supervisors provide feedback on the proposal.
• Assessment: Proposal submission

Phase 2: Data Collection and Cleaning (Week 4-6)

• Students collect relevant data (primary or secondary) and perform data cleaning and preparation.
• Progress reports are submitted to supervisors.
• Assessment: Progress report and quality of data

Phase 3: Data Analysis and Hypothesis Testing (Week 7-10)

• Students develop hypotheses and perform statistical analysis using appropriate tools and techniques.
• Supervisors review analysis methods and provide feedback.
• Assessment:Assignment Evaluation

Phase 4: Result Discussion and Report Writing (Week 11-13)

• Students create visualizations and write a comprehensive report of their findings.
• Supervisors guide report structure and design.
• Assessment: Draft report submission

Phase 5: Final Presentation and Viva (Week 14-15)

• Students present their project findings in a final presentation and participate in a viva to demonstrate understanding and defend their work.
• Assessment: Presentation

Group Work:

• Projects can be completed in groups of 2-3 students. Group dynamics will be monitored, and individual contributions will be assessed during the viva and through progress reports.

Assessment Mechanism of Capstone Project

The evaluation of the capstone project is designed to assess students' ability to apply their statistical knowledge to a real-world problem. The following breakdown outlines the key components of the project evaluation, which may be adjusted based on the specific nature of the project.

Key Evaluation Components:

The breakdown of the activities is given below:

Proposal Submission and Acceptance

• • Students are required to submit a well-structured project proposal outlining the research problem, objectives, methodology, and the data they plan to use.
  • This phase ensures that students have a clear understanding of their project goals and are prepared to move forward with data collection and analysis.
  • Evaluation will focus on the clarity, feasibility, and relevance of the proposal to the field of statistics.

Data Collection and Cleaning

• • Students will either collect primary data or source secondary data relevant to their research problem.
  • Data cleaning, preparation, and organization are critical steps before analysis can be conducted.
  • Evaluation will consider the accuracy and thoroughness of the data collection process, as well as the methods used to clean and organize the data.

Analysis and Hypothesis Testing

• • Students are required to conduct statistical analyses based on their hypotheses, using appropriate statistical tools and techniques.
  • This phase demonstrates the student’s technical competence in applying statistical methods to their collected data.
  • Evaluation will focus on the robustness of the analysis, appropriateness of the methods chosen, and the validity of the conclusions drawn from hypothesis testing.

Results Discussion and Draft Report Submission

• • Students will write a detailed draft report discussing their findings, interpretations, and any limitations of their analysis.
  • This phase emphasizes students' ability to present their results coherently and critically evaluate the outcomes of their work.
  • Evaluation will consider the quality of the report, the depth of the discussion, and the ability to link the findings back to the original research question.

Final Presentation and Viva

• • The final stage involves a comprehensive presentation where students showcase their entire project, including the problem, methodology, analysis, and findings.
  • Following the presentation, students will defend their work in a viva, where they will answer questions and demonstrate their understanding of the project.
  • Evaluation will focus on presentation skills, clarity of communication, depth of knowledge, and the ability to defend the methodology and results.

This structured plan will ensure that students develop the necessary skills for independent statistical research and analysis, while supervisors maintain a supportive role throughout the project.

Internship in BS Statistics

The internship in the BS Statistics program is a 3-credit hour course designed to provide students with firsthand experience in applying their statistical knowledge in a real-world job market setting. The internship allows students to engage in various industries where statistics is essential, helping them gain practical insights and exposure.

Structure of Internship

The internship involves several phases, providing a structured path for students to follow. However, adjustments may be made based on the nature of the internship and the selected industry.

Phase 1: Selection of Institution (Week 1-2)

• Students will choose an industry or institution where they will complete their internship, with guidance from their supervisors.
• Supervisors will approve the selected institution based on its relevance to the field of statistics.
• Assessment: Institution selection and approval.

Phase 2: Internship Engagement (Week 3-12)

• Students will join the selected institution and spend time working on real-world tasks, applying their statistical knowledge in practical scenarios.
• Weekly progress reports will be submitted to supervisors, detailing their tasks, learning experiences, and challenges faced.
• Supervisors will offer guidance and support throughout this period, ensuring students are on track.
• Assessment: Weekly reports and supervisor feedback

Phase 3: Final Report Submission (Week 13-14)

• At the end of the internship, students will submit a comprehensive report detailing their work, learning outcomes, and the application of statistical methods during the internship.
• Supervisors will review the report and provide feedback.
• Assessment: Final report submission

Phase 4: Presentation and Viva (Week 15)

• Students will present their internship experiences, including key takeaways, challenges, and how statistical techniques were applied in the field.
• They will participate in a viva to demonstrate their understanding of the work done and defend their learning.
• Assessment: Presentation and Viva

Group Work:

Students may undertake internships individually or in groups of 2-3, depending on the nature of the internship. Group dynamics and individual contributions will be assessed during the viva and through progress reports.

Evaluation Mechanism of Internship:

The internship evaluation structure is designed to assess the student's practical experience and application of statistical knowledge in a real-world work environment. This assessment ensures that students demonstrate the ability to adapt theoretical learning to professional settings. The breakdown of the evaluation includes key phases of the internship, from the selection of an organization to the final presentation and viva.

Note: It mandatory for the students to pass the final presentation; viva of their project.

Key Evaluation Components:

The breakdown of the activities is given below:

Institution Selection

• • Assesses the student's ability to identify and select a relevant organization for their internship, ensuring that the chosen institution aligns with the program's objectives and the student's professional goals.
  • Students must submit a detailed justification of why they selected the institution, including its relevance to the field of statistics.

Weekly Progress Reports

• • Students are required to submit weekly reports documenting their tasks, learning outcomes, and challenges encountered during the internship.
  • This component evaluates consistency, engagement with the assigned tasks, and the student's ability to reflect on their practical experiences.
  • Supervisors may provide feedback to guide the student’s progress throughout the internship.

Mid Report

• • The mid-report serves as a checkpoint to assess the student's progress, challenges faced, and learning outcomes at the halfway point of the internship.
  • It should include a summary of tasks completed, skills developed, and any contributions made to the organization. Additionally, it may cover challenges encountered and how they were resolved, highlighting problem-solving skills and adaptability.

• • Supervisors may provide feedback to help guide the student’s progress during the remainder of the internship.

Final Report Submission

• • The final report should provide a comprehensive summary of the student’s internship experience, including an analysis of the work undertaken, skills developed, and how academic knowledge was applied in the professional environment.
  • The report must be structured and follow a clear format, demonstrating the student's ability to communicate their findings effectively.

Final Presentation and Viva

• • The final presentation will allow students to present their internship experience, highlighting key learning, challenges, and the impact of their work on the organization.
  • A viva will follow the presentation, where students will be questioned about their practical experiences, methodologies, and insights gained during the internship.
  • This component assesses communication skills, depth of understanding, and the ability to defend their work confidently.

This assessment structure ensures that students are evaluated not only on their academic learning but also on their professional development and adaptability in a real-world job environment.

1.  Lab Work / Practical(s)

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) (Statistics Course)

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)

• • 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)

• • 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

• • 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 (General Course)

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.

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.

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.

Assessment Mechanism:

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.

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.
  • 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.