PROGRAMS

 

Admission Criteria

BS Statistics with specialization in
1. Statistical Computing
2. Applied Statistics
3. Data Science
Eligibility: At least 45% marks in intermediate or equivalent.
Duration: 04 Year Program (08 Semesters)
Degree Requirements: 130 Credit Hours

 

BS Statistics with Specialization (5th Smester Induction) Eligibility:

  • BA/B.Sc. (Minimum of 14 years education) or its equilent with (Statistics or Mathematics of 200 marks must be studied) passed with minimum 45% marks or earned CGPA 2.00/4.00 from HEC recognized institute may apply for admission.
  • ADP (2 Years) with minimum 45% marks or earned CGPA 2.00/4.00 from HEC recognized institute may apply for admission. (Deficiency Courses of Statistics and Mathematics will be offered)

Duration: 02 Year Program (04 Semesters)

Scheme of Studies

BS Statistics (Morning + Evening Program)

  • BS(4-YEAR) Statistics 2024 AND ONWARD

Program Learning Objectives

  • Developing Statistical Expertise: Provide students with a strong foundation in statistical theory, methods, and techniques including probability theory, inferential statistics, regression analysis, and multivariate analysis.
  • Applied Data Analysis: Train students to apply statistical methods and computational tools to analyze real-world data sets from various domains such as economics, finance, healthcare, social sciences, and engineering.
  • Experimental Design and Analysis: Equip students with the knowledge and skills to design experiments, surveys, and observational studies and analyze the resulting data using appropriate statistical models and techniques.
  • Statistical Computing: Familiarize students with statistical software packages such as R, Python, SAS, or SPSS and teach them how to use these tools for data manipulation, visualization, analysis, and interpretation.
  • Problem-Solving and Decision-Making: Develop students’ ability to formulate and solve statistical problems, make data-driven decisions, and communicate their findings effectively to both technical and non-technical audiences.
  • Specialized Knowledge Areas: Offer courses and training in specialized areas of applied statistics such as time series analysis, survival analysis, Bayesian statistics, categorical data analysis, and spatial statistics based on industry demands and emerging trends.
  • Practical Experience: Provide opportunities for students to engage in hands-on projects, case studies, internships, and capstone experiences that allow them to apply statistical methods to solve real-world problems and gain practical skills.
  • Critical Thinking and Analysis: Foster critical thinking, analytical reasoning, and problem-solving skills essential for statistical analysis, model development, and interpretation of results in diverse settings.
  • Ethics and Professionalism: Instill ethical principles, integrity, and professionalism in statistical practice, emphasizing responsible conduct in data collection, analysis, reporting, and decision-making.
  • Preparation for Further Studies or Careers: Prepare students for advanced studies in statistics, data science, or related fields at the graduate level or for careers as statisticians, data analysts, research analysts, consultants, and decision-makers in various industries and organizations.
  • Continuous Learning and Adaptation: Encourage lifelong learning, continuous skill development, and adaptation to evolving technologies and methodologies in applied statistics and related disciplines.

Program Learning Outcomes

  • Statistical Knowledge and Theory: Demonstrate a comprehensive understanding of fundamental statistical concepts, theories, and methodologies. Apply probability theory to model and analyze random phenomena.
  • Data Analysis Skills: Collect, manage, and preprocess data for statistical analysis. Perform exploratory data analysis using graphical and numerical techniques. Apply appropriate statistical methods to analyze and interpret data from various domains.
  • Statistical Computing: Use statistical software packages (e.g., R, Python, SAS, SPSS) for data manipulation, visualization, and analysis. Develop and implement statistical algorithms and simulations to solve complex problems.
  • Experimental Design and Sampling: Design experiments and surveys, including the selection of sampling methods and sample sizes. Analyze data from designed experiments and observational studies using appropriate statistical techniques.
  • Regression and Multivariate Analysis: Conduct simple and multiple regression analyses to model relationships between variables. Apply multivariate statistical methods such as principal component analysis, factor analysis, and cluster analysis.
  • Specialized Statistical Methods: Utilize advanced statistical techniques such as time series analysis, survival analysis, Bayesian statistics, and categorical data analysis. Apply specialized methods to specific fields such as finance, healthcare, and social sciences.
  • Problem-Solving and Critical Thinking: Identify and formulate statistical problems based on real-world scenarios. Use critical thinking and analytical reasoning to select and apply appropriate statistical methods. Interpret results in the context of the problem and draw valid conclusions.
  • Communication Skills: Communicate statistical findings effectively through written reports, oral presentations, and visualizations. Explain complex statistical concepts to non-technical audiences in a clear and concise manner.
  • Preparation for Advanced Studies and Careers: Be prepared for advanced studies in statistics, data science, or related fields at the graduate level. Possess the skills and knowledge necessary for careers as statisticians, data analysts, research analysts, consultants, and other roles that require expertise in applied statistics.

Medium of Instruction: English

BS Degree Requirement

Sr. No Course Work No. of Courses Credit Hours
1. General Education Courses 12 30
2. Core and Major Discipline Courses 19 58
3. Major Specialization Courses 8 24
4. Interdisciplinary Courses 4 12
5. Internship 1 3
6. Capstone Project 1 3
Total No. of Courses of the Program 45
Total Credit Hours of the Program 130

Model Scheme of Studies for BS 4 year

1st Year

Semester Course Code Course Title Credit Hours
1st MDC STAT-114 Fundamentals of Statistics 3(3+0)
GC ENG-105 Functional English 3(3+0)
GC ISL-100 Islamic Studies/Ethics 2(2+0)
IDC MATH-133 Math-I (Calculus-1) 3(3+0)
GC SOC-114 Civics and Community Engagement 2(2+0)
GC MATH- Quantitative Reasoning-I 3(3+0)
Total 16
2nd GC ENG-103 Technical Writing and Presentation Skills 3(3+0)
GC PS-100 Pakistan Studies 2(2+0)
IDC MATH-231 Math-II (Calculus-II) 3(3+0)
MDC STAT-121 Descriptive and Inferential Statistics 3(3+0)
MDC STAT-122 Statistical Methods 3(3+0)
MDC STAT-111 Probability Theory 3(3+0)
Total 17

2nd Year

Semester Course Code Course Title Credit Hours
3rd GC ENG-104 Communication Skills 3(3+0)
IDC MATH-232 Math-III (Linear Algebra) 3(3+0)
MDC STAT-211 Probability Distributions 3(3+0)
MDC STAT-221 Mathematical Statistics-I 3(3+0)
MDC STAT-231 Regression Analysis-I 3(3+0)
IDC CS-105 Computer Fundamentals 3(2+1)
Total 18
4th IDC CS- Computer Programming 3(2+1)
IDC MATH-233 Math-IV (Differential Equations) 3(3+0)
MDC STAT-212 Sampling Techniques-I 3(3+0)
MDC STAT-222 Mathematical Statistics-II 3(3+0)
MDC STAT-232 Regression Analysis-II 3(3+0)
MDC STAT-241 Experimental Design-I 3(3+0)
Total 18

3rd Year

Semester Course Code Course Title Credit Hours
5th MDC STAT-311 Introduction to Econometrics 3(3+0)
MDC STAT-312 Applied Multivariate Analysis 3(3+0)
MDC STAT-313 Statistical Inference-I 3(3+0)
MDC STAT-314 Stochastic Processes 3(3+0)
MDC STAT-315 Time Series Analysis-I 3(3+0)
MDC STAT-316 Statistical Computing and Data Management 3(3+0)
Total 18
6th MDC STAT-321 Research Methodology and Report Writing 3(3+0)
MDC STAT-322 Bayesian Inference 3(3+0)
MDC STAT-323 Experimental Design-II 3(3+0)
MDC STAT-324 Statistical Inference-II 3(3+0)
MDC STAT-325 Survival Analysis 3(3+0)
MDC STAT-326 Statistical Quality Control 3(3+0)
Total 18

4th Year

Semester Course Code Course Title Credit Hours
7th MDC STAT-411 Advanced Topics in Statistics 3(3+0)
MDC STAT-412 Spatial Statistics 3(3+0)
MDC STAT-413 Non-Parametric Methods 3(3+0)
MDC STAT-414 Data Mining and Machine Learning 3(3+0)
MDC STAT-415 Capstone Project 3(0+3)
MDC STAT-416 Internship 3(0+3)
Total 18
8th MDC STAT-421 Advanced Bayesian Inference 3(3+0)
MDC STAT-422 Advanced Multivariate Analysis 3(3+0)
MDC STAT-423 Advanced Regression Analysis 3(3+0)
MDC STAT-424 Advanced Statistical Computing 3(3+0)
MDC STAT-425 Selected Topics in Statistics 3(3+0)
MDC STAT-426 Data Science and Analytics 3(3+0)
Total 18

Model Scheme of Studies for BS 5th Induction

Semester Course Code Course Title Credit Hours
1st YEAR
5th Major STAT-311 Probability Distributions-1 3 (3+0)
  Major STAT-313 Sampling Techniques-I 3 (3+0)
  Major STAT-315 Design and Analysis of Experiments-I 4 (3+1)
  Major STAT-317 Statistical Packages 3 (3+0)
  Major Specialized Course-I 3 (3+0)
  Elective Specialized Course-II 3 (3+0)
  Compulsory STAT-321 Field experience/internship: 3 (3+0)
6th   19
  Major STAT-312 Probability Distributions-II 3 (3+0)
  Major STAT-314 Sampling Techniques-II 3 (3+0)
  Major STAT-316 Design and Analysis of Experiments-II 4 (3+1)
  Major STAT-318 Econometrics-I 3 (3+0)
  Major STAT-320 Statistical programming with Python 3 (3+0)
  Specialized Course-III 3 (3+0)  
2nd Year
7th Major STAT-421 Statistical Inference-I 3 (3+0)
  Major STAT-423 Applied Multivariate Analysis 3 (3+0)
  Major STAT-427 Econometrics-II 3 (3+0)
  Specialized Course-IV 3 (3+0)
  Specialized Course-V 3 (3+0)
  Specialized Course-VI 3 (3+0)
8th   18
  Major STAT-422 Statistical Inference-II 3 (3+0)
  Major STAT-424 Categorical Data Analysis 3 (3+0)
  Compulsory STAT-426 Research Project 3 (3+0)
  GC ISL-401 Teachings of Holy Quran 0 (0+0)
  Specialized Course-VII 3 (3+0)
  Specialized Course-VIII 3 (3+0)
Note: 4 credit hours courses must include Lab./Practical.

List of Courses for Specialization in Applied Statistics

Course Title Credit Hours
Simulation Modelling 3(3+0)
Introduction to Stochastic Analysis 3(3+0)
Data Mining Techniques 3(3+0)
Industrial Statistics 3(3+0)
Actuarial Statistics 3(3+0)
Introduction to Spatial Statistics 3(3+0)
Introduction to Survival Analysis 3(3+0)
Operation Research 3(3+0)
Biostatistics 3(3+0)
Statistical Quality Control 3(3+0)
Bayesian Statistics 3(3+0)
SPSS for Survey Data Analysis 3(3+0)
SPSS for Health and Medical Research 3(3+0)

List of Courses for Specialization in Computational Statistics

Course Title Credit Hours
Data Visualization and Exploratory Data Analysis 3(3+0)
Databases Systems 3(3+0)
Data Mining Techniques 3(3+0)
Statistical Machine Learning 3(3+0)
Statistical Computing and Simulation 3(3+0)
Introduction to Spatial Statistics 3(3+0)
Cloud Computing 3(3+0)
Bayesian Computational Methods 3(3+0)
Time Series and Forecasting 3(3+0)
Statistical Computing in Healthcare 3(3+0)
Statistical Computing 3(3+0)
Mathematical Models and Simulation 3(3+0)
Data Analysis with SAS 3(3+0)

List of Courses for Specialization in Data Science

Course Title Credit Hours
Artificial Intelligence 3(3+0)
Database Management Systems 3(3+0)
Introduction to Data Science 3(3+0)
Tools and Techniques of Data Science 3(3+0)
Machine Learning 3(3+0)
Expert System 3(3+0)
Decision Support System 3(3+0)
Algorithm and Neural Network 3(3+0)
Deep Learning 3(3+0)
Practice in Statistical Data Science 3(3+0)
Analysis of (Algorithms for) Big Data 3(3+0)
Data Mining 3(3+0)

List of General Courses for Statistics (BS-4 Years)

 

  1. Human Resource Management
  2. Environmental Sciences
  3. Principles of Management & Marketing
  4. Basic Financial Management
  5. History of Human Civilization
  6. Foreign Language other than English
  7. Introduction to Physics
  8. Advanced Calculus
  9. Introduction to Genetics
  10. Introduction to Geography
  11. Mathematical Economics I
  12. Mathematical Economics II
  13. Decision Models

List of Elective Courses for Statistics (BS-4 Years)

 

    1. Operations Research
    2. Stochastic Process
    3. Reliability Analysis
    4. Decision Theory
    5. Bio-Statistics
    6. Data Mining
    7. Actuarial Statistics-I
    8. Actuarial Statistics-II
    9. Categorical Data Analysis
    10. Bayesian Inference
    11. Statistical Quality Control
    12. Spatial Data Analysis
    13. Research Methodology

or any other subject depending upon the expertise available

Important note: The Scheme of Studies of BS course can be changed according to the demand of market.

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