• Overview
  • Objectives & Outcomes
  • Course Outlines
  • Degree Plan

Overview

 

The BSc in Statistics at Sultan Qaboos University is a comprehensive program designed to provide students with a solid foundation in statistical theory and its practical applications. The curriculum covers core subjects such as probability theory, statistical inference, regression analysis, and multivariate analysis techniques, while also emphasizing the use of modern statistical software like R and SPSS. Students gain hands-on experience in data analysis, statistical modeling, and research methods, culminating in a final-year project. The program prepares graduates for a wide range of career opportunities in fields such as finance, healthcare, business, and government, where they can apply statistical methods to solve real-world problems. The Department of Statistics also offers a supportive academic environment with access to state-of-the-art computational tools and research opportunities, through the Data Science Analytics Lab., ensuring that students are well-equipped for both professional careers and further academic study.

 

BSc in Statistics Major Requirements

  1. Required Courses: LANC2058 and STAT2101 plus Two Introductory Science Courses
  2. Minimum Required Grade Score of C in STAT2101
  3. Minimum CGPA of  2.00

 

Department Representative

 

Ronald Wesonga (Ph D)

Department of Statistics, Office Number: 0203

Email: wesonga@squ.edu.om

Programme Learning Outcomes (PLOs) - BSc Statistics

Upon successful completion of the Bachelor of Science in Statistics degree program, a student should be able to:

 

  1. Demonstrate understanding of the concepts and consequences of variation.

  2. Extract meaningful insights from data by applying appropriate statistical methods.

  3. Analyse the mathematical and theoretical basis for statistical inference and reasoning.

  4. Apply statistical methods commonly used in practice, selecting appropriate procedures for given problems.

  5. Assess the reasonableness of analytical results and recommend decisions in the face of uncertainty.

  6. Utilize modern statistical computing tools efficiently and effectively.

  7. Demonstrate the ability to work independently and collaboratively, exhibiting leadership when appropriate.

  8. Communicate statistical findings effectively in both technical and non-technical formats, including oral presentations.

  9. Develop competencies for career paths as statisticians or graduate studies.

 

The Program Learning Outcomes (PLOs) are aligned with Graduate  Attributes (GA)

 

Graduate Attributes (GA)

Program Learning Outcomes

A.Cognitive Capabilities.

a,b, d, e

B. Skill and Professional Capability

b, c, d, e, f, h

C.Effective Communication.

d, h

D.Autonomy and Leadership.

d, g, h, i

E. Responsibility and Commitment.

d, h, i

F. Development and Innovation.

d, h, i

 

The Program Learning Outcomes (PLOs) are aligned with the Oman Qaulaification framework (OQF) Characteristics

 

OQF Characteristics

Program Learning Outcomes

K. Knowledge

a, c,d

S. Skills

a,b, c, d, e, f ,g, h

C. Communication, Numeracy, Information Communication Technology Skills

b, d,  h, i

A. Autonomy and Responsibility

 c, g, i

E. Employability and Values

 a, b, c, e, f, g, h, i

L. Learning to Learn

f, g

 

Program  Learning  Outcomes  (PLOs) Integrated Alignment with  SQU  Graduate Attributes  (GA)  and  OQF Characteristics

 

No.

Program Learning Outcomes (PLOs)

Graduate Attributes (GA)

OQF Characteristics

a

Demonstrate understanding of the concepts and consequences of variation.

A, B

K, S, E

b

Extract meaningful insights from data by applying appropriate statistical methods.

A, B

S,C,E

c

Analyse the mathematical and theoretical basis for statistical inference and reasoning.

B

K,S,A,E

d

Apply statistical methods commonly used in practice, selecting appropriate procedures for given problems.

A,B,C,D,E,F

K,S,C

e

Assess the reasonableness of analytical results and recommend decisions in the face of uncertainty.

A, B

S,E

f

Utilize modern statistical computing tools efficiently and effectively.

B

S,E,L

g

Demonstrate the ability to work independently and collaboratively, exhibiting leadership when appropriate.

D

S,E,A,L

h

Communicate statistical findings effectively in both technical and non-technical formats, including oral presentations.

B,C,D,E,F

S,C,E

i

Develop competencies for career paths as statisticians or graduate studies.

D,E,F

C,A,E

 

 

 

Course Code     

           Course Name

STAT2100

                 Discovering Statistics using R

STAT2101

                 Introduction to Statistics

STAT2102

                 Introduction to Probability

STAT2103

                 Probability for Engineers

STAT3239

                 Statistical Inference I

STAT3334

                 Introduction to Inference

STAT3335

                 Introduction to Sampling

STAT3336

                 Computational Techniques in Statistics

STAT3338

                 Statistical Methods

STAT3339

                 Statistical Inference II

STAT4432

                 Regression Analysis

STAT4433

                 Design and Analysis of Experiments I

STAT4434

                 Nonparametric Statistics

STAT4436

                 Survey Design

STAT4438

                 Simulation and Modelling

STAT4441

                 Introdcution to Statistical Learning

STAT4442

                 Introduction to Bayesian Statistics

STAT4443

                 Advanced Programming with R

STAT4533

                 Quality Assurance and Reliability

STAT4535

                 Survival Analysis

STAT5521

                 Categorical Data Analysis

STAT5522

                 Demographic and Health Care Statistics

STAT5536

                 Time Series Analysis

STAT5537

                 Multivariate Techniques 

STAT5539

                 Data  Analysis

STAT5543

                 Data Mining Techniques 
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