PDF Bachelor of Science (B.Sc.) in Statistics - Kau

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Bachelor of Science (B.Sc.) in Statistics

Department of Statistics, Faculty of Science, King Abdulaziz University

FAROUQ MOHAMMAD ALAM September 2020 Quality & Accreditation Unit

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A. Program Information

About Statistics

As a field of science, Statistics plays an important role in explaining and interpreting data and information that are generated from daily life phenomena. Due to the increasing demand on statistical methods and their applications in different fields of science and engineering, the Department of Statistics had been established during the 1980s. Since its establishment, the department carries out its duties and seeks to achieve its objectives, including, but are not limited to, providing academic programs with high learning standards and scientific consultations in various fields of research.

About the Program

The demand for professional statisticians with scientific background has increased due to the grown demand for statistical analysis, statistical applications, and scientific research inside and outside of the academic population.

The Department of Statistics at the Faculty of Science offers a four-year academic program that provides a mixture of theory, application, and practice. It incorporates the scientific foundations of statistics with advanced applications which demand advanced knowledge of statistical thinking and both programming and problem-solving techniques.

Program’s Vision

Local pioneering in teaching statistics, its theoretical and applied branches, at level that suites undergraduate students.

Program’s Mission

Contribute to the university’s strategy by graduating statistical competencies that serve the community and be the first choice in the labor market.

Program Goals

Program Goals (PGs) or Program Educational Objectives (PEOs) are “broad statements that describe what graduates are expected to attain within a few years of graduation. Program educational objectives are based on the needs of the program’s constituencies”. Our PGs cover three classic objectives: professional practice expectations (PG #1), societal context, i.e., how

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graduates fit or fulfill their expected role in society (PG #2), and the academic/professional growth (PG #3). The PGs are stated as follows:

• PG #1 (Professionalism): Be capable/efficient/proficient in utilizing statistical knowledge and related applications based on an understanding of theoretical and applied statistical techniques to problem solving, using creative thinking and contemporary software.

• PG #2 (Leadership): Be significant leaders who contribute effectively to the economic growth and sustainable development of Saudi society from a statistical point of view.

• PG #3 (Life Experience & Research): Be engaged in professional development and/or graduate studies to keep pace with the evolution of career paths resulting from the rapid future technological changes.

Graduate Attributes

The Department of Statistics qualifies the student after his/her graduation to be:

GA # Statement

GA (1) A creative statistician with discipline-based knowledge and critical thinking.

GA (2) A competent statistician with analytical, computational, and discipline-based skills.

GA (3) A capable statistician who supports decision-makers in decision-making and organizational development.

GA (4) A passionate individual who seeks professional development and/or graduate studies.

GA (5) A qualified bilingual individual who effectively writes statistical reports and communicates information.

GA (6) An adaptive individual who upholds social responsibility, adapt with rapid developments.

GA (7) A proactive individual who can effectively function autonomously or in teams.

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Program Learning Outcomes (Students Outcomes)

Knowledge

K1 An ability to identify, formulate, and solve broadly defined technical or scientific problems by applying knowledge of mathematics and science and/or technical topics to areas relevant to the discipline.

Skills

S1 An ability to formulate or design a system, process, procedure, or program to meet desired needs.

S2 An ability to develop and conduct experiments or test hypotheses, analyze, and interpret data and use scientific judgment to draw conclusions.

Competences

C1 An ability to communicate effectively with a range of audiences.

C2 An ability to understand ethical and professional responsibilities and the impact of technical and/or scientific solutions in global, economic, environmental, and societal contexts.

C3 An ability to function effectively on teams that establish goals, plan tasks, meet deadlines, and analyze risk and uncertainty.

Professional Occupations/Jobs

• A graduate of the B.Sc. of Statistics program can work in any institution or corporation which require data analysis specialist. The following professional occupations/jobs are found in the online Job Classification Guide System of the Ministry of Human Resources and Social Development:

• Measurement and evaluation researcher/specialist.

• Administrative employee of divisions, departments, and administrations of statistics in governmental agencies.

• Statistical researcher/specialist.

• Statistical classifier/coder/programmer.

• Data mining/collecting specialist.

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B. Program Requirements

Overview

To obtain a B.Sc. in Statistics degree, all students must satisfy the following requirements:

1. Score minimum Grade Point Average (GPA) 2.0 out of 5.0.

2. Complete 128 credits as shown in the following table:

Courses Credits

Preparatory Year 27

University General Requirements 14

Faculty Requirements 9

Core Courses 57

Electives STAT Courses 15

Free Electives¹ 6

Total 128

Preparatory Year Courses

Course Name Code & No. Credits Prerequisite(s)

Calculus 1 MATH 110 3 -

General Physics PHYS 110 3 -

Computer Skills CPIT 100 3 -

English Language 1 ELI 101 0 -

General Statistics STAT 110 3 -

General Biology 1 BIO 110 3 -

General Chemistry 1 CHEM 110 3 -

Communication Skills COMM 101 3 -

University General Requirements

Islamic Culture 1 ISLS 101 2 -

Islamic Culture 2 ISLS 201 2 ISLS 101

Arabic Language 1 ARAB 101 3 -

Arabic Language 2 ARAB 201 3 ARAB 101

1 Free Electives are university-wide courses which students can choose from outside the department.

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Faculty Requirements

Calculus 2 MATH 202 3 MATH 110

Physics Lab PHYS 281 1 PHYS 110

Lab Safety PHYS 200 1 PHYS 110

General Laboratory Safety CHEM 200 1 CHEM 110

Chemistry Lab CHEM 281 1 CHEM 110

Field Training STAT 390 2 Dept. Approval

STAT Courses Coding

The Department of Statistics offers courses with the code STAT followed by three digits. The left-most digit indicates the level of the course while the right-most digit indicates the sequence of the course within the topic area. On the other hand, the middle digit indicates the topic area as shown in the following table:

Number Topic

0 Statistical Methods

1 Probability Theory

2 Statistical Theory

3 Applied Statistics

4 Statistical Programs

5 Statistical Topics

6 Operation Research

STAT Core Courses

Course Name Code & No. Credits Prerequisite(s)

Statistics in Practice STAT 203 2 STAT 110

Statistical Mathematics STAT 205 3 STAT 110

Probability Theory 1 STAT 211 4 STAT 205, MATH 202

Statistical Lab 1 STAT 241 2 STAT 203

Operations Research 1 STAT 261 3 MATH 241

Statistical Methods STAT 302 3 STAT 203, STAT 211

Probability Theory 2 STAT 312 4 STAT 211, MATH 203

Statistical Inference 1 STAT 321 3 STAT 312

Sampling Theory STAT 351 3 STAT 321

Regression analysis STAT 403 3 STAT 302, MATH 241

Design & Analysis of Experiments STAT 405 3 STAT 403

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Course Name Code & No. Credits Prerequisite(s)

Statistical Inference 2 STAT 422 3 STAT 321

Programming and Simulation STAT 442 3 STAT 403, STAT 405

Non-parametric Statistics STAT 453 3 STAT 302

Research Project STAT 491 3 Dept. Approval

Programming 1 CPCS 202 3 CPIT 100

Intro. to Database Management CPIS 241 3 CPCS 202

Calculus 3 MATH 203 3 MATH 202

Linear Algebra MATH 241 3 MATH 203

STAT Electives STAT Courses

Statistical Quality Control STAT 333 3 STAT 302

Demography STAT 334 3 STAT 302

Biostatistics STAT 335 3 STAT 302

Reliability Theory STAT 336 3 STAT 302

Statistical Finance STAT 337 3 STAT 302

Operations Research 2 STAT 362 3 STAT 261

Categorical Data Analysis STAT 406 3 STAT 302

Non-Linear Regression STAT 407 3 STAT 403

Stochastic Process STAT 413 3 STAT 312, MATH 241

Decision Theory STAT 424 3 STAT 422

Order Statistics STAT 425 3 STAT 422

Bayesian Statistics STAT 427 3 STAT 422

Medical Statistics STAT 436 3 STAT 335

Actuarial Statistical Models STAT 438 3 STAT 337

Data Mining STAT 450 3 STAT 403

Econometrics STAT 451 3 STAT 403

Time Series Analysis STAT 454 3 STAT 403

Applied Multivariate Analysis STAT 455 3 STAT 405

Spatial Statistics STAT 456 3 STAT 403

Queuing Theory STAT 463 3 STAT 362

Special Topics STAT 480 3 STAT 302

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C. Program Curriculum

Course

Required (R) Elective (E)

Curricular Area (Credit Hours)

Math Statistics Topics General

(Department, Number, Title) Selective E (SE) & Sciences (F or A)² Education Other 1st Year, 1st Semester (Level 1)³

MATH 110 Calculus 1 R 3

PHYS 110 General Physics R 3

CPIT 100 Computer Skills R 3

ELI 101 English Language 1 R 0

1st Year, 2nd Semester (Level 2)

STAT 110 General Statistics R 3 (F)

BIO 110 General Biology 1 R 3

CHEM 110 General Chemistry 1 R 3

COMM 101 Communication Skills R 3

2nd Year, 1st Semester (Level 3)

CHEM 281 Chemistry Lab R 1

STAT 203 Applied Statistics R 2 (F)

CPCS 202 Programming 1 R 3

MATH 202 Calculus 2 R 3

STAT 205 Mathematical Statistics R 3 (F)

ARAB 101 Arabic 1 R 3

CHEM 200 General Laboratory Safety

R 1

2nd Year, 2nd Semester (Level 4)

STAT 211 Probability Theory 1 R 4 (F)

ISLS 101 Islamic Culture 1 R 2

PHYS 200 Lab Safety R 1

MATH 203 Vector Analysis R 3

STAT 261 Operation Research 1 R 3 (F)

MATH 241 Linear Algebra 1 R 3

2 F: Fundamental, A: Advanced

3 The preparatory year has two plans; Plan A and Plan B. This curriculum assumes Plan A. Students of Plan B study CHEM 110, BIO 110, and STAT 110 in the first semester, and then study MATH 110 and PHYS 110 in the second semester.

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Course

Required (R) Elective (E)

Curricular Area (Credit Hours)

Math Statistics Topics General

(Department, Number, Title) Selective E (SE) & Sciences (F or A)^* Education Other 3rd Year, 1st Semester (Level 5)

STAT 312 Probability Theory 2 R 4 (A)

STAT 302 Statistical Methods R 3 (F)

STAT 241 Statistical Packages and Research Methods

R 2 (F)

ARAB 201 Arabic 2 R 3

PHYS 281 Physics Lab R 1

CPIS 241 Database Management System

R 3

3rd Year, 2nd Semester (Level 6)

STAT 403 Regression Analysis R 3 (A)

STAT 321 Statistical Theory 1 R 3 (A)

STAT 453 Nonparametric Statistics R 3 (A)

Free Elective E 3

STAT XXX Elective SE 3

4th Year, 1st Semester (Level 7)

STAT 422 Statistical Theory 2 R 3 (A)

STAT 405 Design of Experiments R 3 (A)

Free Elective E 3

STAT 390 Field Training R 2

4th Year, 2nd Semester (Level 8)

STAT 351 Sampling Theory R 3 (A)

STAT 442 Programming and Simulation

R 3 (A)

STAT 491 Research Project R 3 (A)

OVERALL TOTAL CREDIT HOURS FOR THE DEGREE

128 31 63 26 8

PERCENT OF TOTAL 100% 24.2% 49.2% 20.3% 6.3%

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D. Catalog of STAT Courses

Catalog of STAT Core Courses

STAT 110 General Statistics (Theory: 3, Practice: 0, Credit: 3)

This is an introductory course in statistics which is designed to teach scientific track preparatory year students about the basic concepts of statistics, data analysis and probability theory. After completing this course, students are able to conduct basic data analysis using fundamental descriptive and inferential statistical methods and solve basic problems in probability.

STAT 203 Statistics in Practice (Theory: 1, Practice: 2, Credit: 2)

This is an introductory course in statistics designed to teach students who newly joined the department about additional concepts of statistics, data analysis and probability theory. After completing this course, students are able to conduct intermediate data analysis using important descriptive and inferential statistical methods and solve some intermediate problems in probability.

STAT 205 Statistical Mathematics (Theory: 2, Practice: 2, Credit: 3)

This course refreshes the mathematical knowledge and enhances the mathematical background of students who newly joined the department. After completing this course, students can deal with fundamental concepts of both linear algebra and calculus, and their applications in statistics and probability.

STAT 211 Probability Theory (Theory: 3, Practice: 2, Credit: 4)

This course explains the fundamentals of probability theory and univariate distribution theory.

After completing this course, students can deal with probability and probability distributions and their characteristics in the case of a single random variable.

STAT 241 Statistical Packages and Research Methods (Theory: 0, Practice: 4, Credit:2) This course covers fundamental elements of designing, evaluating, and conducting a survey study using questionnaires, implementing an appropriate statistical package to clean and analyze the data, and training students how to write and present a statistical report. After completing this course, students can design and conduct a survey study using questionnaires and write and present a statistical report.

STAT 261 Operation Research 1 (Theory: 2, Practice: 2, Credit: 3)

This is an introductory course designed to teach students about the foundations of operation research. After completing this course, students can use optimization models to formulate real- world problems in different areas and then solve them using operations research computer packages and analyze the outputs.

STAT 302 Statistical Methods (Theory: 2, Practice: 2, Credit: 3)

This course explores key inferential methods and their applications. After completing this course, students can analyze data using parametric and nonparametric inferential methods, including, but not limited to, estimation, hypothesis testing, correlation, and goodness-of-fit methods.

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STAT 312 Probability Theory 2 (Theory: 3, Practice: 2, Credit: 4)

This course explains the fundamentals of multivariate distribution theory (with more concentration on bivariate distributions), related distributional properties, and functions of random variables. After completing this course, students can solve problems related to multivariate probability distributions (especially bivariate distributions) and functions of random variables.

STAT 321 Statistical Inference 1 (Theory: 2, Practice: 2, Credit: 3)

This course explains the theoretical background of estimation methods used. After completing this course, students can perform estimation using different inferential methods.

STAT 351 Sampling Techniques (Theory: 2, Practice: 2, Credit: 3)

This course introduces the sampling techniques to choose elements of the different types of random samples through studying the population. After completing this course, students can perform sampling techniques, verify their theoretical aspects, and know how to implement them.

STAT 403 Regression Analysis (Theory: 1, Practice: 4, Credit: 3)

This course introduces the students to the theory of linear model by exploring topics related to simple and multiple linear regression analysis and corresponding diagnostic (remedial) procedures. After completing this course, students can perform simple and multiple linear regression and diagnose the estimated models using remedial procedures.

STAT 405 Design of Experiments (Theory: 2, Practice: 2, Credit: 3)

This course introduces the students to the statistical principles involved in the planning of experiments and subsequent analysis of data. After completing this course, students can plan, design, and analyze different types of experiments.

STAT 422 Statistical Inference 2 (Theory: 2, Practice: 2, Credit: 3)

This course explains the theoretical background of inferential methods used to test hypotheses.

After completing this course, students can perform hypotheses testing using different inferential methods.

STAT 442 Programming and Simulation (Theory: 2, Practice: 2, Credit: 3)

This course covers key concepts of statistical programming, modeling, and simulation. After completing this course, students can efficiently use simulation techniques. Moreover, they will have a practical understanding for most of the statistical theorems and methods that they previously studied.

STAT 453 Nonparametric Methods (Theory: 2, Practice: 2, Credit: 3)

This course explores key nonparametric methods and their applications. After completing this course, students can analyze data using several nonparametric methods.

This course train students on research projects which are established on real-life problems.

The students are required to collect or simulate data related to the considered research problem(s). They summarized and analyzed the obtained information and report their findings in the form of a thesis that will be discussed with the students in a seminar. After completing this course, students can perform statistical studies, write reports, and demonstrate their findings via presentations.

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Catalog of Third-Year STAT Elective Courses

STAT 333 Statistical Quality Control (Theory: 2, Practice: 2, Credit: 3)

This course aims to introduce the students to quality, quality control, statistical quality control, quality-related concepts, and to recognize and apply relevant statistical concepts in quality measurements such as constructing control charts and developing sampling plans. After completing this course, students should have a firm understanding of the theory and practice of statistical quality control, the basis and principles of statistical process control and technique, control charts for variables and for attributes, accepting sampling, and capability.

STAT 334 Demography (Theory: 2, Practice: 2, Credit: 3)

This course is dedicated to teaching students about the role and applications of statistics in demography. After completing this course, students can use statistical methods to study the population and to deal with demographic data.

STAT 335 Biostatistics (Theory: 2, Practice: 2, Credit: 3)

This course is dedicated to teaching students about the role and applications of statistics in clinical trials. After completing this course, students can use statistical methods to analyze data from clinical trials.

STAT 336 Reliability Theory (Theory: 2, Practice: 2, Credit: 3)

This course is dedicated to teaching students about the role and applications of statistical methods in reliability theory. After completing this course, students can analyze reliability data which are common in industries.

STAT 337 Statistical Finance (Theory: 2, Practice: 2, Credit: 3)

This course is dedicated to teaching students about the role and applications of statistical methods in finance. After completing this course, students should be able to analyze and understand financial data.

This course is dedicated to teaching students about advanced topics in operation research.

After completing this course, students can use advance optimization models to formulate real- world problems in different areas and then solve them using operations research computer packages and analyze the outputs.

Catalog of Fourth-Year STAT Elective Courses

STAT 406 Categorical Data Analysis (Theory: 2, Practice: 2, Credit: 3)

This course is dedicated to teaching students about the role and applications of statistics in the field of qualitative analysis. After completing this course, students can use common statistical models and analysis of tabulated categorical data in which the cell entries represent counts of subjects of items falling into certain categories.

STAT 407 Nonlinear Regression (Theory: 2, Practice: 2, Credit: 3)

This course introduces the students to the theory of nonlinear model and corresponding diagnostic (remedial) procedures. After completing this course, students can perform nonlinear regression analysis and diagnose the estimated models using remedial procedures.

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STAT 413 Stochastic Processes (Theory: 2, Practice: 2, Credit: 3)

The course covers random processes that depend on time in its development, such as population growth, infection and nuclear spread. After completing this course, students can formulate solutions for problems that involve random processes and chains.

STAT 424 Decision Theory (Theory: 2, Practice: 2, Credit: 3)

This course aims to provide the student with information and advanced skills in theoretical and applied issues of decision-making theory. After completing this course, students should be able to obtain optimal decision or strategy using decision-making theory. Moreover, students should be able link between statistical problems and operations research since testing hypotheses and linear programming problems are applications of decision-making theory.

STAT 425 Order Statistics (Theory: 2, Practice: 2, Credit: 3)

This course explains the theory of order statistics which are common in many natural problems related to flood, longevity, breaking strength, atmospheric temperature, atmospheric pressure, and so on. After completing this course, students should be able to solve problems related to order statistics.

STAT 427 Bayesian Statistics (Theory: 2, Practice: 2, Credit: 3)

This course provides a general introduction to Bayesian, modeling, analysis, and computing.

After completing this course, students can analyze data from the Bayesian perspective, and have enough information about Bayes’ rule, prior and posterior distributions, conjugacy, Bayesian point estimates and intervals, Bayesian hypothesis testing, noninformative priors, practical Markov chain Monte Carlo, hierarchical models and model graphs, and more advanced topics as time permits.

STAT 436 Medical Statistics (Theory: 2, Practice: 2, Credit: 3)

This course is dedicated to teaching students about topics of medical statistics and survival data analysis. After completing this course, students can use statistical methods to medical- based problems and survival data analysis.

STAT 438 Actuarial Statistical Models (Theory: 2, Practice: 2, Credit: 3)

This course will provide students with the basic theory of actuarial models and applications of probability to insurance and other financial risks. After completing this course, students can analyze time-to-failure random variable for a single life, and its implications for evaluations of insurance and annuity functions. Moreover, the students can apply the theory and application of Markov Chain models.

STAT 450 Data Mining (Theory: 1, Practice: 4, Credit: 3)

This is a data science course, and it aims to teach the students key topics related to the field of data mining. After completing this course, students can develop data mining projects starting from problem definition to data classifications using data mining methods such as classification tree and neural network.

STAT 451 Econometrics (Theory: 2, Practice: 2, Credit: 3)

This course introduces the students to econometrics, the branch of economics concerned with the use of statistical methods in describing economic data. After completing this course, students can use statistical methods to analyze and describe economic data.

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STAT 454 Time Series Analysis (Theory: 2, Practice: 2, Credit: 3)

This course is dedicated to teaching students about time series applications and analysis. After completing this course, students can analyze time series data.

STAT 455 Applied Multivariate Analysis (Theory: 2, Practice: 2, Credit: 3)

This course aims to provide an overview of some multivariate data analysis techniques and discuss some key multivariate inferential methods. After completing this course, students can differentiate between univariate, bivariate and multivariate data and apply specialized techniques for multivariate data.

STAT 456 Spatial Statistics (Theory: 2, Practice: 2, Credit: 3)

This the aim of this course is to introduce students to methods and models that have been developed for spatial statistics. After completing this course, students can analyze and investigate geographically represented data using spatial methods.

STAT 463 Spatial Statistics (Theory: 2, Practice: 2, Credit: 3)

This course aims to provide the student with information and advanced skills in theoretical and applied issues of queueing theory. After completing this course, students can study and analyze real life service systems to optimize the performance of these systems.

STAT 480 Special Topics (Theory: 2, Practice: 2, Credit: 3)

This course consists of one or more special topics based on courses of study. After completing this course, students should gain advance knowledge in topics, including, but are not limited to the following list: Biostatistics; Missing and censored data analysis; Comparative inference;

Distribution theory and applications; Mixture distributions; Robust methods; Survival analysis.