Description
The Department of Statistics offers an integrated study program to obtain a specialized bachelor's degree. This program is designed to be sequential, coherent and methodologically flexible, with the opportunity for the student to choose a set of elective courses that suit his preferences and goals.
Objectives
Outcomes
After the student has finished studying the courses in the statistics program, the graduate should be able to:
Certificate Rewarded
Bachelor's degree
Entry Reuirements
- The student must have a high school diploma or an equivalent certificate recognized by government agencies.
- To have a college admission percentage.
- To be a believer in the values and orientations of society.
- If the student applying for the study is a non-Libyan, then he is required to reside in Libya throughout the study period, and to pay the study expenses and the prescribed fees in accordance with the rules and regulations in force in the study.
Study Plan
The Bachelor in Statistics prepares students to qualify for Bachelor in Statistics. The student studies several subjects which have been carefully chosen in this major to cover its different aspects.
It comprises 8 Semesters of study, in which the student will study a total of 136 units, which include 11 units of general subjects, and 75 major units, 7 of elective units. In addition to a final project in the student's major.
Study plan for this program is shown below:
1st Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
CS100 | Programming Principles | 04 | General | + |
1. Introducing the steps to solve the problem and the methods of solving it using textual algorithms and flowcharts, developing the ability to think logically to solve problems, and identifying numerical systems and converting between them.2. Introduction to the basics and components of Python language programs and how to convert text algorithms or flowcharts into Python programs3. Raising the student's programming level by identifying functions and menus and how to detect errors.
EL.101 | English Language 1 | 02 | University requirement | + |
English 101 are courses specially designed for students who choose to study at the faculty of Basic Sciences. The principle objectives for both courses is to enable students use English for scientific. They provide students with practice on sentence patterns, structural words as well as non-structural vocabulary which are common to all scientific branches. The material incorporated in these courses intend to give students a good opportunity to read scientific texts, do grammar exercises and work on scientific terminology.
AR051 | Arabic language 1 | 02 | University requirement | + |
Highlighting the beauty of the Arabic language and revealing the elements of originality and strength that are full of it, so that students increase their passion and interest in it.Close contact with our literary heritage and make students aware of its originality, diversity and comprehensiveness.Refine students' talents and develop their ability to understand the language, grammar, morphology and correct Arabic writing.Training students to write their scientific research, reports, and notes in correct writing, free of linguistic, stylistic, and spelling errors, and to facilitate the translation of many specialized texts.
MA101 | Mathematics 1 | 04 | Compulsory | + |
The general objectives of the course in the form of outputs that the student is supposed to acquire after successful completion of the course are:· The student identifies the types of coordinates and converts points from one to the other and represents them as vectors .· Study the geometric object, rotation of axes and their displacement and its effect on points and equations in the plane.· The student shows how to find the equation of a straight line in its different formulas and forms .· The student compares cones with their standard images .
MA102 | Plan Analytic Geometry | 03 | Compulsory | + |
ST101 | Introduction to statistics | 04 | Compulsory | + |
1- Introducing the student to statistics, its importance, types of data, methods of collecting and summarizing them.2- The use of measures of central tendency and measures of dispersion, torsion and kurtosis.3- The concept of linear correlation of Pearson and Spearman and simple linear regression and its relationship to correlation.4- General concepts of probability.
2nd Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
CS111 | Structured Programming I | 04 | General | CS100 | + |
1. Learn basic concepts in computers, programming language, data types, and develop logical thinking skills. Converting algorithms into a program in Fortran.2. Identifying input and output sentences, simple and compound sentences, logical expressions, test operations, and repetitive sentences.3. Study and understand how to format data and correct errors in the program.4. Learn about advanced data structures such as matrices and employ sub-programs to improve program efficiency and the ability to read and understand programs.
MA103 | Solid Analytic Geometry | 03 | Compulsory | MA102 | + |
The general objectives of the course in the form of outputs that the student is supposed to acquire after successful completion of the course are: · The student interprets vectors and connects them to points in Cartesian, cylindrical, and spherical coordinates. · The student recognizes the equations of the line and imagine the solids such as the ball, cylinder and others and find their equations with drawing. · It recognizes the quadratic shape in three variables and reduces it to legal forms.
ST102 | An Introduction to Probability | 04 | Compulsory | ST101 | + |
1- This course aims to expand the student's perceptions of the distinction between probability distributions and their applications. 2- Studying the terms and concepts related to special probability distributions.3- Identify the concept of confidence intervals for the mean and ratio and use them for a sample or for the difference between two samples.4- Identify hypothesis tests for the mean and ratio and use them, whether for one sample or for the difference between two samples.
3rd Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
AR052 | Arabic language 2 | 02 | University requirement | AR051 | + |
Accustom the student to clear expressions of his ideas in pronunciation and writing and the good use of punctuation marks.Developing the student's literary taste so that he realizes the aesthetic aspects of speech styles, meanings and images.Develop the student's spelling and writing ability and skill so that he can write correctly in all respects.Identify the beauty of the Arabic language and literature, and that the student acquires the ability to study the branches of the Arabic language.
ST209 | Statistical Packages | 03 | Compulsory | ST102 | + |
1- Introducing the various statistical programs R and their importance to the student of statistics.2. Methods used to bring data from other systems into the R space.3- Introducing public libraries that help the student to deal with data in the R space.4- The student should learn to write simple software and transfer its results to other software.
MA200 | mathematics 3 | 03 | General | MA102 | + |
The student recognizes functions in more than one variable and their properties and the study of limits and connection.· Discusses the differentiation of functions in more than one variable and its applications.· The student demonstrates the properties of binary and triple integration in different coordinates.· The student is introduced to point and directional multiplication, gradient, divergence, convolution, linear integration and its theories.· The student interprets the convergence and divergence of sequences and series.
ST201 | Mathematical Statistics (1) | 04 | Compulsory | ST102 | + |
1. The concept of discrete and continuous random variable and its distribution functions2- The concept of mathematical expectation and the moment-generating function3. Identify discrete and connected probabilistic models and their applications4- The concept of a function in a random variable and how to know its probability distribution
4th Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
ST204 | Quality Control | 03 | Compulsory | ST102 ST209 | + |
Ø Identify the basic concepts of the quality control. Ø The control charts for the variables. Ø The control charts for the properties. Inspection with sampling and working curve
ST202 | Mathematical Statistics (2) | 04 | Compulsory | ST201 | + |
· Introducing the concept of joint distributions functions. · How to find marginal distributions from joint distributions. · How to find conditional expectation, conditional variance and moments generating function technique. · Using transformations to find functions from others.
MA214 | Advanced Calculus and Analysis Principle | 04 | University requirement | + |
The general objectives of the course in the form of outputs that the student is supposed to acquire after successful completion of the course are: · The student recognizes functions in more than one variable and their properties. · Discusses the differentiation of functions in more than one variable and its applications. · The student demonstrates the properties of binary and triple integration in different coordinates. · The student explains the convergence and divergence of series.
MA206 | 04 | General | + |
MA201 | Ordinary Differential Equation1 | 03 | University requirement | MA102 | + |
The general objectives of the course in the form of outputs that the student is supposed to acquire after successful completion of the course are: · Recognize the basic concepts of an ordinary differential equation. · The student learns methods of solving equations of the first order and the problem of the initial value and ensuring the existence of the solution or not in certain circumstances. · The student acquires the ability to solve linear differential equations. · The student uses Laplace transformations to solve linear equations.
EL102 | English language 2 | 02 | University requirement | EL101 | + |
English are courses specially designed for students who choose to study at the faculty of Basic Sciences. The principle objectives for both courses is to enable students use English for scientific. They provide students with practice on sentence patterns, structural words as well as non-structural vocabulary which are common to all scientific branches. The material incorporated in these courses intend to give students a good opportunity to read scientific texts, do grammar exercises and work on scientific terminology.
5th Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
ST317 | Sampling Techniques | 03 | Elective | ST102 | + |
Identify the purposes from using sampling techniques in studding population. The knowledge of different probability samples. Learning how to estimate the population's parameters form different samples. calculating the sample size for given precision.
MA321 | Complex Analysis | 03 | Compulsory | MA200 MA203 | + |
The general objectives of the course in the form of outputs that the student is supposed to acquire after successful completion of the course are: · The student learns how to deal with complex numbers. · The student recognizes the limits, continuity, and differentiation of functions in the complex variable. · The student discusses the concepts of analytic, holomorphic and harmonic functions and their properties. · The student is introduced to non-algebraic and algebraic functions.
ST308 | Design of Experiment | 04 | Compulsory | ST102 ST209 | + |
Identify the basic concepts of experimental design in bracts. The condition of using every design. How to build the mathematical form and to estimate the parameters. How to handle every design manually and with SPSS .
ST301 | Sampling Distributions | 04 | Compulsory | ST202 | + |
· Clarify the basic concepts in mathematical statistics..· Know the distributions of some random variables and their applications.· Know the ordered statistics.· Understand central limit theorem and limit theorems
CS321 | 04 | Compulsory | CS111 | + |
ST302 | Nonparametric Methods | 03 | Compulsory | ST102 | + |
· To introduce the principles and applications of commonly used nonparametric methods. · To compare these methods to their parametric counter parts. · To introduce the basic methods for analyzing contingency tables. · The student learns how to using statistical program to perform various statistical tests .
6th Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
ST305 | Statistical Inference | 04 | Compulsory | ST301 | + |
To introduce the basic concepts of Estimation theory and methods of finding estimators. The way of finding moments and maximum likelihood estimators and how to obtain good estimators. Concept of sufficient estimators and methods of finding them and method of Bayes estimators. Confidence intervals and test of a statistical hypothesis. Likelihood ratio tests and methods of finding uniformly most powerful tests.
ST314 | Regression Analysis | 03 | Compulsory | ST209 ST308 | + |
Outcomes that the student is supposed to acquire after successful completion of the course.· Understand the statistical principles and methods of regression analysis· Gain proficiency in performing standard regression analyses.· Understand the advantages and limits of regression as an approach to data analysis
ST315 | Demography | 03 | Compulsory | ST102 | + |
Definition of demography, vital statistics, general population estimation.· Calculation of statistical indicators and measures related to population.· The use of some statistical programs in the process of analyzing demographic data.· Understand the concept of population growth and life schedules.
ST318 | Time Series Analysis and Forecasting | 04 | Compulsory | ST209 | + |
Knowledge of time series analysis and methods of forecasting.· Learn about the properties of data and how to work with them.· How to choose the appropriate model for time series using statistical programs.· Developing the student's skills in using Yox Jenkins models in data analysis.
7th Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
ST400 | Applied Linear Models | 04 | Compulsory | ST209 ST308 | + |
· The student learned about the concept of linear models, multiple normal distributions, related distributions, and independence.· The student was introduced to the distributions of quadratic formulas, the concept of regression and correlation, design models, and models of components of variance.· The student was introduced to the simple linear model in terms of estimation and hypothesis tests.· Introduce the student to the use of linear models in data analysis using computers.·
ST406 | Stochastic Process | 03 | Compulsory | MA214 ST202 | + |
· Provide a general idea of what the stochastic process means. · Introducing the concept of the Markov property and its applications and knowing the importance of Markov chains. · Teach how to find transition matrices and stable distributions · Know the continuous Markov models such as births, deaths and waiting models.
ST408 | Categorical Data Analysis | 04 | Compulsory | ST209 ST308 | + |
· Develop the student's experience in the description and statistical inference of tables with different directions.· Expand student experience in building and interpreting models for binary response data· Tests for three-way tables.· Identifying logistic regression and its applications
ST409 | Survival Data Analysis | 04 | Elective | ST305 ST308 | + |
Identify the basic concepts in analyzing life data. Estimating the life curve and comparing two or more curves. Methods of analyzing life data and its applications in a practical way. Identify some parametric models for analyzing life data.
ST410 | Operation Research | 03 | Compulsory | MA206 ST209 | + |
· Knowing how to model some life problems in the form of a mathematical model, · finding the optimal solution of them. · Designing forms for transporting goods to demand · Reaching an understanding of the scientific basis for decision making in the game theory
8th Semester
Code | Title | Credits | Course Type | Prerequisite |
---|
ST499 | Research Project | 03 | Compulsory | + |
Introduce the student to the method of scientific research and identify a topic for research, theoretical or applied.· Ability to collect and analyze data from various sources required by the research project.· The student acquires scientific research skills such as writing, analyzing and discussing.· The student's knowledge of using sources and references correctly.
ST412 | Introduction to Bayesian Data Analysis | 04 | Elective | ST209 ST314 | + |
Introducing the concepts of Bayes' theory philosophy for statistical models.· Introduce and illustrate Bayes' models to solve a number of statistical problems and analyze big data.· Using statistical software to analyze Bayes' models.· Introducing the concept of the accompanying family in general and the Markov simulation based on Bayes' analysis.
ST411 | Econometrical Statistics | 03 | Compulsory | ST102 ST209 | + |
· Identify the index numbers(price, value and quantity) that used in economic studies. · Synthesis of index numbers and comparison of different formulas · Identify the index number using the series method · Know the consumer price index · Knowledge of the production index in national economic and manufactures. · Income distribution indicators.
ST407 | Applied Multivariate Analysis | 03 | Compulsory | ST305 ST400 | + |
Introduce students to a variety of basic ideas in multivariate analysis.2- Focus on intuitive understanding and applications of multivariate methods for datasets using R, Matlab, Minitab3- Introducing the student to basic compounds, factor analysis and cluster analysis.4- Introducing the student to the legal association.
ST404 | Statistical Topics | 03 | Compulsory | ST305 ST308 | + |
identify the analysis of covariance with one associated variable, the covariance by analyzing the single and binary variance of one associated variable.· Recognize the basic concept of generalized linear models, logistic regression and Poisson regression.· Know nonlinear models and polynomial regression.· Understand the applications of bootstrap and remodeling, bias, standard error, confidence intervals and bootstrap in regression.