BS: Statistics and Computer Science

Department of Mathematics was established as one of the first departments shortly after the foundation of EMU. At the beginning, its mission was just to provide service courses in Mathematics and Computer Science. Over the past 30 years, the department has been evolving to one of the biggest and most international departments of EMU. The Department has become one of the most scholarly and competitive mathematics departments in the Turkish-speaking region and the Middle East. The main aim of the department is to educate students in becoming competitive at the international arena and providing high quality educational and research services in their own countries. The Statistics and Computer Science Program is offered by the Mathematics Department.

General Information

The Statistics and Computer Science program is to provide students with a strong basis in the fields of both Statistics and Computer Science, and is to combine the two fields with the aim of train graduates who have the ability to perform advanced data analysis. Students attending the program will have the comprehensive knowledge of topics such as statistical analysis, probability, mathematics and computer science, and will be able to apply such skills in various different fields. By the virtue of the learning outcomes of the program, graduates will have the necessary skills to perform advanced data analysis which will play a vital role in future projections and strategic decision making in their professional lives.

The Statistics and Computer Science program trains students who have the skill of carrying a theoretical data into action in professional settings such as all state institutions with statistician staff, computer software companies, private research companies and especially in finance related companies and banks.

Education

Students of the program are required to obtain total credits of 129 (240 ECTS) in order to be able to graduate from the program and, are subject to an internship scheme within the scope of the course with STAT400 course code during the sixth semester of the program where the students are offered a setting with real life work experiences.

The courses parallel with the courses of the Applied Mathematics and Computer Science Program and Actuarial Science Program will be delivered as joint courses and, therefore the interaction between the programs will be enhanced. Double major opportunities offered to our students will provide the graduates better employment opportunities.

Facilities

Academic staff at the Department of Mathematics consists of 21 prominent academicians who have devoted themselves to teaching and research. More specifically, the academic staff members consist of 9 professors, 2 associate professors and 10 assistant professors. All full-time academic staff are PhD holders, granted the degrees from various prominent international institutions. Moreover, 12 part-time PhD holder staff members and 19 full-time research assistants (master and PhD) are also employed at the department.

Career Opportunities

The Statistics and Computer Science program graduates have the skill of carrying a theoretical data into action in professional settings such as all state institutions with statistician staff, computer software companies, private research companies and especially in finance related companies and banks. Graduates of the department have career opportunities in sectors such as marketing, advertisement, tourism, hospital management, human research and development sectors as well as seeking an academic career as a teacher in an event of completing a pedagogical formation training. Moreover, graduates of the Statistics and Computer Science program have the opportunity to continue their academic studies in post-graduate programs offered by statistics, computer sciences, actuary, actuary and risk management, insurance and actuarial sciences, insurance and risk management and, industrial engineering departments to become academic staff members in higher education institutions.

Contact

Tel: +90 392 630 1227
Fax: +90 392 365 1314
E-mail: math@emu.edu.tr
Web: http://math.emu.edu.tr


* You can contact the department and / or faculty for detailed information about elective courses.

Semester 1

Calculus - I (MATH151)

Credit: 4 | Lecture Hour (hrs/week): 4 | Lab (hrs/week): 1 | Tutorial (hrs/week): - | ECTS: 7
Limits and continuity. Derivatives. Rules of differentiation. Higher order derivatives. Chain rule. Related rates. Rolle's and the mean value theorem. Critical Points. Asymptotes. Curve sketching. Integrals. Fundamental Theorem. Techniques of integration. Definite integrals. Application to geometry and science. Indeterminate forms. L'Hospital's Rule. Improper integrals. Infinite series. Geometric series. Power series. Taylor series and binomial series.

Principles of Statistics (STAT151)

Credit: 3 | Lecture Hour (hrs/week): 1 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 6
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 6
Sets and set operations. Relations and functions: binary relation, equivalence relation, partial order, types of functions, composition of functions, inverse function. Integers and their properties: integers, primes, divisibility, fundamental theorem of arithmetic. Logic and proofs: propositions, theorem, tautology and contradiction, direct proof, proof by contradiction, proof by contraposition, proof by induction. Recursion: recursively defined sequences, homogeneous and inhomogeneus recursive relations, characteristic polynomial, solving recurrence relations. Principles of counting: the addition and multiplication rules, the principle of inclusion-exclusion, the pigeonhole principle. Introduction to Combinatorics: permutations and combinations, repetitions, derangements, the binomial theorem. Boolean algebra: basic Boolean functions, digital logic gates, minterm and maxterm expansions, the basic theorems of Boolean algebra, simplifying Boolean function with Karnaugh maps.
Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: 7
Organization of a digital computer. Number systems. Algorithmic approach to problem solving. Flowcharting. Concepts of structured programming. Programming in at least one of the programming languages. Data types, constants and variable declarations. Expressions. Input/output statements. Control structures, loops, arrays.
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 4
ENGL191 is a first-semester freshman academic English course. It is designed to help students improve the level of their English to B1+ level, as specified in the Common European Framework of Reference for Languages. The course connects critical thinking with language skills and incorporates learning technologies such as IQ Online. The purpose of the course is to consolidate students’ knowledge and awareness of academic discourse, language structures, and lexis. The main focus will be on the development of productive (writing and speaking) and receptive (reading) skills in academic settings.
Credit: 3 | Lecture Hour (hrs/week): 5 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 4
ENGL 181 is a first-semester freshman academic English course. It is designed to help students improve the level of their English to B1+ level, as specified in the Common European Framework of Reference for Languages. The course connects critical thinking with language skills and incorporates learning technologies such as IQ Online. The purpose of the course is to consolidate students’ knowledge and awareness of academic discourse, language structures, and lexis. The main focus will be on the development of productive (writing and speaking) and receptive (reading) skills in academic settings.
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 6
Introduction to probability and statistics. Operations on sets. Counting problems. Conditional probability and total probability formula, Bayes' theorem. Introduction to random variables, density and distribution functions. Expectation, variance and covariance. Basic distributions. Joint density and distribution function. Descriptive statistics. Estimation of parameters, maximum likelihood estimator. Hypothesis testing.

Semester 2

Calculus - II (MATH152)

Credit: 4 | Lecture Hour (hrs/week): 4 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 7
Vectors in R3. Lines and Planes. Functions of several variables. Limit and continuity. Partial differentiation. Chain rule. Tangent plane. Critical Points. Global and local extrema. Lagrange multipliers. Directional derivative. Gradient, Divergence and Curl. Multiple integrals with applications. Triple integrals with applications. Triple integral in cylindrical and spherical coordinates. Line and surface integrals. Independence of path. Green's Theorem. Conservative vector fields. Divergence Theorem. Stokes' Theorem.

Linear Algebra (MATH106)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 6
Systems of linear equations: elementary row operations, echelon forms, Gaussian elimination method. Matrices: elementary matrices, invertible matrices, symmetric matrices, quadratic forms and Law of Inertia. Determinants: adjoint and inverse matrices, Cramer's rule. Vector spaces: linear independence, basis and dimensions, Euclidean spaces. Linear mappings: matrix representations, changes of bases. Inner product spaces: Cauchy-Schwarz inequality, Gram-Schmidt orthogonalization. Eigenvalues and eigenvectors: characteristic polynomials, Cayley-Hamilton Theorem, Diagonalization, basic ideas of Jordan forms.

Statistical Methods (STAT152)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 6
Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: 7
An overview of C++ programming Language: Data types, constants and variable declarations, expressions, assignment statements, input/output statements. Control structures. Loops. Arrays. Sorting and searching arrays. User defined functions. Pass-by-value and Pass-by-reference methods of call. Recursion. Data Files.
Credit: 3 | Lecture Hour (hrs/week): 5 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 4
ENGL182 is a second-semester freshman academic English course. It is designed to help students improve the level of their English to B2 level, as specified in the Common European Framework of Reference for Languages (CEFR). The course connects critical thinking with language skills and incorporates learning technologies such as IQ Online. The purpose of the course is to consolidate students’ knowledge and awareness of academic discourse, language structures, and lexis. The main focus will be on the development of productive (writing and speaking) and receptive (reading) skills in academic settings.
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 4
ENGL191 is a first-semester freshman academic English course. It is designed to help students improve the level of their English to B1+ level, as specified in the Common European Framework of Reference for Languages. The course connects critical thinking with language skills and incorporates learning technologies such as IQ Online. The purpose of the course is to consolidate students’ knowledge and awareness of academic discourse, language structures, and lexis. The main focus will be on the development of productive (writing and speaking) and receptive (reading) skills in academic settings.

Semester 3

Statistical Computing - I (STAT221)

Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: 8
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 8
Complexity measure. Asymptotic notation. Time-space trade-off. A study of fundamental strategies used in design of algorithm classes including divide and concur, recursion, search and traversal. Backtracking. Branch and bound techniques. Analysis tools and techniques for algorithms. NP-complete problems. Approximation algorithms. Introduction to parallel and fast algorithms. Primitive data structures Linear data structures: stacks, queues, deques and their application. Concept of linking, linked lists. Non-linear data structures: trees, graphs. Algorithmic implementation of data structures.
Credit: 3 | Lecture Hour (hrs/week): 2 | Lab (hrs/week): - | Tutorial (hrs/week): 3 | ECTS: 6
Internet-based programming languages, introduction to internet programming architecture and client/server architecture. Process of site design, introduction to HTML, connections and use of internet addresses, use of web editor, use of picture and image with HTML, page design, backgrounds, colors and text with HTML, tables and lists with HTML, borders and layers with HTML, HTML forms and form components, use of HTML templates, XML, RSS, Blog, Cascading Style Sheets (CSS), web site projects and applications, dynamic sites with HTML, JavaScript, JavaScript operators and data types, main concepts in internet based education, theoretical terms in internet based education, use of design principles in internet based education.

Atatürk İlkeleri ve İnkilap Tarihi (HIST280)

Credit: 2 | Lecture Hour (hrs/week): 2 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 2

Turkish as a Second Language (TUSL181)

Credit: 2 | Lecture Hour (hrs/week): 2 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 2

Semester 4

Applied Statistics (STAT242)

Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: 7
Credit: 3 | Lecture Hour (hrs/week): 2 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: 5
Foundations of Computer Networks and Its Architecture, topologies and types of Computer Network, Protocols and Procedures of Computer Network Systems and OSI model, Network connection devices active and passive devices, LAN communication technologies (802.X and Ethernet, token ring FDDI), WAN communication technologies (x25, DSL, ISDN, FR etc.), Network Operating Systems, Communication on Network Systems, Management of Network System, communication on internet: E-Mail, instant message programs, sending and receiving files on internet, using FTP programs, Network security, set up web servers like DHCP, DNS (domain name system), Web server, database server.
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 5
Ordinary differential equations of the first order; separation of variables, exact equations, integrating factors, linear and homogeneous equations. Special first order equations; Bernoulli, Riccati, Clairaut equations. Homogeneous higher order equations with constant coefficients. Nonhomogeneous linear equations; variation of parameters, operator method. Power series solution of differential equations. Laplace transforms. Systems of linear differential equations.
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 1 | ECTS: 5
Number and counting; odometer principle, principle of induction, order of magnitude, handshaking lemma, set notation. Subsets, partitions, permutations; subset, subset of fixed size, the binomial theorem, pascal's triangle, Lucas' theorem, permutations, estimates for factorials, Cayley's theorem on trees, Bell numbers, generating combinatorial objects. Recurrence relations and generating functions; Fibonacci numbers, linear recurrence relations with constant coefficients, derangements and involutions, Catalan and Bell numbers. The principle of inclusion and exclusion; PIE and its generalization, Stirling numbers and exponentials, even add odd permutations. System of distinct representatives; Hall's theorem. Extremal set theory; intersecting families, Erdos-Ko-Rado theorem, Sperner's theorem, the de Brujin-Erdos theorem. Graphs; trees and forests, Cayley's theorem, minimal spanning tree, Eulerian graphs, Hamiltonian graphs, Ore's theorem, gray codes, the traveling salesman, digraphs, networks, max-flow min-cut theorem , integrity theorem, Menger's theorem, Könsg's theorem, Hall's theorem, diameter and girth. Ramsey's theoremlthe pigeonhole principle, bounds for Ramsey's theorem, Applications, infinite version.

Statistical Computing - I (STAT222)

Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: 8

Semester 5

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): 1 | Tutorial (hrs/week): - | ECTS: 7
Concept of regression, Regression data and kinds of data, Simple linear regression analysis, Assumptions, Confidence intervals and tests of hypotheses, Concept of correlation, Multiple linear regression analysis, Assumptions, Confidence intervals and tests of hypothesis, Concept of correlation, Dummy variables, Residuals and outliers, Heteroscedasticity, Correlated errors/autocorrelation, Nonmorality, Multicollinearity, Choice of the best model, Computer applications

Sampilng Techniques (STAT361)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): 1 | Tutorial (hrs/week): - | ECTS: 7

Multivariate Statistical Analysis (STAT351)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): 1 | Tutorial (hrs/week): - | ECTS: 7

University Elective - I (UE01)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 4

Area Elective I (AE01)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 5

Semester 6

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): 1 | Tutorial (hrs/week): - | ECTS: 5
Discrete-time models: Martingale and arbitrage opportunities, financial markets and option pricing, Optimal stopping problem and American options: Stopping time, decomposition of super-martingales, application to the American option, Continuous-time processes and stochastic differential equations: General comments, Brownian motion, continuous-time martingales, stochastic integral and Ito calculus, stochastic differential equations. The Black-Scholes model: Description of the model, the Girsanov theorem, pricing and hedging of options in the Black-Scholes model, American options in the Black-Scholes model, Option pricing and partial differential equations: European option pricing and diffusions, solving parabolic equations numerically, American options, Interest rate models: Modelling principles, some classical models, Asset models with jumps: Poisson process, dynamics of risky assets, Simulation and algorithms for financial models.

Nonparametric Statistical Methods (STAT342)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): 1 | Tutorial (hrs/week): - | ECTS: 5
Credit: 3 | Lecture Hour (hrs/week): 2 | Lab (hrs/week): - | Tutorial (hrs/week): 3 | ECTS: 5
Introduction to database management systems (DBMS), relational database model, mathematical relations, relational algebra, entity relationship (ER) model, entity and entity sets, entity relationship diagram (ERD), normalization, normalization forms, Structured Query Language (SQL), SQL functions, database security and integrity, transaction management, concurrency control, distributed database.
Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 5
Fundamental objectives of computer security: data and information confidentiality, integrity and availability. Three aspects of security: security attacks, security mechanisms and security services; a model of network security. Classical encryption techniques: cryptanalysis and brute-force attack;substitution techniques: Ceasar's cipher, monoalphabetic ciphers, Playfair cipher, Hill cipher and its modifications; polyalphabetic ciphers: Vigenere cipher; transposition techniques: rail fence and other techniques; rotor machines. Modern encryption techniques – block ciphers: diffusion and confusion principles, DES family, IDEA and blowfish. Basic concepts in number theory. Asymmetric-key cryptography – public-key cryptogrwphy, RSA. Integrity of cryptographic data: message authentication, digital signatures, cryptographic hash functions, message authentication codes, MD5, key management and disctribution.

Area Elective II (AE02)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 5

Internship (STAT400)

Credit: - | Lecture Hour (hrs/week): - | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 5

Semester 7

Insurance and Actuarial Analysis (STAT477)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 8

Operating Systems (COMP483)

Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: 7
Operating system and its functions. Interprocess communication, process sequence. Memory management, plural programming, replacement, paging, virtual memory. File system, security and protection mechanisms. Occlusions. Examination of MS DOS and UNIX operating systems.

Big Data (COMP413)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 6
Huge amount of unstructured data. Collecting and analyzing data before and after computers and internet. Handling, storing and processing of big data. Elimination of redundant data. 5 V’s in Big Data. Cloud data and computing. Parallel algorithms for cloud computing. Hadoop system. Hadoop training. Modules of Hadoop. Map reduce algorithm. Stream computing. Big data in machine learning. Mining big data. Web search. Big data programming. Text analytics.

Area Elective III (AE03)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 5

University Elective - II (UE02)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: 4

Semester 8

Advanced Statistical Computing (STAT488)

Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: -

Data Analytics (COMP414)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: -
Descriptive and inferential Statistics. Discrete and continuous random variables. Probability distributions. Regressions analysis. Analysis of Variance (ANOVA). Simple linear regression. Multiple linear regression. Classification. Naïve Bayes classifier. Decision trees. Support Vector Machines (SVM). Learning Vector Quantization (LVQ). Association rules. Apriori algorithm. Cluster analysis methods. Logistic regression.
Credit: 4 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): 2 | ECTS: -
Overview of Programming Languages. Syntax and Semantics. Names, Bindings and Scopes. Data Types. Expressions and and assignment statements. Statement – level control structures. Subprograms. Abstract Data Types. Concurrency. Exception Handling. Object Oriented Programming Languages. Logic programming languages. Functional programming languages.

Area Elective IV (AE04)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: -

University Elecitive - III (UE03)

Credit: 3 | Lecture Hour (hrs/week): 3 | Lab (hrs/week): - | Tutorial (hrs/week): - | ECTS: -

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BS--Statistics-and-Computer-Science