Semester 1
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.
Credit:
3
Lecture Hour (hrs/week):
3
Lab (hrs/week):
1
Tutorial (hrs/week):
-
ECTS:
6
Brief history of statistics. Basic definitions and types of data. Visualization of data. Descriptive statistics. Avoiding misleading conclusions. Random variables. Some known statistical distributions. Introduction to use of computer solving tools.
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):
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:
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 2
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.
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.
Credit:
3
Lecture Hour (hrs/week):
3
Lab (hrs/week):
-
Tutorial (hrs/week):
1
ECTS:
6
Sampling distributions, estimation, confidence intervals, hypothesis testing, distribution fitting, analysis of variance for one factor design, linear regression, association between two categorical variables, basic nonparametric procedures.
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):
3
Lab (hrs/week):
-
Tutorial (hrs/week):
1
ECTS:
4
ENGL192 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. 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
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.
Semester 3
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.
Credit:
4
Lecture Hour (hrs/week):
3
Lab (hrs/week):
-
Tutorial (hrs/week):
2
ECTS:
8
Introduction to statistical techniques in statistical software. Managing and analyzing data using statistical database packages. Introduction to MATLAB with applications to matrix algebra.
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.
Turkish as a Second Language (TUSL181)
Credit:
2
Lecture Hour (hrs/week):
2
Lab (hrs/week):
-
Tutorial (hrs/week):
-
ECTS:
2
Atatürk İlkeleri ve İnkilap Tarihi (HIST280)
Credit:
2
Lecture Hour (hrs/week):
2
Lab (hrs/week):
-
Tutorial (hrs/week):
-
ECTS:
2
Semester 4
Credit:
4
Lecture Hour (hrs/week):
3
Lab (hrs/week):
-
Tutorial (hrs/week):
2
ECTS:
7
Sampling distributions. Sample drawing techniques. Estimation and testing for one or two population characteristics. Maximum likelihood estimation of parameters. Measures of association. Simple and multiple regression. Introduction to design of experiments, analysis of variance; one-way, multiway classifications. Multiple comparisons. Basic nonparametric procedures. Elementary time series analysis; trends, seasonality, forecasting. Indexing. Some applications in medicine, science, engineering and social sciences.
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.
Credit:
4
Lecture Hour (hrs/week):
3
Lab (hrs/week):
-
Tutorial (hrs/week):
2
ECTS:
8
Introduction to programming and computation. Introduction to computer organization and basic data structures. An advanced programming language with applications to statistical procedures. Statistical programming with R
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
Credit:
3
Lecture Hour (hrs/week):
3
Lab (hrs/week):
1
Tutorial (hrs/week):
-
ECTS:
7
Introduction to survey sampling. Probability sampling techniques. Simple random sampling. Stratified element sampling. Systematic sampling. Equal sized cluster sampling. Unequal sized cluster sampling. PPS selection techniques. Sampling errors. Survey research methods. Planning of sample surveys. Questionnaire design techniques. Survey research project.
Credit:
3
Lecture Hour (hrs/week):
3
Lab (hrs/week):
1
Tutorial (hrs/week):
-
ECTS:
7
Sample mean vector and sample covariance matrix; matrix decomposition; multivariate normal and Wishart distributions; parameter estimation; hypothesis testing; MANOVA; principal components; factor analysis; multivariate classification and clustering; canonical correlation.
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.
Credit:
3
Lecture Hour (hrs/week):
3
Lab (hrs/week):
1
Tutorial (hrs/week):
-
ECTS:
5
Review of basic statistics. Distribution-free statistics, ranking statistics, U statistics. Large sample theory for U statistics. Tests based on runs. Asymptotic relative efficiency of tests. Hypothesis testing, point and interval estimation. Goodness of fit, rank-order (for location and scale), contingency table analysis and relevant models. Measures of association, analysis of variance.
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
Credit:
-
Lecture Hour (hrs/week):
-
Lab (hrs/week):
-
Tutorial (hrs/week):
-
ECTS:
5
20 (twenty) working days internship in a private or public institution with functionality in the field of statistics.
Semester 7
Credit:
3
Lecture Hour (hrs/week):
3
Lab (hrs/week):
-
Tutorial (hrs/week):
-
ECTS:
8
Basic definition of insurance. Historical background. Insurance applications in government and private sector, regulations and legislation in insurance. Fundamentals of insurance. Types of insurance, disaster insurance and risk management applications around the world. Turkish catastrophe insurance pool. Definition of risk, probability aspect of risk. Utility theory, claim processes, distribution of claim processes.
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.
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
Credit:
4
Lecture Hour (hrs/week):
3
Lab (hrs/week):
-
Tutorial (hrs/week):
2
ECTS:
-
Reading raw data files, reading data sets. Writing the results to files, or data sets. Susetting data. Combining multiple files. Creating variables and recording data values. Creating listing and summary reports. (Preferably by using one of the following softwares: Matlab, SAS, STATA, R, Python)
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:
-
