## Courses

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**Courses**

**STAT 101 Introduction to Statistics and Data Science I (3-2)4**

Understand the fundamentals of statistics and data science, understanding the difference between a population and a sample in a given dataset. Basic statistical definitions and learn how to work with different sorts of data. How to visualize different data types. Descriptive statistics: measures of central tendency, asymmetry, variability, correlation and covariance. What are the random variables in a dataset? Distinguish and work with different distributions that describe distinct data sources.

**STAT 102 Introduction to Statistics and Data Science II (3-2)4**

Basic statistical analyses in different types of data. Sampling distributions of distinct data sources. Inferential statistics in the data science. Estimation, confidence intervals and hypothesis testing under various data types. Distribution fitting and analysis of variance for one factor design in a given dataset. Linear regression and association between two categoric variables. Basic nonparametric procedures under various data types.

Prerequisite: STAT 101 or STAT 155

**STAT 112 Introduction to Data Processing and Visualization (3-2)4**

Basic definitions and managing different types of data. Introduction to manipulation (indexing, subsetting, reshaping, transforming etc.), visualization, mapping and analysis of data. Dealing with common problems like missing or inconsistent values in datasets. Use of related R and/or Python programming packages. Merging multiple data tables (equivalent to an SQL JOIN)

**STAT 201 Introduction to Probability and Statistics I (3-0)3**

Experiments and events. Set theory. Axioms and basic theorems of probability. Finite sample spaces and counting techniques. Independent events. Conditional probability. Random variables and distributions. Expectation, variance, covariance and correlation. Some special distributions.

**STAT 202 Introduction to Probability and Statistics II (3-0)3**

Random samples. Sample mean and variance. Chebychev's inequality. Law of large numbers. Central limit theorem. Estimation. Maximum likelihood, unbiased, minimum variance unbiased, consistent and efficient estimators. Sufficiency. Confidence intervals. Hypothesis testing. Introduction to nonparametric methods. Regression and analysis of variance.

Prerequisite: STAT 201

**STAT 203 Probability I (3-2)4**

Sample space, events, basic combinatorial probability, conditional probability, Bayes' theorem, independence, random variables, distributions, expectation.

**STAT 204 Probability II (3-2)4**

Transformations of random variables, generating functions, conditional expectation, limit theorems, central limit theorem, limiting distributions.

Prerequisite: STAT 203, MATH 119

**STAT 250 Applied Statistics (4-2)5**

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.

Prerequisite: STAT 102 or STAT 156

**STAT 256 Numerical Methods (3-2)4**

Accuracy in numerical computations. Numerical solution of linear and nonlinear algebraic equations. Finding eigen-values and eigenvectors. Finite difference calculus. Interpolation and extrapolation. Numerical differentiation and integration. Numerical approximation methods.

Prerequisites: STAT 291 or STAT 292, MATH 260

**STAT 291 Statistical Programming (3-2)4** (Statistical Computing I in the Old Program)

Introduction to statistical techniques in statistical software available in the department or on the campus. Managing and analyzing data using statistical database packages like R. Introduction to MATLAB with applications to matrix algebra.

Prerequisites: CENG 240, STAT 102 or STAT 156

**STAT 292 Statistical Computing II (3-2)4**

Introduction to programming and computation. Introduction to computer organization and basic data structures. An advanced programming language with applications to statistical procedures.

Prerequisite: CENG 240

STAT 295 Object Oriented Programming (3-2)4

Introduction to Object-Oriented Programming (OOP) with a language (e.g C++, Python). Programming elements. Functions. Classes and objects. Constructors and destructures. Operator overloading. Type conversion. Inheritance. Pointers. Polymorphism. Templets. Exception handling. String manipulation. File handling. Advanced Input/Output. OO system analysis, design and development.

Prerequisite: CENG 230 or CENG 240

**STAT 303 Mathematical Statistics I (3-2)4**

Common theoretical distributions. Sampling distributions. Principles of point estimation. Techniques of estimation. Properties of point estimators. Optimality criteria in estimation. Selected topics from robust inference. Bayesian inference.

Prerequisite: STAT 204 or CD, MATH 120

**STAT 304 Mathematical Statistics II (3-2)4**

Region (interval) estimation. Hypothesis testing. Optimality properties for hypothesis testing. Likelihood ratio tests. Sequential tests.

Prerequisite: STAT 303

**STAT 311 Modern Database Systems** **(3-2)4**

Introduction to database systems. Relational databases. Entity relationship (ER) model. Normalization. Structured Query Language (SQL). Designing databases. Introduction to distributed, parallel and object databases. Big data storage systems. Datawarehouses. Online Analytic Processing (OLAP). Big data analytics and NoSQL. Web data management. Cloud computing.

Prerequisite: STAT 291

**STAT 333 Data Structures and Algorithms** **(3-2)4**

Introduction data structures and algorithms with an object-oriented programming (e.g. C++, Pyhton). Principles of algorithm design. Recursion. Backtracking. Complexity analysis. Big O notation. Arrays, lists, pointers. Stacks, ques, deques. Trees. Hash and symbol tables. Graphs. Searching, sorting, selection, string algorithms. Pattern-matching. Algorithm design. Greedy, divide and conqure algorithms. Dynamic programming. Classification of algorithms.

Prerequisite: STAT 295

**STAT 361 Computational Statistics (3-2)4**

Random number generation. Generating from other distributions. Monte Carlo methods for inferential statistics. Resampling. Data partitioning. Cross-validation. Bootstraping. Jackknifing. Tools for exploratory and graphical data analysis. Nonparametric probability density estimation.

Prerequisite: STAT 291

**STAT 363 Linear Models I (3-2)4**

Simple and Multiple Linear Regression Models. Estimation, interval estimation and test of hypothesis on the parameters of the models. Model adequecy checking. Multicollinearity. Transformation.

Prerequisites: MATH 260, STAT 102 or STAT 156

**STAT 364 Linear Models II (3-2)4**

Simple nonlinear models, Less than full rank models : One-way , Two-way ANOVA models, Multiple comparison tests, Analysis of Covariance (ANCOVA) Models, Introduction to generalized linear models (GLM), Poisson regression, Logistic regression.

Prerequisite: STAT 363

**STAT 365 Survey Sampling Techniques (4-2)5**

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.

Prerequisite: STAT 102 or STAT 156

**STAT 376 Stochastic Processes (4-2)5**

Review of Probability. Theory Markov Chains. Discrete and Continuous time Markov Chains. Poisson Processes. Queuing Processes. Birth and Death Processes. Decision Analysis.

Prerequisite: MATH 260, STAT 204 or STAT 154

**STAT 401 Introduction to Big Data** **(3-2)4**

In general, turning massive data sets into information and information into value. The definition of big data and continues with programming tools to handle massive data sets. Parallelization used for big data. Ended with an environment which provides scalability to store large volumes of data on commodity hardware. Statistical tools used for big data. Visualization used for big data.

Prerequisite: STAT 295 and STAT 311

**STAT 411 Statistical Data Mining** **(3-2)4**

Descriptive and predictive mining. Data preprocessing: cleaning transformation. outlier detection, missing data imputation. Dimension reduction, Principal Component Analysis (PCA). Sampling, oversampling. Exploratory data analysis (EDA). Clustering methods: partitioning, hierarchical, density-based, model-based. Predictive modeling. Regression. Variable selection. Robust and nonlinear regression. Nonparametric regression. Classifiers. Logistic regression. Decision trees. Random Forest. Model evaluation and validation. Real-life applications using recent available software.

Prerequisite: STAT 363 and STAT 291

**STAT 412 Statistical Data Analysis (3-2)4**

Types of data. Graphical and tabular representation of data. Approaches for finding unexpected in data. Exploratory data analyses for large and high-dimensional data. Analysis of categorical data. Elements of robust estimation. Handling missing data. Smoothing methods. Data mining.

Prerequisite: STAT 291 or STAT 292, STAT 363, or CD

**STAT 433 Statistical Machine Learning (3-2)4**

Regression and prediction, optimization, regularization (Ridge regression and LASSO), neural networks and deep learning, classification, kernel methods and support vector machines, decision trees, bagging and random forest, boosting algorithms, principal component regression, unsupervised deep learning. Applying the methods to real data.

Prerequisite: STAT 411

**STAT 440 Artificial Intelligence and Statistics (3-0) 3**

Foundations and history of Artificial Intelligence (AI). Logical programming. Problem solving. Searching. Game playing. Knowledge, reasoning, planning. Theorem proving. Uncertain knowledge and probabilistic reasoning. Hidden Markov Models. Kalman filters. Bayesian networks. Belief networks. Machine learning. Statistical learning. Reinforcement learning. Natural Language Processing (NLP). Pattern recognition. Speech and image processing. Future of AI. Robotics.

Prerequisite: STAT 333

**STAT 444 Advanced Statistical Computing (3-0)3**

Reading raw data files and Statistical Analysis Software (SAS) data sets, and writing the results to SAS data sets; subsetting data; combining multiple SAS files; creating SAS variables and recoding data values; creating listing and summary reports.

Prerequisite: STAT 102 or STAT 156 or CD

**STAT 455 Statistical Business Analytics(3-2) 4**

General introduction to data structures; Statistical data collection and types of business data; Common business problems: customer analytics, segmentation, sales, demand, pricing, fraud, advertisement targeting; Introduction to marketing analytics, definition of marketing terms and definitions, statistical thinking for business problems; Exploratory data analysis and descriptive techniques for business data; Methods for acquiring and manipulating data; Applications of statistical methods for real business cases, machine learning algorithms; Data-centric decision support systems; Statistical applications & discussions.

**STAT 457 Statistical Design of Experiments (3-2)4**

Strategies for experimentation, randomized complete and balanced incomplete block designs, Latin squares. General, two-level and fractional factorials. Blocking and confounding in two-level factorials. Three and mixed level factorial and fractional factorials. Introduction to response surface methodology. Second-order experimental designs. Nonnormal responses. Unbalanced data in factorials. Split-plot designs, Nested designs, Random effect models. Repeated measures.

Prerequisite: STAT 363 or CD.

**STAT 460 Nonparametric Statistics (3-0)3**

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.

Prerequisite: CD.

**STAT 461 System Simulation (3-2)4**

Introduction to discrete-event system simulation and simulation software. Statistical models in simulation. Queuing models. Input data modeling. Variance reduction techniques. Verification and validation of simulation models. Output analysis for a single model. Comparison and evaluation of alternative system design.

Prerequisite: STAT 102 or STAT 156, and STAT 292

**STAT 462 Biostatistics (3-2)4**

Populations and samples. Types of biological data. Data transformations. Survival data analysis. Life tables. Sample size determination in clinical trials. Measures of association. The odds ratio and some properties. Application of generalized linear models and logistic regression to biological data. Analysis of data from matched samples.

Prerequisite: STAT 102 or STAT 156 or CD

**STAT 463 Reliability (3-0)3**

Reliability studies. Statistical failure models. Censoring and truncation and their types. Useful limit theorems in reliability. Inference procedures for lifetime distributions. System reliability. Bayesian methods. Accelerated life testing.

Prerequisite: STAT 304

**STAT 464 Operations Research (2-2)3**

Basic operations research methodology. Basic models such as network flow models, project scheduling, dynamic programming, and production and inventory control. LP and game theory. Two person zero-sum games and mixed strategies.

Prerequisite: MATH 260

**STAT 467 Multivariate Analysis (4-2)5**

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.

Prerequisites: MATH 260, STAT 102 or STAT 156

**STAT 472 Statistical Decision Analysis (3-2)4**

Introduction to decision making and types of decision situations. Bayes theorem and Bayesian decision theory. Prior, posterior and conjugate prior distributions. Loss functions. Empirical Bayesian approach. Utility theory for decision making. Value of information. Sequential decision procedures. Multidecision problems.

Prerequisite: CD

**STAT 477 Statistical Quality Control (2-2)3**

Introduction to concepts of quality and total quality management. Basic principles of teamwork and learning. Probability in Quality Control. Methods and Philosophy of Statistical Process. Control Charts for variables and attributes. Cumulative-Sum and Exponentially Weighted Moving-Average Control Charts. Process Capability Analysis. Introduction to Experimental Design and Factorial Experiments. Taguchi Method, Lot-by-Lot Acceptance Sampling for attributes and by variables.

Prerequisite: STAT 102 or STAT 156 or CD

**STAT 479 Linear Programming (2-2)3**

Introduction to Linear Programming (LP). The simplex method. Transportation, assignment and transshipment problems. Sensitivity testing, duality theory and its applications. Advanced methods in LP and revised simplex algorithm.

Prerequisite: MATH 260

**STAT 480 Application of Statistical Techniques in Socio-Economic Research (3-2)4**

Principals of empirical socio-economic research. Formulation of research problems, determination of research design, application of sampling design. Strategies of field work, collection of data, improving data quality, selecting appropriate statistical methods. Evaluation of test of hypothesis and interpretation of findings. Preparation and presentation of a research proposal and report.

Prerequisite: STAT 250

**STAT 482 Categorical Data Analysis (3-2)4**

Probability distributions and measures of association for count data. Inferences for two-way contingency tables. Generalized linear models, logistic regression and loglinear models. Models with fixed and random effects for categorical data. Model selection and diagnostics when response is categorical. Classification trees.

Prerequisite: STAT 304

**STAT486 Applied Statistics and Econometrics(3-2)4**

The course contains theory of regression analysis for econometrics, and explains applications of regression analysis to a variety of econometric problems. Hypothesis testing and confidence intervals in multiple regression and Diagnostic Checking. Stationary and nonstationary time series. Cointegration and error correction models. Panel data regression models (Fixed and random effects model). Dynamic econometric models: Autoregressive and distributed-lag models. Stochastic regressors and the method of instrumental variables

**STAT 487 Insurance and Actuarial Analysis (3-0)3**

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.

Prerequisite: CD.

**STAT 493 New Horizons in Statistics (3-0)3**

New advances in the field of statistics.

Prerequisite: CD.

**STAT 495 Applications in Statistics (2-2)3**

Applications of different statistical methods in various disciplines such as medicine, science, engineering and social sciences. Presentation of projects involving these applications as group studies.

Prerequisite: STAT 102 or STAT 156 or CD

**STAT 497 Applied Time Series Analysis (3-2)4**

Time series as a stochastic process. Means, covariances, correlations, stationarity. Moving averages and smoothing. Stationary and nonstationary parametric models. Model specification. Estimation and testing. Seasonality. Some forecasting procedures. Elementary spectral domain analysis. Exponential smoothing methods. Unit root tests.

Prerequisite: CD.

**STAT 499 Undergraduate Research (1-4)3**

This course is intended to improve the research capabilities of graduating students. Each student will be given a project and an academic advisor; lectures will be given on research design, data evaluation and report writing. A final report and/or seminar is required at the end of the semester.

Prerequisite: CD.

***CD: Consent of the Department.**

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