LEVEL 1 SEMESTER I

STA131: Descriptive Statistics

Introduction and Data Collection: Sample vs Population, Types of variables, Levels of measurement, Data and Data Collection methods. Representation and Summary of Data : Discrete and continuous Data, Stem and leaf Diagrams, Frequency distributions ; Tally system & Distribution shape, Graphic presentation ; Bar Chart, Histogram, frequency polygons, Ogives, Pie Chart, Elementary Box and whisker diagrams. Measures of location; Mean, Median, Mode. Measures of dispersion: Standard deviation, variance, quartiles and inter- percentile range. Elementary probability: Introduction, Probability terms and approaches, Union and intersection of events, Addition Rules for probability, Multiplication Rules for probability, Bayes’ theorem. Probability distributions: normal, binomial, poisson. Populations and samples: Sampling distributions – of the mean and of proportion, Confidence Intervals estimate for the mean when the population standard deviation is known, unknown and for proportions. Association and simple regression: Introduction, scatter plots, correlation coefficient, simple linear regression model. Elementary time series and forecasting: Introduction, Time series, Smoothing technique, Seasonal indexes and Forecasting. Practicals: use of calculators to carry out statistical calculations.

STA141: Introduction to Statistics

An Introduction to Fundamental Statistical concepts and methods; Basic definitions, Measurement and Measurement Scales, Variables and Types of Data, Discrete and Continuous Variables, Independent and Dependent Variables; Statistical Notation and Rules: Sample and Population Notation Organisation of Data: Frequency Tables, Group Frequency and Related Frequency Distributions. Presentation of Data: Bar Charts, Pie Charts, Line Graphs, Histograms, Frequency Polygon and Ogive. Measures of Central Tendency and Dispersion: Arithmetic Mean, Geometric Mean, Harmonic Mean, Median, Mode, Range, Quartile Deviation, Inter-quartile Range, Mean Absolute Deviation, Variance, Standard Deviation, Coefficient of Variation. Types of Distribution and Skewness: Symmetrical/Normal Distribution, Positively Skewed Distribution, Negatively Skewed Distribution, Coefficient of Skewness; Counting Techniques: Fundamental Counting Principle, Using Factorial Notation, Permutations and Combinations; Basic Probability Concepts: Events in a Sample Space, Set Theory, Laws of Probability, Probability of an Event, Complementary Event, Mutually Exclusive Events, Union of Events, Independent Events, Marginal and Conditional Probability and Independence, Rule of Total probability and Bayes Theorem; Discrete Random Variables and Probability Distributions: Probability Mass Function, Binomial, Poisson & Hyper geometric Distributions. Continuous Random Variables and Probability Distributions: Probability Density Function, Uniform, and Normal Distribution/Standard Normal Distribution. Applications of the Normal Distribution. For practicals, the use of calculators to carry out statistical calculations.

LEVEL 2 SEMESTER III

STA21: Probability Theory I

Conditional probability and independence: conditional probabilities, Bayes formula, Independence. Random variables: Continuous and discrete, expected value, expectation of a function of a random variable, variance, expected value of sums of random variable, cumulative distribution function. Moment generating functions, probability generating functions, characteristic functions. Transformation of random variables: transforming a single random variable, transforming two or more random variables, linear transformations. The course will make exclusive use of the R software package for modelling, applied analysis, and simulations.

STA213: Mathematics for Statisticians

Linear algebra: system of linear equations and matrices, Linear dependence and independence Linear algebra: system of linear equations and matrices, Linear dependence and independence, determinants, Euclidean vector spaces, Eigen vectors and Eigen values, spectral decomposition, quadratic forms. Calculus: Basic methods of differentiation. Utilitarian treatment of the Intermediate Value Theorem, Rolle’s Theorem, Mean Value theorem. L’Hopital’s rule. Multiple Integration: iterated sums, double and triple integrals by repeated integration. Analysis: Convergence of series: Infinite series and series as sequences; convergence, examples including ∑n⁻ᵃ. Comparison test, absolute convergence theorem (absolutely convergent series are convergent), conditional convergence, rearrangements of series (Riemann series theorem), Cauchy product, ratio test, alternating sign test. Sequences of functions: Pointwise and uniform convergence, continuity of limits of uniformly convergent continuous functions. Use of various statistical and mathematical software to perform the tasks covered in the lectures. Solving matrix/vector algebra will be done manually and using R (statistical software) and YACAS (symbolic equation solver). Calculus problems will be tackled using YACAS.

STA215: General Linear Models

Simple linear regression and correlation: Introduction, Relations between Variables, when to apply Regression Analysis; Scatter Plots, Correlation and Cross tabulations. Simple Linear Regression. Assumptions of Linear Regression. Simple linear regression Solving methods; Simultaneous Equations, Using Matrices and Least Squares Method. Interpreting the Regression Parameters. Properties of fitted regression line. Hypothesis test in Simple Linear Regression; T- Test. Estimation of σ squared. Confidence intervals: C.I on the Slope and intercept and Mean response, Prediction on new observations; Adequacy of the Regression Model; Residual Analysis, Coefficient of Determination and Lack of Fit.; Multiple Regression: General Considerations. Introduction’ Model Assumption. Model fitting using Matrixes method. Model Assumption Diagnostics.

LEVEL 2 SEMESTER IV

STA206: Statistical Data Processing

Taxonomy of Scientific Computing Languages. Operating systems, interpreted and compiled languages, networks and communication. Review of R Software Development. Text Processing. Relational Databases Management Systems. Structured, Self-describing data; XML and parsing. Integrating Software Components. Statistical Reporting. Data Capture and Coding; Internet, Manual coding, Automated coding. Quality Control. Data Editing; the required level of editing, structure of an edit, Clerical coding, Clerical editing, Input editing, Output editing, Validation edit, Missing data edit, Logical edit, Consistency (or reconciliation) edits. Weighting and Estimation. Practicals: Use of CSPro (Survey Software) for data entry and the R/PSPP/SPSS statistical programme for data preparation/cleaning and analysis.

STA212: Probability Theory II

Multivariate probability distributions: joint distribution functions, joint probability density functions, marginal and conditional pdfs, conditional expectation and variance, expectation of a function of two random variables. Multivariate normal distribution (bivariate normal distribution). Sums of random variables. Modes of convergence: convergence in distribution, convergence in probability, weak and strong law of large numbers, central limit theorem. Inequalities: Chebyshev’s inequality, Markov inequality, covariance inequality. The course will make exclusive use of the R software package for modelling, applied analysis, and simulations.

STA232: Statistical Inference I

Introducing inference: Binomial distribution. Random sampling; the sample mean. Distribution of the sample mean. Modes of convergence. Central limit theorem. Introduction to confidence intervals and hypothesis testing: Generating confidence intervals. Basic ideas about hypothesis testing, Type I and Type II errors. Significance tests. P values. Sensible statistical reporting. Inferences for means of Normally distributed populations: Procedures where the variance is known. Procedures where the sample size is large.t tests. Matched pairs problems. Comparing two population means. Comparing population variances. Comparing several population means (Analysis of variance). Methods for categorical data: Fitting hypothesized frequencies to data. Fitting hypothesized probability distributions to data. Chi-square tests of homogeneity. Chi-square tests of independence. Tests based on ranks including the Wilcoxon signed rank test, Mann-Whitney U-test, Kruskal-Wallis test, Friedman test, rank correlation, and Kolmogorov-Smirnov type tests. Fitting distributions using statistical software.

LEVEL 3 SEMESTER V

STA303: Stochastic Processes

Theory and applications, including moment generating functions, probability generating functions, conditional expectation, Poisson process, Markov chains, and topics selected from: branching processes, queuing theory, random walks, and reliability theory. Practicals: Simulation of Stochastic Processes using the R programming language.

STA305: Sampling Theory

Design and implementation of selection and estimation procedures: Estimates versus estimators, population and sampling units. Sampling and estimation concepts, Sampling errors, Nonsampling errors. Emphasis on human populations. Simple random sampling: Introduction to Simple Random Sampling (SRS), Population parameters, Sample statistics, Estimation of the sample mean and population total, SRS with replacement, Confidence interval for mean and total, Sample size determination, Attribute proportion estimation, Confidence intervals for proportion. Unequal Probability Sampling: Hansen-Hurwitz estimation, Horvitz-Thompson estimation, sampling with probabilities proportional to size (PPS). Ratio and Regression Estimation: What is ratio estimation?, Which is better SRS and ratio estimation? Regression estimation, Which is better SRS and regression estimation, Extension to multiple regression, Sample size estimation for ratio and regression. Stratified Sampling: What is stratified random sampling?, Estimation of stratum population mean and stratum population total, Allocation of sampling units, Quota sampling, Ratio estimation with a stratified SRS, Regression estimation with a stratified SRS, Poststratification. Cluster Sampling and Systematic Sampling What is one-stage cluster sampling, what is the difference between this type of cluster sampling and stratified sampling, Equal sized clusters, Systematic sampling, Estimation of mean and total, Confidence intervals of mean and total. Multistage Sampling and two-phase procedures: Optimal allocation of resources; estimation theory; replicated designs; variance estimation; national samples and census materials. Practicals: Use the R statistical Package to analyse data using different types of sampling methods and learn to interpret output. Also do the same with a scientific calculator.

STA321: Statistical Computing

This course introduces students to statistical computing using both R and Python, starting with the fundamentals of each language and their respective environments. Students begin by learning basic syntax, data types, and structures such as vectors, data frames in R, and lists, NumPy arrays, and Pandas DataFrames in Python. They then progress to importing, exporting, and managing real-world datasets, including handling missing values and understanding tidy data principles. The course covers essential data manipulation techniques using tools like dplyr in R and pandas in Python, enabling students to filter, group, and summarize data effectively. Students also engage in exploratory data analysis by calculating summary statistics and creating visualizations such as histograms, boxplots, and scatter plots using ggplot2 in R and matplotlib/seaborn in Python. Emphasis is placed on developing programming skills through writing functions, using control flow statements, and understanding vectorization for efficient computation. By the end of the course, students have a solid foundation in data handling, visualization, and programming basics, preparing them for more advanced statistical modeling and simulations in the next phase of the course.

LEVEL 3 SEMESTER VI

STA302: Statistical Inference II

Estimation: point estimation; unbiasedness; mean squared error; consistency; the score function; Fisher information; Cramer-Rao inequality; efficiency; most efficient estimators; sufficiency; factorization theorem; minimal sufficiency; Rao Blackwell theorem and its use in improving an estimator. Methods of estimation: method of moments; maximum likelihood estimators (MLE), and their asymptotic properties; Bayesian estimation, estimation with algorithms (Newton and E-M algorithms), asymptotic distribution of the score function; confidence intervals based on the MLE and on the score function; nuisance parameters and profile likelihood. Hypothesis testing: Wald test; uniformly most powerful test; the generalised likelihood ratio test; asymptotic form of the generalised likelihood ratio test; multinomial test; Pearson Chi-squared statistic; the Deviance function. Confidence intervals: coverage probability, inversion of a test procedure, pivotal approach. Bayesian inference: introduction, priors, posteriors, conjugate prior, non-informative priors, Jeffrey’s non informative prior, Bayesian estimation, predictive distributions, accuracy of an estimate, loss functions and expected posterior loss, optimal decisions with respect to a loss function, credibility intervals, highest posterior density credible intervals, hypothesis tests, large sample Bayesian approximation. Practicals: Fitting distributions using statistical software.

STA306: Time Series Analysis

Decomposition of time series, seasonal adjustment methods, and index numbers. Forecasting models including causal models, trend models, and smoothing models. Additional topics include autoregressive (AR) forecasting models, moving average (MA) forecasting models, and integrated (ARIMA) forecasting models. A major statistical package is used as a tool to aid calculations required for many of the techniques. The use of the R programming language handling data and date-time data; time series operators; time series graphics and basic forecasting methods; ARIMA. STA314: Generalized Linear Models and Categorical Data Analysis

Sampling Distributions, Categorical Data Analysis: Contingency tables, 2×2 contingency tables,  r × c contingency tables, r × c × l contingency tables, Log-Linear Models for Contingency Tables. Non-linear regression models.  Difference between Linear and Non-Linear Regression Models:  Power Model, Exponential Model, Saturation Growth Model, Harmonic Decline Curve, Polynomial Model, Transformation to a linear model, intrinsically linear and non-linear models, Parameter estimation using the Newton-Gauss method, Steepest Descent Method, Levenberg-Marquardt Method. Practicals: Any major statistical package (R, SPSS, etc) is used as a tool to aid calculations required for many of the techniques.

LEVEL 4 SEMESTER VII

STA407: Design and Analysis of Experiments

One-Way Treatment Structure in a Completely Randomized Design Structure with Homogeneous Errors, One-Way Treatment Structure in a Completely Randomized Design Structure with Heterogeneous Errors, Simultaneous Inference Procedures and Multiple Comparisons. Randomised Block Designs, Latin Square Design, Graeco-Latin Square Designs, Balanced Incomplete Block Designs, Introduction to Factorial Designs & Blocking in Factorial Designs; 2k Factorial Designs, Blocking & Confounding in the 2k Factorial Design; Two-level Fractional Factorial Designs. Practical’s: use of R to carry out statistical calculations.

STA413: Statistical Learning Theory

Probabilistic formulations of prediction problems, Plug-in estimators, empirical risk minimization  Linear threshold functions, perceptron algorithm, Risk bounds, Concentration inequalities , Uniform convergence, Rademacher averages; combinatorial dimensions, Convex surrogate losses for classification, Game-theoretic formulations of prediction problems, Minimax strategies for log loss, linear loss, and quadratic loss, Universal portfolios, Online convex optimization, Neural networks, Stochastic gradient methods, Combinatorial dimensions and Rademacher averages. Hardness results for learning, Efficient learning algorithms, Kernel methods, Reproducing kernel Hilbert spaces, Mercer’s theorem, Convex optimization for kernel methods, Representer theorem, Ensemble methods, AdaBoost, AdaBoost as I-projection, Convergence and consistency of AdaBoost. Practical: Use R statistical package and Python for analysis.

STA417: Statistical Quality Control

Basic concept of process monitoring General theory and review of shewhart control charts for measurements and attributes (p, d = np, C, X and R chart) O.C. and ARL for X control chart. General ideas on economic designing of control chart. Assumptions and costs. Duncan’s model for the economic design of X chart. Cu-sum charts using v masks and decision intervals. Classification of nonconformities and their weighting modification of the c chart for Quality scores and Demerit Classifications Q chart for no. of nonconformities per (u chart) Multivariate Quality control. Hotelling’s T2 and MEWMA chart. Unit – III: Concept of six sigma. Evolution of six sigma Quality approach practical approach to six sigma quality Basic steps involved in application of six sigma Define-measure-Analyze improve and control approach. Unit – IV: Principle of acceptance sampling problem of lot acceptance. Acceptance sampling plans for attributes. Single double and sequential sampling plans and their properties Dodge Roming sampling plans for attributes (AOQL and LTPD). Plans for inspection by variables for one sided and 2 sided specifications MIL std plans continuous sampling plans, Dodge type CSPI, CSPII and CSPIII. Practicals: To use the statistical package, SAS as it contains several tools and charts for analyzing quality control, including X-bar, R, s, P, NP, C, U, EWMA, CUSUM, Individuals, Moving Range, Pareto, and Levey-Jennings charts. Each procedure is easy to use and is validated for accuracy.

STA499: Research Project

In this Research Project, students are required to undertake either a theoretical or an empirical research project.  In addition to regular progress reports, students explain the rationale of their research, its purpose, methodology, assumptions, hypothesis, data types and processing procedures, etc., in seminar type presentations.

LEVEL 4 SEMESTER VIII

STA410: Multivariate Analysis

Multivariate data (Introduction), Matrices, Multivariate normal distribution, Inference about the mean vector. Hotelling’s T^2, Testing for equality of covariance matrices. Principal Components Analysis. Factor analysis and Canonical Correlation Analysis. Classification and discrimination. Cluster analysis. Practicals: Any major statistical package (R and SPSS) is used as a tool to aid calculations required for many of the techniques.

STA418: Survival Analysis

This advanced course provides a comprehensive introduction to the statistical methods used for analyzing time-to-event data, commonly encountered in medical research, reliability engineering, and other applied fields. The course begins with the fundamentals of survival data, focusing on techniques such as the Kaplan–Meier estimator for non-parametric survival function estimation and the log-rank test for comparing survival distributions between groups. Students will then explore the Cox Proportional Hazards (PH) model extensively, including methods for assessing the proportional hazards assumption, the use of stratified Cox models, and extensions that incorporate time-dependent covariates for more flexible modeling. Parametric survival models will be introduced as alternatives to the Cox model, allowing for explicit distributional assumptions about survival times. The course also covers competing risks survival analysis, which addresses scenarios where multiple types of events may preclude the occurrence of the primary event of interest. Practicals: Use R statistical package for analysis and be able to interpret the outputs.

STA420: Monitoring and Evaluation

This course introduces evaluation methods and tools necessary for monitoring and evaluating public health programs. The course will present case studies to describe how evaluation methods can be used to inform public health decision making. The range of topics includes: evaluation planning, survey development and validation techniques, assessment of modern and rapid testing methods; an overview of various methodologies and designs for estimating coverage and changes for a region; methods for evaluating sub-regional performance (i.e. the health districts of a region); and comprehensive monitoring and evaluation approaches that allow for both local and regional assessment. Emphasis will be on the practical aspects of design, analysis and presentation. Students will use a public health systems approach to the evaluation of the programs and discuss as a group the consequences of the decisions they make on the implementation and evaluation of specific public health programmes. Practicals: To use Statistical Package for the Social Sciences (SPSS) for statistical computations.

STA422: Machine and Deep Learning

Basics, Supervised, unsupervised, reinforcement, Bias-variance trade-off, overfitting, underfitting.  Naive Bayes, Bayesian Belief Networks, K-Nearest Neighbors, K means clustering, Classification and Regression trees(CARTs), Support Vector Machines, Gradient boosting Regression, Random Forest (Classification & Regression), Artificial Neural Networks, Deep Neural Networks, Recurrent Neural Networks,  Bayesian neural nets, Deep Boltzmann Machine(DBM), Deep Belief Networks(DBN), Convolutional Neural Networks (CNNs). Practical: Use R statistical package and Python for analysis

STA438: Operations Management

The main goal of the course is the introduction of problem solving using scientific methodologies. Operations research applies scientific methodology to the analysis, of management, function and operation of complex systems, resources, human resources, and/or information. The course is built around non-probabilistic models mainly the linear programming methodology and its variations. Case studies involve among others the project management problem, the routing problem, the maximal flow problem. Topics include the Introduction to Project Management, Time Analysis and Cost Analysis, Decision Analysis, The Value of Perfect and Sample Information, Routing Problems, Queuing Theory, Single server models with poison arrivals and exponential services, Dynamic Programming.

STA499: Research Project

In this Research Project, students are required to undertake either a theoretical or an empirical research project.  In addition to regular progress reports, students explain the rationale of their research, its purpose, methodology, assumptions, hypothesis, data types and processing procedures, etc., in seminar type presentations.

ELECTIVES

STA241: The National Statistical System

The 10 Principles of Official Statistics; The role of statistics in Evidence Based Policy Making; The National Statistical Office and the National Statistics Act; What is the National Statistical System (NSS); The composition of the NSS; What is the National Strategy for the Development of Statistics (NSDS); Essential components of a NSDS.

STA223: Statistics and Society

Fundamental concepts of statistics: Sampling Techniques and Types of Studies; Measurement, Data Types, and Dealing with Errors; Visual Displays of Data; Statistical Graphics; Summary Statistics and Shapes of Distributions; Probability; Normal Distribution; Sampling Distributions; Inference for Population Mean(s); Inference for Population Proportion(s); Inference for Two-way Contingency Tables; Correlation and Regression. Practicals: include the use of a statistical calculator and advanced Microsoft Excel for computing certain calculations.

STA301: Distribution Theory

Brief review of basic distribution theory, joint, marginal and conditional pmfs and pdfs, conditional expectation. Some discrete distributions – Binomial, Poisson, negative binomial, geometric, uniform, multinomial and hyper geometric distribution. Comparison between binomial and hyper geometric distributions. Continuous distributions- Normal, bivariate normal, exponential uniform. Functions of random variables and their distributions. Joint distribution of sample and induced sampling distribution of a statistic. Beta, Gamma, Cauchy, Log-normal, Weibull, Laplace distributions. Chi-square distribution and its properties t and F distributions and their properties. Markov, Holder, Jensen, Liaponov inequalities. Approximating distributions of sample. Compound, truncated and mixture distributions. Distributions of quadratic forms under normality and related distribution theory. Order statistics, their distribution and their properties, joint and marginal distribution of order statistics. Extreme values and their asymptotic distributions. Practicals: Fitting distributions using statistical software.

STA309: Reliability Theory

Reliability concepts and measures : components and systems, coherent systems, reliability of coherent systems, cuts and paths, modular compositions, bounds on system reliability, structural and reliability importance of components. Life distributions, reliability functions, hazard rate, common life distributions, exponential, Gamma, Weibull, Lognormal etc. Estimation of parameters, confidence intervals, LR and MLE tests for these distributions. Notions of ageing: IFR, IFRA, NBU, DMRL and NBUE classes and their duals, loss of memory property of the exponential distribution, closures of these classes under formation of coherent systems, convolutions and mixtures. Univariate shock models and life distributions arising out of them, bivariate shock model, common bivariate exponential distributions and their properties. Reliability estimation based on failure times in variously censored life tests and in tests with replacement of failed items, stress and strength reliability and its estimation. Maintenance and replacement policies, availability of repairable systems, modeling of repairable system by a non-homogeneous Poisson process. Reliability growth models, probability plotting techniques, Hollander- Proschan and Deshpande tests for exponentially, tests for HPP vs. NHPP with repairable systems. Practicals: Fitting distributions using statistical software.

STA312: Risk Theory IPremium calculation principles with emphasis on the utility principle, risk aversion. Partial insurance coverages, optimality of excess loss. Individual model for aggregate claims, safety margin, normal and compound Poisson approximations. Collective risk model, convolution methods and moment generating function (or Laplace transform) methods. Compound and compound mixed distributions. Recursive methods for the calculation of aggregate claims. Stochastic processes, operational time, contagion models. The surplus process, the adjustment coefficient, the probability of ruin, random variables relating to the surplus. Practicals: Aimed to make the theory even more directly applicable by using the software R.

STA322: Research Data Management

Overview of problems encountered in gathering and processing data from biomedical investigations; Data policy, Data management plans, introduction to data management techniques useful in biomedical studies;file formats, documentation and metadata, security, storing and preserving data, sharing data, Licensing. Practicals: Use Microsoft Access as a database package for data creation and storage.

STA323: Actuarial Statistics

This course covers models for insurer’s losses, and applications of Markov chains. Poisson processes, including extensions such as non-homogeneous, compound, and mixed Poisson processes are studied in detail. The compound model is then used to establish the distribution of losses. An extensive section on Markov chains provides the theory to forecast future states of the process, as well as numerous applications of Markov chains to insurance, finance, and genetics. The course is abundantly illustrated by examples from the insurance and finance literature. While most of the students taking the course are future actuaries, other students interested in applications of statistics may discover in class many fascinating applications of stochastic processes and Markov chains. Application of probability theory and statistical methods to some insurance problems: loss distributions, premium principles, risk models, ruin theory, reinsurance, experience rating, run-off triangles, time series models, generalised linear models,  Survival Models and the Life Table, Non-parametric models: Kaplan-Meier, Nelson Aalen and the comparison of survival functions,  Semi-parametric models: The Cox regression model, Parametric models: Markov models, Binomial and Poisson models  and simulation methods (Monte Carlo Simulation). Practicals: Use of Excel, Python, Visual Basic (VB) in insurance and financial computations.

STA412: Risk Theory ΙΙ

Ruin theory, Lundberg inequality, Cramer-Lundberg formula. Renewal equations and Laplace transform methods. Right tail methods, asymptotic results, bounds and approximations. Applications to excess loss coverages, stop loss coverages, and reinsurance. Loss distributions, fitting to empirical data. Generalized models of the surplus process, dynamic solvency models. Practicals: aimed to make the theory even more directly applicable by using the software R.

STA414: Economic and Financial Statistics

Statistical indices: Organization and use of business and enterprise statistics; business register (updating and creation of); CPI, PPI, wholesale price indices. Overview of economic statistics; classifications, concepts in national accounting (mainly valuation; accounting base; institutional sector accounting; supply and use tables); constant price compilation; government accounting; relation between government and national accounts; balance of payments; monetary and financial statistics; quarterly accounts. Social Accounting Matrix: Social Accounting Matrix in analysing socioeconomic dimensions; Government strategies to decrease poverty, unemployment and inequality; Linking government strategies to the Social Accounting Matrix. Tourism Satellite Accounts (TSA): System of Tourism Statistics (STS), The need to measure the economic impacts of tourism; The TSA framework as a Satellite to SNA93 & SNA2008; Review of the basic tourism concepts, definitions and classifications; The demand approach- tourism consumption; The supply approach- relating products and activities – Production accounts; The confrontation between Supply and Demand within a TSA; The complying and compilation with the whole set of TSA. Satellite Health Account (SHA): Classifications; Description of the accounts; Components of health expenditure; Households in the Satellite Health Account; Sources of Information for the Satellite Health Account; Environmental – Economic Accounting: Asset accounts for natural resources; Physical and hybrid flow accounts; Accounts that separately identify all transactions included implicitly in the economic accounts. Practicals: To use Statistical Package for the Social Sciences (SPSS) and Stata for statistical computations.

 STA415: Spatial Statistics

Spatial data structures: geostatistical data, lattices, and point patterns. Stationary and isotropic random fields. Autocorrelated data structures. Semivariogram estimation and spatial prediction for geostatistical data. Mapped and sampled point patterns. Regular, completely random, and clustered point processes. Spatial regression and neighborhood analyses for data on lattices. Practicals: Use R and Winbugs to solve problems and be able to interpret the outputs.

STA428: Selected themes in BiostatisticsClinical agreement (kappa statistics, Bland-Altman method, intra class correlation); diagnostic tests (sensitivity, specificity, predictive values, ROC curves, likelihood ratio); statistical process control (special and common causes of variation, Shewhart, CUSUM and EWMA charts); Clinical trials (equivalence trials, cross-over trials).

Practicals: Use R statistical package for different exercises and be able to interpret the outputs.

STA422:  Bayesian Inference

Bayes Theorem, Model-based Bayesian Inference. Inference using conjugate prior distributions (Poisson rate of count data, success probability of binomial data, mean of normal data with known variance, mean and variance of normal data, normal regression models, other conjugate prior distribution. Nonconjugate analysis. Markov Chain Monte Carlo Algorithims   in  Bayesian Inference: Simulation, Monte  Carlo integration, Markov Chain Monte Carlo methods, The Metropolis-Hastings Algorithm, The Gibbs Sampler, Winbugs. Practicals: Use Winbugs to solve problems and learn to how to interpret the outputs.

STA423: Large Data Analysis

Machine Learning: Introduction and Concepts, Differentiating algorithmic and model based frameworks, Regression : Ordinary Least Squares, Ridge Regression, Lasso Regression, K Nearest Neighbours Regression & Classification, Supervised Learning with Regression and Classification techniques -1, Bias-Variance Dichotomy, Model Validation Approaches, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Regression and Classification Trees, Support Vector Machines, Supervised Learning with Regression and Classification techniques -2, Ensemble Methods: Random Forest, Neural Networks, Deep learning, Unsupervised Learning and Challenges for Big Data Analytics, Clustering, Associative Rule Mining, Challenges for big data analytics, Prescriptive analytics, Creating data for analytics through designed experiments, Creating data for analytics through  Active learning, Creating data for analytics through Reinforcement learning. Current areas that deal with problem of Big Data; techniques from computer science, mathematics, statistics; high performance and parallel computing, matrix techniques, cluster analysis, visualization; variety of applications including Google Page Rank, seismology, Netflix-type problems, weather forecasting; fusion of data with simulation; projects. Practicals: Big data analysis using various computer software.

STA424: Project Management

Introduction to Project Management; Behavioural aspects of Projects; Project life cycles; work breakdown structure; planning and scheduling projects using the Gantt charts. Project Scheduling: Program Evaluation and Review Technique (PERT)/Critical Path Method (CPM); Project scheduling with Deterministic time estimates, the Concept of a critical path, determining the critical path and the computing algorithm; Project scheduling with Probabilistic Time Estimates; The Critical Path; Computing Probabilities of Completing the Project within specified Time periods.  Considering Time-cost Trade-Offs; Project crashing- Linear Programming Model for crashing. Practicals: Using Computer to schedule and manage Projects (Redmine is an open source project management tool, Top features: Gantt charts and calendar for planning); Risk management.