Overview

Credit Hours - 3

 Design of Experiments (DOE) is widely used in many scientific and industrial investigations. Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. This course aims to explore joint application of DOE and Machine Learning (ML) methodologies in industrial settings. Topics to be covered include: Experimental factors, response, and strategy of experimentation; response surface designs, guidelines for machine learning experiments; validation and resampling methods; measuring classifier performance; assessing a classification algorithm's performance; comparing two classification algorithms; and comparing multiple algorithms.

Credit Hours - 3

As a specific part of the Big Data Analytics program, this course gives students advanced statistical tools and methods for biological data analysis. With an emphasis on the utilization of stochastic processes, survival analysis, and biostatistics in the context of data analytics, the course equips students with the knowledge and abilities needed to take on challenging issues in biology, health, and related subjects. Students will gain the ability to apply these techniques to huge datasets and 42 make relevant inferences and predictions through case studies and practical exercises. This course aims to enhance students' understanding of biostatistical methods in data analytics. Topics to be covered include: Introduction and Applications of Biostatistics in Data Analytics, Survival Analysis in Data Analytics (Parametric and Non-Parametric Survival estimates, Proportional hazard models, Accelerated failure time model, hypothesis testing) Stochastic Processes in Biostatistics (Statistical processes, Linear growth population models, competing risk etc)

Credit Hours - 3

 This course provides a thorough understanding of statistical experimental design and its applications. It systematically covers various methods for designing experiments and analyzing the large datasets generated from these experiments using JMP Statistical Software. Additionally, the course will explore analytical techniques for managing and interpreting big data produced by experimental research. This course aims to enhance students' skills in graphical and analytical methods for analysing large datasets from statistical experimental designs. It focuses on computing, transforming, and coding the big data generated by factorial designs. Topics to be taught in this course include: Model Specification for Multi-factor Designs, experimental design data extraction and transformation techniques, Estimation, and Orthogal coding of experimental data generated from industries. Some applications will be considered in the field of Insurance, Banking and Finance, Agriculture, Biology, Health, Marketing, Transportation, and Engineering

Credit Hours - 3

This course is intended to give students a thorough understanding of the ideas and methods of planning, monitoring, evaluation, and learning (PMEL) in various industries. The course will cover theoretical frameworks, methodologies, and practical tools utilized in the PMEL cycle, with an emphasis on both qualitative and quantitative methods. Students will learn how to plan, execute, and evaluate development projects, programmes, and policies, with a focus on continuous learning and improvement. This course aims to introduce students to the fundamental principles and theories that govern planning, monitoring, assessment, and learning. Topics to be taught include an introduction to PMEL, project planning and design, monitoring systems and indicators, data management and analysis, evaluation design and methods, qualitative methods in evaluation, quantitative methods in evaluation, data analysis and reporting, learning and adaptive management and ethics and challenges in PMEL.

Credit Hours - 3

This course comprehensively examines the principles and practices involved in project and portfolio management within organizations. Students will learn to plan, execute, and oversee projects effectively, ensuring they align with broader organizational objectives and strategies. By integrating theoretical knowledge with practical case studies, students will acquire the skills to manage complex projects and portfolios in dynamic organizational settings. Students will develop practical expertise in managing individual projects and broader project portfolios through realworld examples, hands-on activities, and simulations. Topics to be covered include: Introduction to Project Management, Project Lifecycle Management, Risk Management, Resource Planning and Allocation, Stakeholder Engagement and Communication, Portfolio Management, and Ethics and Professionalism in Project Management.

Credit Hours - 3

 The seminar aims to provide a comprehensive exploration of advanced topics and contemporary research in the field. The course focuses on critically engaging with the latest theories, tools, and techniques for analyzing and interpreting large, complex datasets. Through a blend of expert-led lectures, student presentations, and collaborative discussions, participants will explore key areas such as data mining, machine learning, predictive analytics, and data visualization. The seminar will also address the ethical challenges and considerations of working with big data, including issues 38 related to data privacy, security, and the broader societal impact of data-driven decision-making. Students will have the opportunity to present their research, receive feedback from peers and faculty, and sharpen their analytical skills. Designed to promote professional growth, the seminar enhances students' competencies in research communication, academic writing, and the critical analysis of the latest advancements in big data analytics.

Credit Hours - 3

This comprehensive course delves into the essential principles and methodologies of forensic accounting, focusing on detecting and preventing fraud. Participants will gain an in-depth understanding of how forensic accountants operate, including their approaches to investigating financial discrepancies and assessing the integrity of financial statements. The course also includes practical exercises and case studies to apply the learned techniques in real-world scenarios. This course aims to offer students a thorough understanding of forensic science and fraud detection. The topics covered in the program encompass a comprehensive range of areas, including fraud detection and prevention, as well as the documentation of fraudulent activities. Students will learn advanced fraud investigation techniques designed to collect, analyze, and evaluate evidence meticulously. These techniques are crucial for interpreting and effectively communicating findings in a clear and actionable manner.

Credit Hours - 3

This course provides essential knowledge about Artificial Intelligence (AI) for students. AI is a rapidly advancing technology that significantly impacts individuals and society. Throughout the course, students will receive a foundational introduction to the key building blocks of AI, including concepts such as machine learning, and neural networks. Students will also explore the current applications of AI and examine critical issues, such as bias. Additionally, students will engage in hands-on projects, applying AI techniques to real-world challenges in diverse fields. This course aims to give students a comprehensive understanding of AI, including its foundational principles, key components, and real-world applications. Topics to be covered include: Introduction to Artificial Intelligence, Perception and Intelligence, Algorithms in AI and Machine Learning Algorithms (supervised and unsupervised algorithms, deeping learning algorithm, reinforcement learning) Ethics and Challenges of AI, Emerging trends in AI.

Credit Hours - 3

This course provides students with a comprehensive introduction to operations research and optimization principles and techniques. It explores mathematical and analytical methods to tackle complex decision-making issues within organizations. The curriculum covers the formulation of optimization problems, the application of algorithmic solutions, and the interpretation of results to support informed decision-making. This course aims to equip students with the skills necessary to address optimization challenges and enhance operational efficiency in diverse environments. Topics to be covered include: Linear Programming, Integer Programming, Dynamic Programming, Network Modelling, Game Theory, Assignment and Transportation problems, Queuing Theory and Project Management.

Credit Hours - 3

 This course covers the fundamental concepts and techniques of big data warehousing and computing. Students will learn how to design, implement, and manage large-scale data warehousing solutions using cutting-edge technologies such as Hadoop, Spark, and NoSQL databases. Through hands-on labs and real-world case studies, students will gain practical experience in building and managing big data systems and analyzing and extracting insights from large datasets. This course aims to equip students with the skills and knowledge to design and implement effective big data solutions in various domains. Topics include: Big data architecture and ecosystem, Data warehousing concepts and design principles, Data ingestion, processing, and storage using Hadoop and Spark, NoSQL databases and data modeling, Big data analytics and visualization, Performance optimization and scalability, Data security and governance.

Credit Hours - 3

 Business Analytics is a vital discipline that employs data analysis to inform and enhance strategic business decisions. This course is designed to provide a comprehensive understanding of Business Analytics, covering quantitative and qualitative techniques. Participants will learn how to effectively leverage data-driven methods and various analytical tools to assess company performance across different metrics. This course aims to equip students with the skills needed 31 to interpret data meaningfully, draw actionable insights, and apply these insights to improve organizational decision-making processes. Topics to be covered include: Data Collection and Management, Descriptive, Predictive and Prescriptive Analytics, Data Visualization, Big Data and Cloud Computing.

Credit Hours - 3

This course provides a comprehensive introduction to the principles and practices of quality control and continuous improvement using statistical methods. It covers key topics such as process variability, control charts, process capability analysis, and acceptance sampling. The course emphasizes applying statistical methods to identify, evaluate, and resolve quality-related issues in production and service environments. Students will learn to apply and interpret statistical quality control techniques to enhance process efficiency and product quality. This course aims to equip students with the knowledge and tools to monitor, control, and improve processes and products across various industries, thereby providing them with versatile skills applicable in a wide range of professional settings. Topics to be covered include: Statistical Process Control, Process Capability Analysis, Design of Control Charts, Acceptance Sampling, Six Sigma and Lean Quality Tools and Continuous Improvement and Total Quality Management (TQM).

Credit Hours - 3

 This course offers an in-depth examination of Management Information Systems (MIS) and their essential role in modern organizations. It addresses the fundamental concepts, technologies, and applications of information systems that support business operations, decision-making, and strategic objectives. The curriculum includes various information systems, such as transaction processing systems, decision support systems, enterprise resource planning (ERP) systems, and customer relationship management (CRM) systems. This course aims to bridge the gap between business and technology by analyzing how information systems help achieve organizational goals, improve business processes, and provide a competitive advantage. Topics to be covered include: Information Systems and Business Strategy, Technology Infrastructure, Types of Information Systems, Systems Development and Project Management and Data Management and Analytic.

Credit Hours - 3

 This course comprehensively explores linear and nonlinear models, emphasizing their important roles in various real-world applications across fields such as economics, health, finance, biology, and engineering. Students will develop a solid understanding of the theoretical foundations underlying these models and learn how to construct and specify effective models tailored to specific datasets and research questions. Students will use a practical, data-driven approach to grasp theoretical concepts and engage in hands-on experience with real data. This structure 27 ensures students can effectively apply these techniques to solve big data-related problems. This course aims to enhance students' practical experience with statistical tools and prepares them to confidently implement and analyze linear and nonlinear models in various fields. Topics to be taught include an introduction to linear models, multiple linear regression, model diagnostics and transformation for linear models, generalized linear models, non-linear regression models, generalized additive models, mixed-effects models, model selection and validation, advanced topics in non-linear modeling, and software applications.

Credit Hours - 3

 Natural language processing (NLP) is a field that addresses both theoretical and practical challenges related to enabling computers to understand and perform various tasks using human languages. By leveraging NLP techniques, users can extract meaningful information from large datasets, automate language-related tasks, and enhance user experiences across various applications, such as chatbots, virtual assistants, and sentiment analysis tools. This course aims to introduce students to various methods of representing human languages, such as English, French, and Chinese, as computational systems and utilizes these representations to create programs that handle text and speech data. Topics to be covered includes computational treatments of words, sounds, sentences, meanings, and conversations. We will explore how probabilities and real-world text data can help us. Students will experience the interaction of different levels in state-of-the-art applications such as translation and information extraction.

Credit Hours - 6

The Capstone Project in Big Data Analytics gives students the chance to apply their knowledge and skills in a hands-on project involving large-scale datasets. This course is designed to bring together the concepts learned throughout the programme, focusing on real-world data challenges and analytics techniques. Students will work on a significant project that involves end-to-end data analysis, including data collection, cleaning, exploration, and advanced analytics. They will use big data tools and technologies to handle and process vast amounts of information, applying 25 methodologies such as machine learning, data mining, and predictive modeling to derive actionable insights. The project will require students to develop and implement analytical solutions to address complex problems, make data-driven decisions, and effectively communicate their findings. Through this capstone experience, students will demonstrate their ability to manage large datasets, employ sophisticated analytics techniques, and professionally present their results, preparing them for advanced roles in the field of big data analytics

Credit Hours - 3

 A key feature of the Big Data Analytics curriculum is Data Management and Data Visualization, which gives students the abilities they need to effectively manage, arrange, and present massive datasets. The capacity to organize, store, and show data in a meaningful way has grown in importance as the amount of data keeps growing. This course aims to ensure that students can transform complex data into valuable insights by covering the essentials of data management, including storage, retrieval, and security, as well as effective strategies for data visualization. Topics to be covered include: Overview of data management in the context of big data (Data retrieval and querying techniques, Data storage solutions: relational databases), Data Quality and Integrity, Data Security and Privacy, Data Visualization Fundamentals, Tools and Technologies for Data Management and Visualization, Advanced Data Visualization Techniques

Credit Hours - 3

This course, a sequel to Computational Statistics in Big Data I, is highly relevant to big data analytics. It introduces students to various computationally intensive statistical techniques and the role of computation as a discovery tool. Through hands-on projects and real-world applications, students will develop the skills to design and produce compelling visual representations that support data-driven decision-making. This course aims to equip students with advanced R programming skills and computational methods for analysing large datasets. Topics include numerical optimization in statistical inference [expectation-maximization (EM) algorithm, Fisher scoring, etc.], random number generation, Monte Carlo methods, randomization methods, jackknife methods, bootstrap methods, tools for identification of structure in data, estimation of functions (orthogonal polynomials, splines, etc.), and graphical methods. Additional topics may vary. Coursework will mainly be computer based practical assignments and projects

Credit Hours - 3

This course thoroughly explains the theoretical foundations and practical techniques needed to create compelling and visually appealing data visualizations. Emphasis will be placed on storytelling through data, which enables students to engage their audiences and communicate insights clearly and effectively. Through hands-on projects and real-world applications, students will develop the 21 skills to design and produce compelling visual representations supporting data-driven decisionmaking. This course aims to enhance programming abilities and equip students with the theoretical and practical tools to create engaging data visualizations, identify patterns and trends within complex big data sets, and effectively convey insights. Topics to be taught include visual representation methods and techniques that improve their understanding of complex data and models, with emphasis placed on the identification of patterns, trends, and differences from data sets across categories, space, and time using R, PowerBI, and Tableau. In addition, the students will be introduced to Basic Statistics, Data Manipulation, Statistical Programming and inference. The course will be mainly laboratory based with practical hands-on exercises and projects.

Credit Hours - 3

 The Applied Multivariate Analysis course provides students with a thorough understanding of essential statistical techniques for analyzing multivariate data. Participants will learn how to apply these techniques in various fields, such as psychology, marketing, finance, and health sciences. The course emphasizes both the practical implementation and interpretation of these methods. The course aims to focus on real-world applications of multivariate analysis, equipping students with the skills needed to analyze complex datasets effectively and make informed decisions based on their findings. Topics to be taught include dimensionality reduction techniques through principal component analysis, explore the relationships between multiple variables using canonical correlation analysis, and develop classification models through discriminant analysis. Furthermore, the course delves into factor analysis, a method used to identify underlying factors that explain the patterns of correlations within a set of observed variables and cluster analysis for segmentation.

Credit Hours - 3

 This course explores the methodologies and tools used in Big Data research. Students will gain practical experience writing proposals and planning research projects, collecting data, and analyzing and interpreting large datasets. The course covers the theoretical foundations of various research methods, the application of these techniques, and the ethical considerations involved in Big Data research. The course aims to equip students with the necessary research methodologies in Big Data and to develop initiatives using various data collection strategies, such as data mining, web scraping, and sensor data collection. Topics to be taught include an introduction to big data and research methodology, ethical considerations in big data research, research design and big data, data collection methods for big data, data processing and management, statistical and computational methods for big data analysis, tools and technologies for big data research, visualization and interpretation of big data, writing and presenting big data research, and case studies in big data research.

Credit Hours - 3

This course delves deeply into the concepts and algorithms used in pattern recognition and regression through the lens of machine learning. Students will learn to evaluate the strengths and weaknesses of different algorithms and understand the underlying principles that guide their design and application. This course covers the methodologies, technologies, and algorithms of statistical pattern recognition from a variety of perspectives. This course aims to equip students with a strong understanding of various unsupervised and supervised learning algorithms at both theoretical and practical levels. Topics to be covered include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis, linear discrimination function, boosting techniques, support vector machine, decision trees and deep learning with neural networks

Credit Hours - 3

 The Big Data Analytics curriculum includes descriptive and inferential statistics. With a focus on practical skills, the course equips students to summarize, display, and comprehend complicated data and make conclusions based on statistical inference. Students will have gained the capacity to apply statistical techniques to practical big data situations by the end of the course, which will improve their ability to derive insights from relevant data. This course aims to equip students with a solid understanding of descriptive and inferential statistics, as well as how to apply them to the analysis of huge datasets. 16 Topics to be covered include: Univariate and Multivariate descriptive statistical summaries in Data Analytics, Point and Interval Estimation in Analytics, Hypothesis testing in Analytics (Parametric and Non-parametric), Introduction to Linear Regression for Analytics.

Credit Hours - 3

Data Mining focuses on extracting valuable insights from large datasets by identifying significant patterns and creating predictive models to forecast future trends. Students will gain hands-on experience in applying techniques in data miming to real-world problems, evaluating predictive model performance, and making informed decisions based on their findings. This course aims to provide students with a thorough understanding of Data Mining principles and methods, emphasizing practical application. Topics to be covered include: Big Data and Statistics with spreadsheets, R or Python, Data Preprocessing, Regression and Classification tasks techniques, Cluster analysis, Association analysis, Anomaly detection and Applications.

Credit Hours - 3

An essential part of the Big Data Analytics program is the course “Probability Applications in Data Analytics.” Students who complete this course should have a thorough understanding of probability theory and how it is used in real-world data analytics scenarios. Both basic and sophisticated probabilistic ideas will be covered, and students will learn how to apply these theories to actual data analytics situations. This course aims to equip students to handle challenging data-driven problems in the field of big data by emphasizing the use of probability to model, analyze, and interpret data. Topics to be covered include: Fundamentals of Probability in Data Analytics, Discrete and Continuous Univariate Distributions, Multivariate Distributions and Their Applications, Application of Moments in Data Analytics, Generating Functions and Their Applications, Central Limit Theorem and Law of Large Numbers, Application of Bayes' Theorem in Data Analytics.

Credit Hours - 3

Overview

This is the fourth and final in the sequel of seminar presentations. Each student in the Department or Programme is expected to attend all scheduled seminars. Each student is expected to make his/her own presentation to discuss the findings of his/her research.  

Credit Hours - 3

Overview

This is the third in the sequel of seminar presentations. Each student in the Department or Programme is expected to attend all scheduled seminars. Each student is expected to make his/her own presentation on the progress made on his/her research.

Credit Hours - 3

Overview

This is the second in the sequel of seminar presentations. Each student in the Department or Programme is expected to attend all scheduled seminars. Each student is expected to make his/her own presentation on the experiential research learning progress made on his/her research.

Credit Hours - 3

Overview

This is the first of four seminars organized in the department. Each student in the Department or Programme is expected to attend all scheduled seminars. Each student is expected to make his/her own presentation on a project proposal.  Topics must relate to statistical issues such as insurance, medicine, mortality and mobility, health outcomes, economics, policy, pension, social phenomena, mathematical finance, statistics, and other related fields with particular reference to the advancement of the statistic profession.

Credit Hours - 45

Overview

A statistics project is undertaken in either an applied area or theoretical development of statistical methods, after presenting a proposal as specified in STAT 710. The final write-up of the project should be submitted by the end of the fourth academic year of study.

Credit Hours - 3

Overview

This course is concerned with the design of experiments when random variation in the measured responses is appreciable compared with the effects to be investigated. Under such conditions, statistical methods are essential for experiments to provide unambiguous answers with a minimum of effort and expense. This is particularly so if the effects of several experimental factors are to be studied. The emphasis of this course will be on designs derived using the theory of optimum experimental design. 

Students will be introduced to many ‘standard’ designs and demonstrate how to generate them in JMP software, the use of JMP in the analysis of data with linear and non-linear models, general discussion of the theory of optimum design, a description of a wide variety of optimality criteria that may be appropriate for designing an experiment, description of algorithms for the construction of D-optimum designs, response surface designs in which there are both qualitative and quantitative factors, blocking of response surface designs with mixture experiments, methods of optimum experimental design for non-linear regression models, including those defined by sets of differential equations, designs for non-linear models require prior information about parameter values (Bayesian procedures), design augmentation and designs for model checking and for discriminating between models will be covered, explore 

the use of compound designs, we will move beyond regression to generalized linear models, appropriate, for example, when the outcome is a count measurement with a binomial distribution.

Credit Hours - 3

Overview 

This course introduces the theory and application of common algorithms used in statistical computing. 

Topics include root finding, optimization, numerical integration, Monte Carlo, Markov chain Monte Carlo, stochastic optimization and bootstrapping. Some specific algorithms discussed include: Newton-Raphson, EM, Metropolis-Hastings algorithm, Gibbs sampling, simulated annealing, Gaussian quadrature, Romberg integration, etc. Also discusses applications of these algorithms to real research problems. 

Credit Hours - 3

Overview 

Students are given a grounding of time series models. 

Topics include: Fourier analysis of data, traditional Time Series models: EWMA, EWR, and ARMA. Time series Stationarity, Autocorrelation, ARIMA models, identification, estimation, diagnostic checking and linear prediction. Non-stationarity and differencing. Properties of autoregressive moving average models and estimation of their parameters, spectral analysis, forecasting. Dynamic linear models (DLMs). Bayesian learning, forecasting, and smoothing. Mathematical structure of DLMs and related models. Intervention, discussion of applications to problems in economics, engineering, physical science, and life science.

Credit Hours - 3

Overview

Students will explore concepts in linear statistical models. 

Topics include: Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Generalized (Log-linear) models for specific non-Gaussian settings.  Model selection and its consequences. Models for multilevel, clustered, repeated measures and longitudinal data for both Gaussian and non-Gaussian outcomes. Random effects, Bayes, empirical Bayes and mini-max estimation for such models. 

Credit Hours - 3

Overview

Students will delve into advanced topics in mathematical statistics. 

Topics include; Modern trends in Decision theory and statistical optimality criteria, sufficiency, mini-max criteria, invariance, estimation and hypothesis testing theory: likelihood ratio test, Neyman-Person Test, large sample theory and information theory would be discussed.

Credit Hours - 3

Overview

This is a graduate course in applied econometrics. 

Topics include multiple linear regression, the bootstrap, quantile regression, instrumental variables, maximum likelihood and probit regression. Ordinary least squares estimation, the bootstrap and jackknife, instrumental variables, solving systems of equations, M-estimation, maximum likelihood, the generalized method of moments, discrete response models, and time series analysis.  

Credit Hours - 3

Overview

This course provides an in-depth discussion to sophisticated tools for probability modeling and data analysis from the Bayesian perspective. 

Some topics include: Hierarchical model building techniques, optimization algorithms and Monte Carlo simulation techniques. These techniques allow one to account for risk in quantitative analysis and decision making. The techniques are used by professionals in such widely disparate fields as finance, project management, energy, manufacturing engineering, research and development, insurance, oil and gas transportation and the environment.

Credit Hours - 3

Overview

This course discusses demographic concepts and measures, collection and evaluation of demographic data, analysis of demographic data and the dynamics of population change. 

Specific topics include Population structure and population change, the components of population change, vital events, rates of incidence and rates of change, crude and specific rates, life expectancy. Fertility measures based on Vital Statistics: Computation, uses and limitations of birth rates. Measures based on Censuses and Surveys: Child-woman ratio, number of children ever born.  Completed family size. Their justification as measures of Fertility. Parity progression ratios. Reproduction concepts and measures. Mortality Measure based on vital statistics.

Credit Hours - 3

Overview

Topics include, report writing techniques, questionnaire design, conducting research in Statistics, ethics in research, reviewing related literature, writing a proposal for grants etc., Budgeting and time-lines construction for a research study, scientific report writing, power point presentation, dissemination of research results etc.

Credit Hours - 3

Overview

This course will cover the design and analysis of sample surveys. Topics include methods and design of field surveys in education, the social sciences, criminal justice research, and other areas. 

It treats methods of eliciting information through household, mail and telephone surveys, methods of assuring privacy, enhancing cooperation rates and related matters. In-depth methods of statistical sampling and sample design are covered. Much of the course is based on contemporary surveys sponsored by the Ghana Statistical Service and other Government agencies.   Simple random sampling, stratified sampling, cluster sampling, multi-stage and multi-phase sampling, rare sampling techniques like capture recapture, successive and re-sampling strategies, adaptive cluster sampling, rank-set sampling, graphics, regression analysis using complex surveys and methods for handling non-response bias will be discussed. 

Credit Hours - 3

Overview

This course provides grounding in stochastic processes, probability and measure theory suitable for statistical work. 

Topics include: Principles of modeling and classification of stochastic processes, Martingales, Markov Chains, Markov processes, Poisson Processes, Brownian motion, stochastic differential equations and diffusion processes, Gauss-Wiener processes. Simulation methods and applications for stochastic processes. Probability spaces, theory of measure and integration, random variables, and limit theorems. Distribution functions, densities, and characteristic functions; convergence of random variables and of their distributions; uniform convergence. Weak laws of large numbers, variants of the central limit theorem, rates of convergence of limit theorems, local limit theorems, stable laws. 

Credit Hours - 3

Overview

It is anticipated that many of the students who go through the Ph.D. program in the Sciences may nurse special interest in teaching and academia. Focusing on group discussions, this course is expected to equip students with the requisite knowledge in overall management of students at the tertiary level. The course will focus on teaching the methodologies and techniques in handling Science-teaching at the undergraduate level. 

Topics such as laboratory supervision and safety, grading issues, special needs students, lecturing and tutoring techniques, examination preparation, teacher/student relationship, tertiary education management, will be discussed through reading, class/group discussions as well as presentations.

Credit Hours - 3

Overview

This course will enable students gain insights on the practice of science as a discipline. The aim of the course is to help students to fit their research to relevant trends and directions for national development.

Major scientific concepts like inductivism are examined as well as the history of science and science itself, an overview of current approaches to research and an understanding of research partnerships, networks and appropriate methods of communicating science depending on the audience. Course content will also cover topics such as the basis for the scientific method; conceptual frameworks; the philosophy of science; ethics in research; pure versus applied science debates; approaches to research; science for development and the merit of broader impact criteria; north south/south south collaboration and partnerships; research networks; communicating science to the policy makers, lay audience and to the media.

Credit Hours - 3

Overview

The course examines historical and contemporary issues in science, relating to the student’s area of specialization and relevance.  Such topics are expected to challenge the students into exploring current and relevant research trends/discoveries in scientific approaches. The course will enable students explore scientific knowledge in modern science and add on to their depth of information in their chosen areas of specialty. It is expected that, the course will complement other courses on the PhD flagship of the various departments in the Sciences and elsewhere. Additionally, it will expose students to current trends of presentations, and foster stronger confidence-building attitude that will enable enhanced international academic competitive spirit.

Credit Hours - 3

Overview 

This course will focus on statistical decision-making in today's dynamic business environment where products and processes are continuously improving. Decisions by modern managers are increasingly data-driven and require lots of statistical skills including, gathering and describing data, designing samples and experiments, drawing statistical inferences and conclusions, evaluating the confidence of conclusions, developing regression models for anticipating future behavior and use of statistical quality control and six sigma to drive process improvement. All of these important capabilities must be utilized by a manager to make informed decisions. This course will be taught on topics, representing each of the skills a business manager must master. Readings will be assigned for each module and homework problems plus case studies will be assigned for analysis and class discussion. The case studies will use actual business examples to aid understanding of the statistical principles and assist translation to actual use. Extensive use will be made of the Minitab computer program for representing and analyzing data. 

 Topics in this course are: Descriptive statistics and numerical measures, regression and the model building process, regression models with categorical dependent variables, applied models with categorical dependent variables. 

Credit Hours - 3

Overview 

The course provides in-depth knowledge and understanding of how statistical techniques might be applied to improve the activities of the local government. Students will analyze data gathered from the local government. The students will also know and apply the techniques of simulation and optimization to solve the problems in some of the aspects of local government administration where statistics may have critical applications such as Finance Department, Housing Valuation, Education, Supplies and Municipal Trading, Engineering, Recruitment of Local Government Staff, Ambulance Services, Public Health, Monitoring and Demographic Information. 

The course includes research design, data collection, data cleaning, data visualization, descriptive statistics, non-parametric methods, statistical inference, confidence intervals, bootstrapping, power analysis, ANOVA, contingency tables, simple and multiple regression and correlation, logistic regression, and survival analysis. 

Credit Hours - 3

Overview 

In recent years, the government, development partners, and development-oriented NonGovernmental Organizations (NGOs) have emphasized Planning, Monitoring, Evaluation and Learning as part of their search for efficiency in the formulation, implementation, and administration of policies programmes, and projects. The course gives students a solid understanding of theoretical and practical elements of monitoring and evaluation. In this course, students will learn about standard principles, methodologies, techniques, and best practices increasingly used in monitoring and evaluating development programmes. 

 Topics include: Fundamentals of project planning and management; Research methods and statistics for impact evaluation; Planning for monitoring and evaluation system; Designing for monitoring and evaluation systems, Cost-benefit analysis: Theory and application; Risk management in monitoring and evaluation; Environmental and Social Impact Assessment. 

Credit Hours - 3

Overview 

Forensic Statistics is relevant to all forensic disciplines and the Bayesian paradigm connects them. The area where it is most pronounced and is most developed is DNA evidence. In this course, attention will be paid to a number of topics that are crucial for criminal investigations. The course concentrates on the nature and significance of biological evidence and the underlying (molecular) biological principles of the scientific methods employed for the analysis and interpretation. The emphasis of this module is on the science behind the forensic examination of biological traces. Forensic DNA has become the dominant technology applied nowadays in the forensic field. 

The course includes: The Bayesian paradigm for computation of the value of evidence will be applied to DNA evidence of complexity ranging from standard profiles to more complicated DNA evidence like instance mixtures, relatedness issues, and database search. Introduction to Forensic Science; Deductive Reasoning; Physical Evidence; The Crime Scene; Serology/Spatter; Hair/Fiber Analysis; Handwriting Analysis and Forgery; Fingerprinting; Time of Death and Entomology; Odontology and Impressions; Anthropology (Bones); Ballistics.

Credit Hours - 3

Overview

Design of Experiments (DOE) is widely used in many scientific and industrial investigations. Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. In this course, a background on the joint application of DOE and Machine Learning (ML) methodologies in industrial settings will be explored.  

Topics to be covered include: Experimental factors, response, and strategy of experimentation; response surface design; randomization, replication, and blocking; guidelines for machine learning experiments; cross-validation and resampling methods; measuring classifier performance; assessing a classification algorithm's performance; comparing two classification algorithms; and comparing multiple algorithms. 

Credit Hours - 3

Overview 

The course provides understanding and quantifying capacity fundamentals of transport systems and mobility to determine the safety, quality, and sustainability of transport infrastructure and optimize these systems' operation. The student will know and apply the techniques of modeling, simulation and optimization to solve problems involved in the design and management of supply chains. The course also provides knowledge for planning, management and operation of transportation systems and mobility, the ability to analyze service levels to users, operating costs, and environmental and social such as mass transit, private vehicle traffic impacts, air transport, sea transport, intermodal transport, and urban mobility. 

 Topics include: Introduction & History; Regional Issues; Legislative Issues; Transit Finance; Customer Satisfaction & Marketing; Transit Operations; Planning; Intelligent Transportation Systems; Human Resources and Labor Relations; Urban Development & Land Use Planning; Freight & Intermodal; Service for Persons with Disabilities; Transport Development Management (TDM) & Commuter Benefits. 

Credit Hours - 3

Overview 

Nonparametric inference is a statistical approach that is different from parametric Statistical Inference as here the underlying distribution is assumed to be unknown. Also, these work when the population is not Normally distributed. This usually involves using infinite-dimensional statistical models that are flexible and adaptable. Nonparametric statistical inference techniques are particularly useful for handling the complex and massive datasets, which are common in today's statistical problems. 

Topics in this course are: Application and interpretation of non-parametric tests including weighted rank tests, normal score and permutation tests, nonparametric inference, density estimation, nonparametric regression, comparison tests, and non-parametric estimation. 

Credit Hours - 3

Overview 

This course covers basic statistical concepts and techniques as they apply to data analysis and presentation in Health practice. Extensive use is made of the statistical software packages R, Statistical, SPSS, and JMP, among others. Students will analyze data gathered from previous statistical studies in Health. Students will review examples from published studies on Health and health-related issues in journals such as the Journal of Medical Internet Research (JMIR) and the Journal of the American Medical Informatics Association (JAMIA). 

The course includes research design, data collection, data cleaning, data visualization, descriptive statistics, probability distributions, hypothesis statistical testing (z-tests, t-tests, and nonparametric methods), statistical inference, confidence intervals, bootstrapping, power analysis, ANOVA, contingency tables, simple and multiple regression and correlation, logistic regression and survival analysis. 

Credit Hours - 3

Overview 

This course focuses on statistical techniques for evaluating time-based processes. It provides an introduction to time series methods and their applications in various contexts, such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, risk management, and sociology. The course covers time series modeling techniques and their use in forecasting, emphasizing linear models and extensions to non-linear models. Topics include: Stochastic process and its main characteristics; Time series as a discrete stochastic process. Stationarity; main characteristics of stochastic processes (means, autocovariance and autocorrelation functions). Stationary stochastic processes. Wald decomposition. Lag operator. Moving average models МА(q). Condition of invertibility. Autoregressive models АR(р). YullWalker equations. Stationarity conditions. Autoregressive-moving average models ARMA (p,q). Coefficient estimation in ARMA (p,q) processes. Box-Jenkins' approach to Coefficients estimation in autoregressive models. Coefficient estimation in ARMA (p) processes. Quality of adjustment of time series models. AIC information criterion. BIC information criterion. “Portmanteau”statistics. Box-Jenkins methodology to the identification of stationary time series models. Forecasting in the framework of Box-Jenkins model Forecasting, trend, and seasonality in BoxJenkins model. Time series with non-stationary variance. Non-stationary mean. ARIMA (p,d,q) models. 

Credit Hours - 3

Overview 

This course aims to develop an understanding of the statistical methods and concepts applicable to environmental science problems. The curriculum entails the use of various statistical software packages such as R, Statistica, SPSS, and JMP. Students will analyze data collected from previous environmental science studies in this course. They will also examine and discuss examples of environmental science studies published in journals and other related issues. 

Topics include: Sampling, capture-recapture methods, regression, risk analysis, time series, spatial statistics, and environmental extremes. Environmental sampling and modelling; quantile regression. Time series analysis: time series modelling and forecasting. Monitoring, modelling and forecasting change: statistical monitoring methods for environmental system, change-point analysis, statistical methods for non-stationary.

Credit Hours - 3

Overview 

In Biostatistical Processes, students will be instilled with various modeling techniques and their applicability to data, mainly in the fields of Biology and Medicine. Students will learn about the fundamental concepts in Biology and Medicine, including risk evaluation of various Biology and Medicine studies. The course aims to teach students how to turn Biology and Medicine related questions into quantitative queries about data and possible solutions. 

Topics to be covered include: Deterministic and Stochastic Models of Population Change, Life Table; its Concept and Structure, Competing risks of illness and death, Survival and Life Expectancy of Populations at Risk, Stochastic illness-death Models: Epidemic Processes, Chain binomial models, Clustering Theory, Effects of Immunization, illness-death Processes, and Applications to the Study of Chronic Diseases. 

Credit Hours - 3

Description  

Seminar will be held once every week in which students will make oral presentations. Each student will present an interesting and existing Applied Statistics inclined paper that he or she will critique and be assessed by Senior Members of the Department. Seminars are compulsory for every student's attendance, whether he/she is presenting or not. Practitioners will be invited to do seminar presentations to students. 

Credit Hours - 3

 Overview

In today's world, businesses, governments, and not-for-profit organizations are faced with complex decisions. They must determine what to produce, where to produce it, how to do it cost-effectively, and how to allocate resources. The factors that affect these decisions are complex, and making a decision through manual methods is often difficult. However, Operations Research (OR) provides the necessary tools to make such decisions rigorously and effectively. 

 Topics to be covered include: Linear programming, simplex method, duality and complementary slackness, sensitivity analysis, primal and dual algorithms, integer linear programming, branch and-bound, heuristic methods, algorithm analysis and complexity, interior point methods, and network analysis. 

Credit Hours - 3

Overview 

Statistical methods play a vital role in quality control and improvement. The focus of this course is on both applications and theory. Controlling and improving quality has become an important business strategy for many organizations, manufacturers, transportation companies, financial services organizations, and healthcare providers. A comprehensive coverage of modern quality control techniques to include the design of statistical process control systems, lean 6 sigma, and process improvement will be of particular interest. 

 Topics to be covered include: Introduction to Quality Improvement and Statistical Process Control; Statistics needed for SPC: graphics, distributions, tests of hypothesis; Philosophy of the Shewhart Chart; Control charts for variables: X bar and R chart; X bar and S chart; Control charts for attributes: counting defects or nonconformities; Advanced control charts: CUSUM, Exponentially weighted moving average (EWMA); System Capability Analysis: process capability analysis, measurement capability analysis, gage R&R; SPC with autocorrelated data, Multivariate SPC; Acceptance Sampling. 

Credit Hours - 3

Overview

This course entails an overview of scientific writing of research output, approaches to conducting research, purposes of conducting research etc.; types of research and their evaluation; an overview of the scientific research process; proposal writing; definitions and functions of a proposal; research design; sampling techniques; designing research instrument: questionnaire and interview/focus group guide; survey methods; pre-testing and piloting; dealing with non-response issues etc. Qualitative research methods and analysis; Field and Interview Skills; Research Ethics; Report Writing: Types of reports; Requirements to compete in readership market; Secrets of a well-written report; Organizing report writing. 

Credit Hours - 3

Overview

Students will be given the background necessary to design and analyze the results from field and laboratory experiments. The class will focus on statistical analysis for agricultural scientists but will be relevant for students in various biological fields as well. Computer-assisted analysis will be presented and will be utilized by students in assignments. 

 Topics to be covered include: Basic terminology in Biometry: Symbolic notations, Degrees of freedom, Mathematical models, Descriptive statistics; Analysis of variance: Introduction; Oneway classification; Two-way classification, one measurement; Two-way classification, repeated measurements; Three-way classification; Nested classification; Fixed and random effects; Fixed model; Random model; Mixed model; Mean separation, partitioning of sums of squares; Linear comparisons; Orthogonal polynomial comparisons; Range tests; Mean separation within interactions. Regression: model; Linear and curvilinear regression; Prediction; Analysis of covariance. The emphasis is on the applications in the Agricultural setting. 

Credit Hours - 3

Overview 

This course provides students with the opportunity to gain knowledge about financial and insurance statistics. It encompasses highly interdisciplinary courses that focus on solving realworld problems through the application of insurance, finance, and statistical tools and methods. Students will develop expertise in valuing, hedging, and managing the combined financial and insurance risks inherent in complex financial products. 

Topics to be taught in this course include: Principles of General Insurance, Theory of Interest and Decremental Rates, Life Contingencies, Social Security and Pension Schemes, Risk Analysis and Associated Statistical Problems, and Estimation of Lifetime Distributions. 

Credit Hours - 3

Overview 

This course is geared toward helping students properly design experiments for their research in fields of statistics and other related fields. It offers comprehensive understanding and application of statistical experimental design, providing a systematic development of the methods and techniques of designing and conducting industrial experiments, and analysis of data generated from experiments using the JMP Statistical software. Analytical techniques of handling data generated from experiments will be explored.   

 Topics to be taught in this course include: Model Specification for Single-Factor and Multi-factor Designs, Main Effects, Specific Effects and Interactions, Estimation, Multiple Comparisons, and Analysis of Covariance. Some applications will be considered in the field of Agriculture, Biology, Health, Marketing, Transportation, and Engineering. 

Credit Hours - 3

Overview

In this course, students will learn about the fundamental concepts of medical statistics, including risk evaluation, various types of medical studies, causality, and censoring. Students will also be introduced to commonly used statistical methodologies. The course aims to teach students how to turn health-related questions into quantitative queries about data and possible solutions. 

 Topics to be taught in this course include: Time series, survival analysis, Bayesian methods, confounding and causation, meta-analyses, screening and diagnostic testing, estimation and study designs, epidemiology and disease control. 

Credit Hours - 3

  Overview

The course aims to acquaint the students with the general mechanics of Probability Theory as well as statistical skills with applications and emphasis on the military. This course will provide Judgments of Probability and Relative Importance in a Military Decision. Students will appreciate how the probability of success is calculated for military simulations; how does one begin to quantify the advantages and disadvantages of other factors when attempting to calculate the odds. Topics to be covered include: Introduction to Safety Assessment of Complex Systems; Functional hazard assessment; Probability calculations; Reliability of systems and equipment; Common cause failures; Particular risks and zonal safety analysis; Fault tree analysis; Failure mode and effect analysis (theory/practice); Evaluation of in-service and equipment and risk management. 

Credit Hours - 3

Overview

This course teaches students about the process of collecting and analyzing demographic data including vital statistics. It covers population issues in Ghana and the world as a whole. Specifically, we will learn about the key measures used to study population structure and the components of population change, including fertility, mortality, and migration. We will also examine how to develop population projections at national and sub-national levels. Additionally, we will explore how understanding demographic patterns and processes can help address some of the major social policy challenges of the 21st century. 

Topics to be covered include: Demographic concepts and Measures, Collection and analysis of Demographic data; Vital statistics, Standardisation, Rates and Proportions, The Dynamics of population change, and Population Projection. 

Credit Hours - 6

Description

Each student will undertake an interesting Applied Statistics project which will typically not exceed three to four months under the supervision of a senior member of the Department. Specifically, a project is undertaken in an applied area after presenting a proposal as specified in ASTA 620. Students would then submit a proposal write-up for approval by their supervisor(s) by the end of the second semester of year one. The final write-up of the project itself should be submitted by the end of the academic year (i.e. year two of study). 

Credit Hours - 3

Overview

The short-run goal of the course is for the student to understand the implications in Econometrics of measure theory, probability theory, mathematical expectation, modes of convergence, limit theorems and asymptotics. This involves applying statistical techniques, primarily regression analysis, to economic events and behavior. The emphasis of this course is on the practical use of basic econometric techniques and their merits for economic analysis. 

 Topics to be covered include: Introduction and Fundamentals of Econometric models; Simple and Multiple Regression Models; Hypothesis Testing; Model Specification; Binary variables; Heteroskedasticity; Basic regression analysis with time series data; Time-series and Serial Correlation. 

Credit Hours - 3

Overview

This course is a sequel to Computational Statistics I, and it is aimed at developing students programming and computational skills in the R free software. 

Topics to be considered include: Simulation of random variables from probability distributions, the visualization of multivariate data, Optimization, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jack-knife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. Selection of examples that illustrate the application of numerical methods using R functions. 

Credit Hours - 3

Overview

This course covers topics needed to develop a broad working knowledge of modern computational statistics. We seek to develop a practical understanding of how and why existing methods work, enabling effective use of modern statistical methods. Achieving these goals requires familiarity with diverse topics in statistical computing, computational statistics, computer science, and numerical analysis. 

 Topics to be considered include: Descriptive and exploratory studies of large datasets, model fitting, and testing are some relevant topics covered in this course. Students will be introduced to statistical packages R and SPSS and shown how to use them to perform statistical analysis. 

Credit Hours - 3

Overview

In this course, we will explore various techniques for choosing a sample for a survey from a larger population. We will discuss the common issues that arise in these sampling methods and focus on the underlying concepts rather than just the mathematical formulas. Additionally, we will pay attention to how to estimate population parameters of interest. 

Topics in this course are: Optimal designs, analysis and comparison of various sampling schemes are some topics in this course. Much of the course will be based on contemporary surveys sponsored by the Ghana Statistical Service and other government agencies. 

Credit Hours - 3

Overview

This graduate course we will explore various stochastic processes and their applications to stochastic systems. It equips students with the relevant statistical techniques to understand random occurrences in everyday life. The course is designed for students who have completed at least one undergraduate semester of probability coursework. 

Topics in this course are: Basic concepts of random variables, random vectors, stochastic processes, basics of randomized algorithms, Monte Carlo simulation and sampling, applications in distributed algorithms, data sketching, and Optimization.                              

Credit Hours - 3

Overview

To analyze complex problems that arise in practice effectively, one needs to understand fundamental statistical principles and have knowledge of suitable modeling techniques. With modern statistical practice, computing using high-level software is also an essential skill. The course aims to equip students with these skills by introducing them to the principles and concepts of linear statistical models. 

Topics in this course are: Least Square Estimation, Interval Estimation and Tests Under General Linear Models (GLM), Polynomial and Multiple Linear Regression, Analysis of Variance (ANOVA), Covariance (ANCOVA), and their practical applications. 

Credit Hours - 3

Overview

Drawing conclusions about populations or scientific truths from data is the process known as statistical inference. Statistical modeling, data-oriented strategies, and explicit use of designs and randomization in analyses are some ways to make inferences. Additionally, various theories, such as frequentists, Bayesian, probabilistic, design-based, etc., make inferences complex due to numerous nuances like missing data, observed and unobserved confounders, and biases. This complexity can leave practitioners needing clarification and guidance about choosing a technique. This course offers a practical approach to learning inference to enable practitioners to get things done. 

Topics in this course are: Optimal Procedures in estimation, Invariance and Unbiasedness, Uniformly Minimum Variance Unbiased Estimation (UMVUE), Minimax and Bayesian Estimation. Statistical tests of hypotheses and applications; Most Powerful and Uniformly Most Powerful Tests, One-Sided and Two-Sided Hypotheses, Unbiased Tests, Likelihood Ratio Tests, Bayesian Tests and their applications in real-life situations. 

Credit Hours - 3

Overview

In this course, students will learn techniques to handle multiple outcome variables effectively and efficiently. It provides knowledge on tackling large dataset problems in statistical sciences, sharpening students' statistical intuition and abstract reasoning, as well as their reasoning from numerical data through community-based and other research. The course aims to enhance students' critical thinking in domains involving judgments based on multivariate data to communicate statistical ideas effectively through written and oral presentations to research partners (community partners). Topics in this course are: The Multivariate Normal and Wishart Distribution, Estimation and Tests of Multivariate Normal Populations, Multivariate Analysis of Variance and Covariance, Classification and Discriminant Analysis, Component and Factor Analysis, Cluster and Corresponding Analysis, and their applications.

Credit Hours - 3

Overview

In the real world, many processes have a deterministic structure with random variation. For example, flipping a coin is best studied by treating the outcome as a random event. Probability originated from games of chance with dice and cards in the 15th and 16th centuries. This course provides master's level students with knowledge of probability and its applications. Topics in this course are the principles of probability, random variables, limiting theorems, generating functions, and their applications.

Credit Hours - 3

Overview

This course is focused on statistical learning, estimation, decision theory. Topics include Specification of a Decision Problem, Optimal Procedures, Invariance and Unbiasedness, Uniformly Minimum Variance Unbiased Estimation (UMVUE), Minimax and Bayesian Estimation/inference, detection theory, likelihood ratio tests, multiple hypothesis testing, generalized likelihood ratio testing, maximum likelihood estimation, Bayesian inference, empirical risk minimization, concentration inequalities.

Credit Hours - 3

Overview

The Tests of Hypotheses is a continuation of STAT 601. In this course, as the name suggests, students will get understanding of statistical tests of hypotheses and applications. The course includes topics such as; The Neyman-Pearson Theory, Most Power and Uniformly Most Powerful Tests, One-Sided and Two-Sided Hypotheses, Unbiased Tests, Likelihood Ratio Tests and Applications, Bayesian Tests and Confidence Intervals.

Credit Hours - 3

Overview

The core component of the study of Statistics is Probability. This course offers students at the masters’ level in-depth knowledge in Probability and its theoretical framework. Topics in this course are; Probability Measure and Probability Space, Axioms and Basic Properties of a Probability Measure, The Probability Calculus, Random Variable and Distribution Function, Expectation and Conditional Expectation, Characteristics Functions, Convergence Concepts and Limit Theorems. Univariate Distribution Families, Binomial and Poisson Processes and Related Distributions, Generating Functions and their Applications, Vector Random Variables, Transformation of Random Vectors, The Gamma and Beta distributions and related Families, Order Statistics and Multivariate Normal Distribution Theory.

Credit Hours - 3

Overview

This course is a build-up on STAT 603, and offers students the skill to explore applications of theories of Probability. Topics include Univariate Distribution Families, Binomial and Poisson Processes and Related Distribution, Generating Functions and their Applications, Vector Random Variables, Transformation of Random Vectors, The Gamma and Beta distributions and related Families, Order Statistics and Multivariate Normal Distribution Theory.

Credit Hours - 3

Overview

Linear statistical models are at the core of the field of statistical modelling since most advanced statistical tools are generalisations of the linear model. This course teaches the fundamentals of linear models required by any statistician, applied or theoretical. We focus on basic theory and applications illustrated by examples of everyday life activities using computer software package R as a tool to improve understanding of linear models.

Some topics to be considered are; Theory of Least Square Estimation, Optimality Property and Distribution Theory, Interval Estimation and Tests Under General Linear Models (GLM), Polynomial and Multiple Linear Regression, and Analysis of Variance (ANOVA) and Covariance (ANCOVA). Formal and informal test for model assumptions and remedial measures. 

Credit Hours - 3

Overview

This course in nonparametric inference is designed to introduce statistics graduate students in their first year (or beyond) to some important topics in nonparametric statistics for performing statistical inference and prediction. This course will focus on theory, methods, computation, and application, providing an introduction with firm foundations available to those working on applications and desiring to utilize best practices.

The course is concerned with developing statistical techniques and models that make weak assumptions.  A standard nonparametric approach estimates a nonlinear function from an infinite-dimensional space rather than a linear model from a finite-dimensional space. This course gives an introduction to the most basic materials from classical nonparametric inference, with a focus on density estimation, quantile regression, confidence sets, nonparametric regression, etc. Rank-type and order-type methods for handling location, dispersion, correlation, distribution and regression problems, Resampling-type procedures such as bootstrap and subsampling, and permutation tests for testing and assessing precision. Smoothing-type and learning techniques such as density estimation, nonparametric regression and classification for estimation and prediction.

Credit Hours - 3

Overview

This course introduces students to the current statistical methods for analyzing discrete response data. Students will learn single sample problems and problems with structured means which can be studied through loglinear and logistic models. Classical maximum likelihood estimators and estimators based on Bayesian, smoothing, shrinkage, and ridge approaches will be described for estimating parameters in structured and unstructured problems. Maximum likelihood estimation theory for loglinear models will be developed through the notion of linear projection. Students will also be introduced to several interesting data sets from a wide variety of fields including political science, medicine, cancer research, ecology, and biology. The majority of the computation will be done using software such as R and SAS, but the emphasis will be on understanding the concepts underlying those procedures and interpreting the results. 

Credit Hours - 3

Overview

In Biostatistical Processes, students are instilled with various modelling techniques and their applicability to data mainly in the fields of Biology and Medicine. Deterministic and Stochastic Models of Population Change, Life Table; its Concept and Structure, Competing risks of illness and death, Survival and Life Expectancy of Populations at Risk are some topics to be treated in this course. Other topics are: Stochastic illness-death Models: Epidemic Processes, Chain-binomial models, Clustering Theory, Effects of Immunization, illness-death Processes and Applications to the Study of Chronic Diseases.

Credit Hours - 3

Overview

The programme is geared toward research. And as research students, the process of certain investigations require sampling, it is therefore imperative for students to acquire necessary knowledge in order to carry out good research. This course focuses on advanced sampling methods such as multiphase, rare sampling techniques like capture recapture, multistage, successive, and re-sampling strategies, adaptive cluster sampling, and rank-set sampling. The sampling methods will have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities.  

Credit Hours - 3

Overview

This course enables students to understand random occurrences in everyday life and the appropriate statistical techniques that are applicable. Topics include: Basic Concepts, Theory and Application of selected Discrete and Continuous Parameter Processes. Arrival processes; definition and properties of a Poisson process; Memoryless property; Poisson process as a limit of shrinking Bernoulli processes; examples using independent Poisson processes; Non-homogeneous Poisson processes; Wiener process / Brownian motion; Ergodic Markov chains; Arbitrary finite-state Markov chains; Expected first-passage times; Renewal-reward processes; time averages; Generalized stopping trials, embedded renewals, and G/G/1; Little’s theorem; M/G/1 queues; Expected number of renewals; ensemble averages; elementary renewal theorem; Renewal-reward processes. 

Credit Hours - 3

Overview

This course offers students the chance to learn about Actuarial Science as a branch of Statistics. It offers students knowledge on: Principles of General Insurance, Theory of Interest and Decremental Rates, Life Contingencies, Social Security and Pension Schemes, Risk Analysis and Associated Statistical Problems.

Credit Hours - 3

Overview

This course inculcates into students the procedures in collecting and analysing demographic data. It also generally provides knowledge on population issues pertaining to Ghana and the world as a whole. Some topics are: Demographic concepts and Measures, Collection and analysis of Demographic data, The Dynamics of population change, Stable Population Theory and Population Projection.

Credit Hours - 3

Overview

In this course, students learn about techniques which allow the presence of more than one outcome variable. This course offers knowledge on overcoming large dataset problems in statistical sciences. Topics are: The Multivariate Normal Distribution, Sampling Theory, Estimation and Tests of Multivariate Normal Populations, Multivariate Analysis of Variance and Covariance, Classification and Discriminant Analysis, Component and Factor Analysis and Canonical Correlations.

Credit Hours - 3

Overview

This course is geared towards helping students to properly design experiments for their research in fields of Statistics and other related fields. 

Topics to be taught in this course include; Fixed, Random and Mixed effects models, Completely Randomized Design (CRD), Randomized Complete Block Design (RCBD), Latin Square Design (LSD), Derivations of expected mean squares, F tests, Symmetric and asymmetric factorial designs, blocking and confounding, Fractional factorial designs, Split-plot designs, Linear Models and Its Applications in Design of Experiments, Incomplete Block Designs (IBD), Balanced Incomplete Block Designs (BIBD), Analysis of Covariance (ANOCOVA), Optimal Designs, Linear design theory, exact and approximate designs, Optimality criteria, optimality conditions, Construction of optimal designs. Some applications will be considered in the field of Agriculture, Marketing, Transportation and Engineering. R/STATA/JMP/SAS statistical software will be used to perform the analysis of experiments.

Credit Hours - 3

Overview

This course entails an overview of Research Methodology, Approaches to conducting research, purposes of conducting research etc.; types of research and their evaluation; an overview of the research process; proposal writing; Definitions and functions of a proposal; research design; sampling techniques; designing research instrument; questionnaire and interview/focus group guide; survey methods; pre-testing and piloting; dealing with non-response issues etc. Qualitative research methods and analysis; Field and Interview Skills; Research Ethics; Report Writing: Types of reports; Requirements to compete in readership market; Secrets of a well-written report; Organizing report writing. Students  will  be introduced  to  the use  of   Latex as  a  type editor.

Credit Hours - 3

Overview

The short run goal of the course is for the student to understand the applications in Econometrics of measure theory, probability theory, mathematical expectation, modes of convergence, limit theorems and asymptotics. This involves applying statistical techniques, primarily regression analysis, to economic events and behavior. The emphasis of this course is on the practical use of basic econometric techniques and its merits for economic analysis. Topics to be covered include: Introduction and Fundamentals of Econometric models; Simple and Multiple Regression Models; Hypothesis Testing; Model Specification; Binary variables; Heteroskedasticity; Basic regression analysis with time series data; Time-series and Serial Correlation.

Credit Hours - 3

Overview

Statistical learning and data mining methods use a variety of computational tools for understanding large, complex datasets. In some cases, the focus is on building models to predict a quantitative or qualitative output based on a collection of inputs. In others, the goal is simply to find relationships and structures from data with no specific output variable. This course takes an applied approach to understanding the methodology, motivation, assumptions, strengths, and weaknesses of the most widely applicable methods in this field.  This course focuses on regression and classification methods. The syllabus includes: model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forest, and boosting; support vector machines and some unsupervised learning methods such as principal components and clustering (k-means and hierarchical) are discussed.

Credit Hours - 3

Overview

This course is aimed at developing students' competence in the use of statistical software tools such as R and Python for advanced programming of statistical methods, algorithms and procedures, mathematical analysis, conducting Monte Carlo simulations, bootstrapping, jackknifing, and performing advanced analysis. 

Credit Hours - 3

Overview

This course emphasizes the application of experimental designs in the Agricultural and Public health setting. Therefore, students will be given the background necessary to design and analyze field or clinical trials and laboratory experiments results. In particular, the class will focus on statistical analysis for agricultural scientists and be relevant for students in various biological fields. The computer-assisted analysis will be presented and will be utilized by students in assignments. The topics include: Basic terminology in Biometry: symbolic notations, nested classification, balanced and unbalanced designs, designs for linear and curvilinear regression, split plot and combined designs. 

Credit Hours - 3

Seminar I is held once every week in which students will make oral presentations. Each student will present an interesting and existing Statistics inclined paper which he or she will critique and will be assessed on by Senior Members of the Department. Seminars are compulsory for every student.

Credit Hours - 3

Overview

In order to infer on information gathered, data must undergo analysis. The study of this course will enhance students’ skills in data analysis, expose them to trending statistical software and allow them to practice. Descriptive and exploratory studies of large datasets, model fitting and testing are some relevant topics pertaining to this course.   

Credit Hours - 3

This is oral presentation delivered by each student in year 2. The components a student is examined on include Research Proposal and Progress Report on thesis (held midway into the second semester). Seminars are held once a week and compulsory for all postgraduate students.