Data and Environmental Engineering

Engineering Smarter Solutions for a Sustainable World

In an era shaped by rapid technological advancement, data is the power behind better decisions, smarter planning, and a healthier environment. At Nile University’s School of Energy and Environmental Engineering, the Data & Environmental Engineering program prepares you to be at the forefront of this transformative field, where data intelligence meets environmental stewardship.

Modern organizations and government bodies require professionals who can collect, analyze, and interpret complex data to inform environmental impact assessments, design sustainable systems, and support regulatory compliance. This program combines core engineering principles with advanced data analysis to equip you with the technical expertise employers demand.

Prof. Ayat Osama Ghallab

Program Director of Data and Environmental Engineering

Prof. Ayat Ossama Ghallab is a full-time Professor and the Program Director of the Data and Environmental Engineering Program at Nile University. She is an active member of the university’s Smart Engineering Systems Research Center (SESC).
Prior to joining Nile University, Prof. Ghallab served as a Professor in the Chemical Engineering Department and as the Academic Coordinator for the Petroleum and Petrochemical Engineering Program at Cairo University’s Faculty of Engineering. She has also held part-time professorships at several distinguished Egyptian institutions, including the Arab Academy for Science, Technology & Maritime Transport, the Higher Technological Institute, and the Egyptian Military Academy (Air Defense College).
With a strong commitment to interdisciplinary education, Prof. Ghallab teaches undergraduate courses spanning chemical engineering fundamentals, process modeling, heat transfer, process design, and environmental science. She has successfully supervised over 20 master’s and doctoral theses and has authored more than 20 publications. Her professional expertise is further demonstrated by her contributions to 28 Environmental Impact Assessment reports as a researcher at Cairo University’s Center of Environmental Research & Studies, as well as her service as a peer reviewer for 7 international journal papers.
Prof. Ghallab maintains an active international presence, having participated in conferences in Bulgaria, Poland, and China, and was recently selected for the Erasmus+ Mobility program at the University of Oradea, Romania (2025).

Through a balanced blend of theory and hands-on application, you’ll develop skills in:

Data collection and engineering for environmental systems and impact assessment
Advanced analytics, data management, and environmental decision support
Integration of engineering tools and data platforms to solve real-world challenges
Environmental sustainability principles aligned with local and global standards

You’ll gain the ability to design and build engineering systems that support environmental planning, monitoring, and evaluation — empowering you to make data-driven contributions to industry, consulting, research, and public policy.

Data and environmental engineering is one of the fastest-growing areas in the job market. As digital transformation and artificial intelligence reshape industries, industries increasingly seek specialists who can turn data into actionable insight — especially in environmental contexts.

Graduates leave the program ready to support:

Environmental impact assessments and regulatory compliance
Big data analysis for engineering and environmental decision-making
Digital transformation initiatives within industry, consulting, or public sector
Smart solutions for water, air, waste, and climate systems planning

With a strong foundation in both data engineering technologies and environmental processes, you’ll be well-positioned for diverse roles, including:

Environmental Data Analyst
Data Engineer for Sustainability Systems
Environmental Consultant
Impact Assessment Specialist
Digital Transformation Analyst
Research and Development Technician
Government and NGO Environmental Planner

This program opens doors across sectors such as industrial operations, environmental consulting, research institutions, ministries, and international organizations — wherever data and sustainability converge.

Graduates of the Data & Environmental Engineering program will be:

Skilled in applying analytical thinking and technical engineering methods
Proficient in data tools, environmental modeling, and sustainability problem-solving
Prepared for leadership roles requiring innovation and ethical practice
Equipped for lifelong learning and advancement in a rapidly evolving field

Curriculum

Program Courses

Introduction to computers and information skills to enable efficient use of computers and to prepare student for lifelong learning in information technology, Basic understanding of operating-systems, hardware, networks, software applications, Basic understanding of system development, and social implications of information technology, Introduction to standard office software applications for information formatting and web-page design, and database management through a series of assignments and projects

Introduction to the process of writing through reading, integrated approach to teaching the skills of thinking, reading and writing that first-year students need in order to succeed in their academic work Independent thinking by showing them how to organize information, interpret different perspectives, solve challenging problems, analyze complex issues and communicate ideas clearly by drawing heavily on exciting topics to stimulate the interests, thinking and writing critically.

Building more formal, academic discourse, focusing on writing meaningful essays and developing their skills, through observation, analysis, critical reading and thinking, arts of style, organization and thoughtful content as well as sharpening the skills of logical reasoning and problem analysis through the development of reading comprehension strategies for informative and expository texts with focus on summarizing, analyzing and synthesizing textual material, examining selected readings and stylistic strategies as a means of developing effective argument-based writing.

Development and use of research, critical analysis, organization and revision within the writing process, methods of documentation in library and online research, using quotations and source citations for professional papers using inductive and deductive reasoning, developing the skills of scientific argumentation, persuasion, evaluation and criticism needed for a research paper, one minor and one major project during the term as well as a series of short response essays.

The ability to communicate effectively using the written and spoken, learn and practice the skills of interpersonal and professional communication, improving skills in oratory and public presentations by introducing writing professional documents, including technical/scientific reports, business letters, faxes, resumes, etc. Report writing process by practicing techniques of writing a well-structured report as well as delivering a well-structured presentation in a formal setting, inter-personal and professional communication with special consideration given to the cultural and linguistic aspects, human perceptions, interpersonal dynamics, the art of listening and convincing, verbal and visual symbols.

Overview of the main engineering disciplines thus helping them make the right choice regarding the future careers, history and the concepts of the main engineering disciplines, and mini projects those are relevant to these engineering disciplines.

Introduction to the history and context of cities and urbanization, building types used in world architecture spanning history of engineering, art and science of monuments from pre-history up until the Greco-Roman times, knowledge of significant structures and buildings in this period, built environment and the socio-cultural dimensions that shape cities, the roles of architecture, urban design and urban planning in shaping the city, terms that help read the city, tools applied to shape the city, challenges and opportunities of cities, urban metrics, trends, and issues associated to urbanization.

Energy Sources and Technologies: An overview of different energy sources and technologies, including fossil fuels, renewable energy sources (such as solar, wind, hydroelectric, and geothermal), nuclear energy, and emerging technologies. Introduction to Environmental Engineering: An overview of the role of environmental engineering in addressing environmental challenges, including pollution control, water and wastewater treatment, air quality management, solid waste management, and environmental remediation.

Introduction to the entrepreneurial activity, Survival and growth of small and medium enterprises (SMEs), the managerial aspects of SMEs compared to large firms, the benefits and drawbacks of being an entrepreneur, developmental structures and designs, focus development, management during fast growth periods, lack of resources and financing, development of sustainable intangible resources (legitimacy, status, reputation, etc.), ownership, governance and management, succession planning, generational gaps, delegation and control and the role of non-family members, and challenges faced by family businesses.

This course is an elective course in one of these tracks. - (HUM111) In Critical Thinking and Ethics, developing a broad understanding of logical and critical thinking method; linking between science and society in daily lives, such as studying the characteristics of the method of scientific inquiry and to give an overview of the role of scientific communities, applicable method for helping to develop a reliable persuasive method, ethical issues and problems that arise in professional and business environments, such as integrity, civic responsibility, ethical conduct and misconduct, employee and corporate rights and responsibilities, and on issues concerning social and economic justice in a global economy. - (HUM112) A course in any of the fields of Literature, Philosophy, Art, Music, or Sports. - (HUM113) A course in any of the fields of sociology, economics, education, history, anthropology, psychology, or geography.

Introduction to modern programming design techniques using C and C++. A study of basic programming constructs, techniques, and fundamental control structures, Object-Oriented and modular programming, data types, functions, arrays, and pointers, problem analysis, decomposition, and modern programming paradigms and methodologies.

Geometric construction, sketching, orthographic projection, sectional drawing and geometric dimensioning, technical mechanical, architectural drawing and steel elements for structures, manual and computer-aided means of communication (manual drawing and AutoCAD) assemblies.

Concepts and theorems of differential calculus, elementary functions with emphasis on trigonometric, hyperbolic functions and their inverses, techniques of differentiation, limits, L'Hôpital's rule and indeterminate forms, the basic concepts of plane analytic geometry particularly conic sections, introduction to three-dimensional coordinate geometry and different coordinate systems, and introduction to functions of several variables and their partial derivatives, sequences, infinite series, convergence and divergence test for series, and power series expansion

Techniques of integration, definite and indefinite integrals, improper integrals, multiple integrals, applications of integration (finding the length of a plane curve, planar areas, areas of surfaces of revolution and volumes of revolution). Functions of complex variables and their derivatives, Complex integrals, Cauchy integral theorems.

Definition of statistical experiments, sample space, events, operations on events, combinatorial analysis (permutations, combinations and counting rules), definition of the probability, probability axioms, conditional probability, independence of events and Bayes theorem, definition of the random variable, discrete and continuous random variables, discrete probability distributions (binomial distribution, Poisson distribution, geometric distribution, hypergeometric distribution), continuous probability distributions (uniform distribution, normal distribution and exponential distribution), introduction and overview of statistics, data description using measures of central tendency, measures of dispersion, measures of position, sampling distribution, central limit theorem, interval estimation, confidence interval, and hypothesis testing.

Finding the solutions of linear systems of equations by different methods, the concepts of matrices, vector spaces, inner product spaces, linear transformation, and orthogonality, the matrix function, the diagonalization process, eigenvalue problem, and modelling of different engineering applications related to machine learning.

Finding the solutions of ordinary differential equations (ODEs) by different ways using analytical techniques for linear first order and higher order ODEs and their simulations, time-frequency transformation, Laplace transform, Fourier transform, modelling of different dynamical engineering applications.

Fundamentals of mechanics, concept of equilibrium, free body diagrams, equations of equilibrium, different types of supports, reactions, forces in space, equivalent force-couple systems, 3D equilibrium of rigid bodies, Centroids, Second moment of area (moment of inertia), Analysis of simple trusses, method of joints, method of sectioning, kinematics of particles, planar rectilinear, curvilinear motion in Cartesians-coordinates, relative motions, Tangential-normal, radial-transverse components of acceleration, Kinetics of particles, Newton's second law of motion, Angular momentum, Principle of work and kinetic energy, conservation of energy .

Basic Dimensions, dimensional analysis, elasticity, tensile stress, shear stress, bulk stress, fluid Statics, hydrostatic pressure, Pascal's principle, Archimedes' principle, dynamics of ideal fluids, continuity equation, Bernoulli's equation, viscosity, oscillatory motion, simple harmonic motion, spring-mass system, planar, cylindrical, and spherical waves, wave propagation, sound waves, the nature of heat, the laws of thermodynamics, thermal expansion, kinetic theory of gases, ideal gases, molar specific heat, degrees of freedom, reversible and irreversible processes, thermal cycles, thermal engines and heat pumps

Electrostatics, electric charge, Coulomb's law, insulators and conductors, electrostatic field, electric flux, Gauss' law, electric potential, electrostatic potential energy, dielectrics and capacitances, electromotive force, electric current, resistance, Ohm's law, electric power, direct current circuits, Kirchhoff's laws, mesh analysis, magnetism, magnetic forces, sources of magnetic fields, Bio- Savart law, Ampere's law, induction, Faraday's law, Lenz's law, optics, wave nature of light, reflection and refraction, interference, diffraction.

Wastewater treatment, Solid waste management, Chemical Reactions, Stoichiometry, Chemical reaction balancing, determining limiting reactant and percentage excess of the other reactants, Degree of conversion, finding the number of moles in the product stream, Combustion Reactions, Thermochemistry, Hess law, Chemical kinetics, Electrochemistry, Nernst equation, Energy Balance

Designing and verifying modern digital systems, Boolean algebra, Digital number systems and computer arithmetic, combinational and sequential logic design and optimization, Register-transfer design, Basic processor organization, Instruction set issues, Levels of abstraction and hardware description language methods, Computer-aided digital design software.

Electrical Circuits variables and elements, Simple resistive circuits, Analysis of electrical circuits, ohm's law, Kirchhoff's laws, series parallel equivalent, star delta transformation, source transformation, Network theorems: Mesh current method, Nodal voltage method, Thevenin's equivalent, Norton's equivalent, superposition principles. Sinusoidal steady state analysis, Phasor diagram representation, Applications of network theorems on alternating current circuits, Electric power in alternating current circuits, complex power calculations, power factor, circuits with nonlinear resistances, Transients in electrical circuits.

Propositional Logic (logical operators, truth table, propositional equivalences, Translation), Predicate logic (quantification, nested quantifiers, equivalences, translation Inference rules), proofs (direct, by contraposition, by contradiction by cases), Set theory (set builder notation, subset, Cartesian product, power set, set identities), Functions (types, inverse, composition, ceil and floor functions), Sequence and Summation, Matrices (Introduction, matrix arithmetic, matrix multiplication, transpose, powers of matrices, zero one matrices), Integers (integers, division, division algorithm, modular arithmetic, primes, GCD, LCM), Mathematical Induction, Relations (properties, combining relations, representation, equivalence relation)

Formal techniques for the design and analysis of algorithms, Mathematical theory and practical considerations of efficiency, Fundamental concepts of data structures and algorithms for representing and processing information; Linked lists, stacks, queues, directed graphs and trees, Analysis of algorithms, sorting, searching and hashing techniques, Mathematics foundation, Divided-and-conquer, Dynamic programming, Greedy method, NP-completeness complexity, Approximation algorithms, randomized algorithms, and backtracking algorithms, Advanced data structures including: Binary trees, Heaps, Priority Queues, and Huffman Coding Trees.

Basic hardware structure of a programmable computer and the basic laws underlying performance evaluation, Designing the control and data path hardware for a processor, machine instructions execute simultaneously through pipelining and simple superscalar execution, Designing fast memory and storage systems, Design and simulation of a register transfer (RT) implementations in Verilog.

Principles of Data Science, Basic tool and techniques of data handling, exploratory data analysis, data visualization, data-based inference, and data-focused communication, Fundamentals of Artificial Intelligence -- State space representation, uninformed search, and reinforcement learning, Data-driven decisions in their field of study, Applications related to Big Data, Neural Networks and Deep Learning, Computational techniques using Python.

Various building blocks and principles behind embedded real- time systems, Integrated hardware and software aspects of embedded processor architectures, along, Real-time, resource/device and memory management, Interaction with devices (buses, memory architectures, memory management, device drivers), Concurrency (software and hardware interrupts, timers), Real-time principles (multi-tasking, scheduling, synchronization), Implementation trade-oﬀs, Profiling and code optimization (for performance and memory), Embedded software (exception handling, loading, mode-switching, programming embedded systems), Skills in the design/implementation/debugging of core embedded real-time functionality.

Data models and database design, Modeling the real world: structures, constraints, and operations, the entity relationship to data modeling (including network hierarchical and object- oriented), Relational model, Tables Normalization, Structured Query Language, Use of existing database systems for the implementation of information systems.

Big Data framework using Hadoop and Spark, principles of HDFS, YARN, MapReduce, HBase, A distributed column-oriented database, Real-time data processing using Spark, Understanding parallel processing in Spark, and using Spark RDD optimization techniques and SparkML, Using Pig and Hive to process and analyze large datasets stored in the HDFS and to use Sqoop and Flume for data ingestion.

Fundamentals of artificial intelligence (AI), Statistics, Uncertainty, Bayes networks, Problem-solving, Knowledge, Reasoning, Planning, Natural language, Understanding, robotics, and robot motion planning, Programming using AI language tools.

Introduction to machine learning, Statistical pattern recognition, Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines), Unsupervised learning (clustering, dimensionality reduction, kernel methods), Learning theory (bias/variance trade-offs, practical advice), Reinforcement learning and adaptive control, Recent machine learning applications, such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Introduction to data mining methods and applications, Basic concepts and tools for data mining, including data sources, data preprocessing, data cleaning tools, data warehouse, association, classification, and methods, Mainstream algorithms for data mining, Statistical modeling, and Popular tools for mining structured data, unstructured data, and specific data types such as time-series, social networks, multimedia, and Web data.

Comprehensive understanding of ethical considerations and responsibilities in the practice of engineering, ethical principles, dilemmas, and decision-making frameworks relevant to engineers and their role in society, Ethical theories and frameworks, Professional codes of ethics, Ethical considerations in engineering design, Ethical responsibilities in research and innovation, Professional responsibility and social justice.

Understanding of the legal frameworks, regulations, and policies governing environmental engineering practice, Intersection of engineering, law, and environmental policy, focusing on promoting sustainability, protecting public health, and ensuring compliance with environmental regulations, Introduction to environmental law and policy, Clean Air Act and air quality regulations, Clean Water Act and water quality regulations, Hazardous waste management and remediation, Environmental impact assessment, Renewable energy policies and incentives, Climate change law and adaptation strategies, Case studies and regulatory analysis

Principles, methodologies, and techniques to effectively plan, execute, and manage engineering projects, Real-world applications in engineering practice, Introduction to project management, Project initiation and planning, Project allocation, implementation and control, Scheduling and estimating, Developing approval process, including testing for alternatives; Project communication and teamwork, Project risk management, Resources selection, Post project evaluation, Project procurement and contracts, Project closure and lessons learned, Project management tools and software, Project Management Book of Knowledge (PMBOK), PERT, CPM.

Fundamentals of fluid statics and dynamics, Conservation of mass, momentum, and energy in fixed and moving control volumes, Steady and unsteady Bernoulli's equation, Differential analysis of fluid flow, Dimensional analysis, and similitude, Laminar and turbulent flow, Boundary layers, Modeling different dynamic engineering applications based on the mathematical background.

Basic principles of the hydraulics of open channel flows and solving open channels flow problems, Calculating changes in water surface profile, fundamentals of designing water resources related projects, Risk of hydrological events and magnitude of rainfall and runoff from a catchment, Generation, synthesis, and prediction of runoff hydrographs using unit hydrograph.

Essential concepts of the physical and chemical properties of soils, Physical processes in Earth's major systems: the atmosphere, hydrosphere, lithosphere, and biosphere. Transport of materials---such as pollutants, nutrients, and sediments---within these natural systems, Fundamentals of geology, hydrology, atmospheric dynamics, and environmental interactions

Material and Energy balance analyses, Principles of mass transfer and pollutant transport in the natural and engineered environmental systems, Environmental process modelling for the various environmental systems, Analyzing fate and transport of pollutants in natural environmental and engineered systems, Analyzing and solving complex problems.

Fundamentals of wastewater analysis/characterization, Preliminary design and operation of unit processes in wastewater treatment, Physical, chemical, and biological principles in wastewater assessment and treatment, with a particular emphasis on water recycling and resources recovery, Characterization of surface water and groundwater, Selection and design of conventional and advanced unit processes for drinking water treatment, Sludge treatment and disposal

Practical applications and an understanding of theories which are related to typical topics in the areas of environmental engineering, measuring solutions pH, turbidity, acidity and alkalinity, measuring concentrations of different ions in water and wastewater for water quality analysis, determining some soil properties.

The aim of this course is to provide students with practical applications and an understanding of theories, which are related to typical topics in the areas of environmental engineering including Water hardness, Dissolved oxygen, Solids in sewage, Optimum Alum dosage, Monitoring PM and Measuring sound levels

Sources & effects of air pollutants on health and environment, air pollution control legislations, Meteorology to estimate air pollutants emission and evaluate air quality, Global environmental issues, Fundamental theories and practices for major air pollutant control devices

General concepts of microbiology, microbial ecology, and their environmental applications, Cellular organization, metabolism, function, and biological interaction of key environmental microorganisms, Biology and microbiology of the natural environment and environmental engineering processes, Biological processes for breakdown, treatment, and management of various types of waste

Human-environment interactions and environmental repercussions, human activities, Qualitative, quantitative and transdisciplinary analysis of environmental issues, air quality, water quality, waste, energy and resource management, Environmental risk assessment (ERA) and systems management strategies, Incorporating data science techniques to enhance analytical capabilities, Environmental assessment tools and methods including life cycle assessment (LCA), material flow analysis (MFA), strategic environmental assessment, cost-benefit analysis (CBA), Application of these concepts and methods/tools for the various environmental systems.

Solid and hazardous waste engineering principles and management issues, Municipal solid waste properties, generation, collection, management, recycling, treatment and disposal, Hazardous waste properties, generation, management, containment, treatment.

Principles of an environmental impact assessment (EIA), Causes of impacts and the use of a formal EIA, EIA concepts and methodologies relating to social, engineering, and economic issues, Monitoring and developing follow-up procedures as well as options for designing these procedures, Simplified version of an Environmental Impact Statement.

Fundamental operating system principles, Overview of the components of an operating system, mutual exclusion and synchronization, Implementation of processes, Scheduling algorithms, Memory management and file systems.

Introduction to parallel computing for scientists and engineers, Shared memory parallel architectures and programming, Distributed memory, Message-passing data-parallel architectures, and programming.

Fundamental concepts of data networks, Engineering principles of computer networks and integrated digital networks, Data networks overview, OSI layers, Data link protocol, Flow control, Congestion control, Routing, Local area networks (Ethernet, Token Ring and FDDI), Transport layer, Introduction to high-speed networks and Performance evaluation techniques.

Introduction to cybersecurity, hacking, social networks, privacy, cryptography, Legal aspects, Social implications, Password management, Digital forensics, Computer networking, Wireless security, and ethical issues, Protecting from various cybersecurity threats.

Fundamental principles and techniques in deep and reinforcement learning, Convolutional neural networks, Recurrent and recursive neural networks, Backpropagation algorithms, Regularization and optimization techniques for training such networks, Dynamic programming, Monte Carlo, and temporal difference, Function approximation reinforcement learning algorithms, Applications of deep and reinforcement learning, Active research topics in deep and reinforcement learning areas.

Cloud computing concepts from platforms and services to programming and infrastructure, Internet of Things (IoT) systems, Architecture, Technologies on each layer, Cloud converging technologies, Cloud frameworks, Service models, Virtualizations and different types of hypervisors, IoT components and architecture, Sensor and sensing technology, IoT platforms, Application of cloud and IoT.

Image sampling and quantization, color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, multi-resolution image processing, image compression, noise reduction and image restoration, general principles of image processing and practical projects.

Basics of remote sensing, characteristics of remote sensors, remote sensing applications in academic disciplines and professional industries, image acquisition and data collection in the electromagnetic spectrum and data set manipulations.

Reflects current trends and emerging technologies in data engineering

Collect, analyze, and manage water-related data, Key concepts: automated data collection, databases, data management, and web-based data access, Engaging in hands-on projects, Tackle real-world hydrology or water resource problems, Designing data models, Automating data processes.

Fundamentals of water quality parameters and their significance, Mathematical modeling techniques, Integration of machine learning algorithms in predictive modeling and optimization, Case studies on pollutant transport and nutrient cycling, Use of simulation software and tools for water quality assessment

Principles of Geographic Information Systems, fundamentals of GIS; introduction to modern spatial data and structures; input of Geospatial data; functions of geographic information systems; spatial Analysis; coordinate transformation and map projection; interpolation techniques; relations between GIS and remote sensing; and applications of geographic information systems to a variety of environmental and geologic issues.

Principles of air quality and its impact on health and environment, Machine learning techniques to predict air quality levels, Analyzing and interpreting air quality data, Practical solutions for air quality monitoring and management

Reflects current trends and emerging technologies in environmental engineering

Major sustainability challenges from the perspectives of society, economy, and the environment, Stimulation of critical thinking, curiosity, and the ability to structure and synthesis knowledge through the lens of sustainability, Sustainability issues from different perspectives and at different scales, Concrete examples such as palm oil production, waste management, pollution & human consumption, and climate change, More sustainable world through choices and actions, as individuals, societies, nations, and as a global community.

Introduction to sustainability in various engineering disciplines, Overview of concepts of sustainability, principles, concepts, and applications of sustainable engineering, Interdisciplinary nature of sustainable engineering, Life cycle assessment (LCA), Sustainable Development Goals (SDGs), Design for environment (DFE) principles, Ethical, Social, and Regulatory Considerations.

Economic, environmental and social aspects of sustainability for engineering design, streamlined life cycle assessments, EcoAudits, carbon/water/energy footprints, economic assessments

Practical application of renewable energy technologies, Energy and resource conservation and Project siting, Economics, Financing, Technical and engineering aspects, Regulatory issues, Energy storage, monitoring and verification, Advantages, limitations and potential of various energy sources, wind, solar, small-scale hydro, ground-source heat pumps, combined heat and power, biofuels, fuel cells, strategies and cost/benefit analyses employed by energy analysts to meet demand with clean energy production, proposal for a renewable energy project.

Reflects current trends and emerging technologies in sustainability

Numerical methods used for solving linear algebraic equations and differential equations, numerical methods for performing differentiation, integration, and curve fitting, properties of some special functions.

Optimization problem formulation and terminology; Objective function; constraints; optimization variable; constrained and unconstrained optimization. Solution techniques using gradient based methods, penalty techniques are discussed. Formulation and solution of linear programming, non-linear programming. Algorithms are implemented in computer programs for problem solution.

Joint and conditional probabilities, correlation, independence, linear combinations of random variables. Essential statistical techniques: descriptive statistics, sampling distributions, data analysis, and parameter estimation, Central limit theorem, t-distribution, point estimates and confidence intervals of population mean and variance, and hypothesis testing will be explored in detail, linear regression, multiple linear regression, analysis of variance (ANOVA), modeling of complex engineering data, real-world engineering examples

A minimum of three weeks of practical training in off-campus sites is selected by the program. Students are required to submit a recognition letter from the site where they received their training, in addition, a report and a presentation are submitted as well. Course is a Pass/Fail course.

Application-oriented capstone project to show competence in major academic area, where an independent research project is conducted under the guidance of a school member in the DEE program. The research should contribute to the advancement of knowledge in the field. A written report and formal presentation are required

Show competence in the major academic area, where an independent design/management project is conducted under the guidance of a school member in the DEE program. The design/management project should contribute to the advancement of knowledge in the field by utilizing computer software such as finite element packages for structural analysis and Primavera project planner for construction management. Professional drawings, calculation sheets, written report, and formal presentation are required depending on the project nature.