Earth and Environmental Sciences - Senior
COURSE #: EAES4034
Course Description
The course in Hydrology and Hydrogeology offers a fundamental understanding of all facets of hydrology, with a primary emphasis on water on the surface of the earth. The goal of this course is to study the physical, chemical, hydrological, geological, and other factors that influence the occurrence and dynamics of ground and surface water. Students develop the skills necessary to research water systems, carry out laboratory and field investigations, and resolve hydrological issues. Students gain exposure to a variety of spatial data types used in the investigation of hydrology and water resources, such as expertise in RS and GIS systems.
Course Learning Outcomes
Upon completion of this course, students will be able to:
- Explain the hydrologic cycle, particularly the inherent hydrologic processes and what affects the inherent hydrologic processes.
- Describe basic concept of remote sensing and numerical/spatial analysis techniques commonly used in hydrology and hydrogeology data analysis.
- Use numerical/spatial analysis techniques within an image processing and GIS framework to solve hydrology, hydrogeology, and environmental problems.
- Describe the water cycle and its driving processes.
- Apply the water-balance equation to various hydrological problems in time and space.
- Measure elements of the water cycle, such as stream flow to explain how human activity affects those elements.
- Analyze municipal planning and hydrological data to assess the area's water resource management.
- Analyze how ground water has been used by humans conducting case studies.
Course Assessments and Grading
Item |
Weight, in % |
Class performance & activities |
5% |
Lab assignments |
5% |
Data collection, analysis & reports |
15% |
Short field work & report |
5% |
Mid-term exam |
20% |
Group project & presentation |
15% |
Workshop Quiz & paper |
10% |
Final exam |
25% |
COURSE #: EAES 3027
Course Description
Introduction to methods is used in academic and professional endeavors to formulate and answer earth and environmental research questions. Students will practice using qualitative and quantitative methodologies and research methods including use of academic, public domain and other literature, interviews, field studies data collection, surveys, and primary and secondary data, as well as approaches such as case studies and participatory research. Using an inquiry approach, students will gain practical experience in research design, data collection, and data analysis.
Course Learning Outcomes
At the end of this course the students will be able to:
- Locate relevant earth and environmental information of all types.
- Evaluate and synthesize the information from a variety of quantitative and qualitative sources and viewpoints.
- Formulate relevant and testable research questions about the earth and environment.
- Construct a plan to answer such questions using appropriate research methods.
- Communicate a coherent synthesis and analysis of earth and environmental information, orally, graphically, and in writing.
Course Assignments and Grading
Item |
Weight, % |
Pre-class reading questions |
25 |
In-class activities |
40 |
Research design plan |
20 |
Presentation of research design plan |
10 |
Research ethics |
5 |
COURSE #: ECON 1001
Course Description
Introduction to Microeconomics deals with the interactions between individual households and business. The course helps in explaining the mechanism behind determination of prices of different commodities. It also explains about the prices of the factors of production. It helps in understanding the working of the free-market economy and it introduces students to some of the basic concepts used in economics. The course introduces the students to the various basic concepts necessary to understand economic policies and their effect on society and shows which policies can enhance productive efficiency that may result in greater social welfare. In brief, the course will introduce some explanation about the working of a capitalist economy.
Course Learning Outcome
Upon successfully completing this course, students should be able to:
- Define basic microeconomic concepts.
- Explain how markets optimally allocate scarce resources faced with unlimited wants.
- Describe models of goods’ markets with competitive, oligopolistic and monopolistic setups.
- Determine models to solve microeconomic problems as well as assess the power and limitations of these models.
- Relate the "language" of formal mathematical models and the "language" of graphs, to the microeconomic concepts under review.
Course Assessments and Grading
Item |
Weight |
General participation (includes weekly assessments and in class participation) |
30% |
Homework assignments |
20% |
Midterm |
20% |
Final exam |
30% |
COURSE: # ECON 2006
Course Description
This course focuses on ways to manage personal finances effectively in order to increase savings and reach financial goals and examines ways of how financial reporting is prepared and communicated by businesses. The course enables students to develop their knowledge and understanding of principles and purposes of accounting for individuals, businesses and non-for-profit organization.
Course Learning Outcomes
After completion of the course, students should be able to:
- Define the basic accounting vocabulary, accounting principles, and concepts
- Explain ways to prepare a personal and family budget to reach financial goals
- Discuss ways to increase personal savings
- Perform accounting tasks for non-current assets, inventory, receivables, non-current liabilities, current liabilities, and equity
- Prepare a multiple-step income statement, statement of financial position, and cash flow statement of a company
- Explain the interactions between the financial statements and the way they are used by investors, creditors, regulators, and managers
- Record business transactions using accounting software
- Analyze the financial statements of a company.
Course Assessment and Grading
Item |
Weight |
In-class activity |
10% |
2 quizzes |
35% |
Accounting software project |
25% |
Final exam |
30% |
COURSE #: ECON 3031
Course description
This course builds on an understanding of entrepreneurship and the vital role played by entrepreneurs and entrepreneurship in the development of economy. In addition, the course provides a platform for undertaking research in the field of economics of entrepreneurship and implementing economic analysis of entrepreneurial ventures. Students are introduced to the concept and theory of entrepreneurship. Students use methods of applied entrepreneurial research, assess performance of entrepreneurial ventures, analyze public policy, market regulation and taxation and their impact on entrepreneurial activity.
Course learning outcomes
Upon successfully completing this course, students should be able to:
- Explain the role of entrepreneurship in economies
- Analize entrepreneurial markets and behavior
- Demonstrate an understanding of entrepreneurial financing
- Present an analysis of public policy
Course Assessments and Grading
Item |
Weight, % |
Participation in discussions |
10% |
In class presentations |
30% |
Research Proposal |
30% |
Final Exam |
30% |
COURSE #: EAES 4751
Course Description
Programming in Python is an introductory course that covers programming techniques and tools to manipulate, manage, and analyze relevant data. The course focuses on the Python programming language that students will use to solve statistical analysis problems, apply Machine Learning and Deep Learning techniques, and create a website using Django. The tasks will be accomplished by identifying and using existing Python packages as well as appropriate open-source software extensions. The course introduces basic to advanced statistical functions, data visualization, and data manipulation techniques. The relevant functions in data science are explained. The main goal of this course is to give students an understanding of the breadth of different programming applications. In particular, students will be taught how to design and write effective code using Python to perform routine and specialized data manipulation, management, statistical analysis, and web application development tasks. The tasks will be accomplished by identifying and using existing Python packages as well as appropriate open-source software extensions.
Course Learning Outcomes
Upon completion of this course, every student will be able to:
- Explain the theoretical concepts of different data types
- Conceptualize and create loops and if/else statements in Python
- Create specialized functions in Python to handle results
- Manipulate data for descriptive statistical analysis in Python
- Use Django framework for development of different types of websites, in particular, a highly customizable app, such as an internet magazine website
- Use special packages, such as panda, to create graphs and convert plain text to formatted text.
- Use the packages NumPy, Matplotlib, Pandas and Skikit-Learn for various mathematical calculations, data manipulation, graphing and creating machine learning algorithms.
Course Assessment and Grading
Item |
Weight |
6 Home Assignments |
60% |
Class attendance and participation |
10% |
Final Project |
30% |
COURSE #: EAES 3045
Course description
Geophysics is an interdisciplinary and applied science which involves geology and physics. This course introduces students to various subdisciplines of geophysics (geodesy, gravity, geomagnetism, seismology, and geochronology) and their applications in study of the interior and crust of the Earth and applications in prospecting for water, oil and mineral resources. The course presents a broad overview of size and shape of the earth; seismology and the interior of the earth; heat flow in the earth and geothermics; gravity and variation of density; geomagnetic field and paleomagnetism; electrical properties of the Earth; geochronology and absolute age, and application of the physical properties for exploration purposes.
Course learning outcomes
Upon the completion of the course, students will be able to:
- understand physical behavior of earth and its different layers, including atmosphere and lithosphere
- interpret geophysical data, including seismic waves and earthquake data, thermal data and gravity anomalies
- use mathematical skills to explain geophysical behaviors and processes
- apply geophysical quantitative methods to wide geological problems.
Course Assessment and Grading
Pre-class homework assignments |
5% |
Post-class homework assignments |
30% |
Big-Picture Problems |
10% |
Participation |
5% |
Midterm exams (5% each) |
10% |
Final Project |
10% |
Final exam |
30% |
COURSE #: DMNS 2011
Course Description
Linear Algebra is a foundational course at UCA. It can be applied in business, economics, sociology, ecology, demography, engineering, and other areas. In this course, students will study mathematics that deals with the system of linear equations and their applications, operations with matrices, applications of Markov chains, applications of determinants, eigenvalues and eigenvectors and their applications.
Course Learning Outcomes
Upon successful completion of this course, students should be able to:
- Set up and solve a system of equations to fit a polynomial function to a set of data points.
- Use matrices and Gaussian and Gauss – Jordan eliminations to solve a system of linear equations.
- Do operations with matrices
- Find the inverse of a matrix.
- Use a stochastic matrix to find the nth state matrix of a Markov chain.
- Find steady state matrices of absorbing Markov chain.
- Use matrix algebra to analyze an economic system (Leontief input- output model).
- Find the least square regressions line for a set of data.
- Use Cramer’s rules to solve a system of n linear equations in n variables.
- Model population growth using an age transition matrix and an age distribution vector.
Course Assessments and Grading
Item |
Weight |
Unit Test 1 |
25% |
Unit Test 2 |
30% |
Attendance/ Homework |
10% |
Final exam |
35 % |
COURSE #: DMNS 2035
Course Description
This is an introduction to statistics for students in earth and environmental sciences. It assumes zero to little prior experience in statistics but does assume a good background and understanding of single variable calculus. This course will cover elementary probability including introducing random variables, discrete and continuous probability distributions so that students are able to use the language of probability in statistics. The second part of the course will cover inferential statistics wherein students will learn how to draw conclusions about a population based on a random sample.
Course Learning Outcomes
At the end of this course the students will be able to:
- apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly using appropriate diagrams, titles and labels when required
- summarise the ideas of randomness and variability, and the way in which these link to probability theory to allow the systematic and logical collection of statistical techniques of great practical importance in many applied areas
- have a grounding in probability theory
- perform inference to test the significance of common measures such as means and proportions
- use simple linear regression and correlation analysis and know when it is appropriate to do so.
Course Assessments and Grading
Item |
Weight |
Group project |
30% |
Homeworks |
20% |
Midterm exam |
20% |
Final exam |
30% |
COURSE #: ECON 4037
Course Description
Electricity plays a crucial role in our life, allowing us to perform our daily routines and to undertake economic, social and developmental activities. Economics of Electricity is an interdisciplinary economics course which covers both a detailed description of the technologies and institutions of the consumption, production and transportation sides of electricity markets. A variety of core microeconomic concepts are introduced for non-economists, in order to better understand the behavior of firms, consumers and regulators in electricity markets. The course emphasizes issues related to both the local and the global electricity sectors, with an eye towards pricing, regulation and access. In particular, the tools learned in the course will set the foundations to conduct energy research, making use of quantitative data, write policy case studies, produce market forecasts and manage energy-related firms and investments.
Course Learning Outcomes
At the end of the course in energy economics students are expected to be able to:
- Apply microeconomic modeling techniques to understand the functioning of a variety of energy markets and the impact of government actions in them;
- Discuss the special characteristics of wholesale and retail electricity markets, electricity pricing and electric grids;
- Model investment and other financial decisions for a variety of energy firms;
- Assess the effects of energy price volatility on energy importers and other sources of systemic risk;
- To explain the determinants of energy access and link between energy access and economic development;
- To evaluate programs for improving energy efficiency;
Course Assessment and Grading
Item |
Weight |
Class attendance and participation |
20% |
In-class weekly quizzes |
35% |
Mini-Midterm |
15% |
Final Exam |
30% |
COURSE #: ECON 4308
Course description
Data Science III: Automated statistical analysis of the terabytes of data that are produced every minute in text form is one of the frontiers of data analysis. It allows the integration of new data types in standard statistical analysis as well as new insights into the properties and significance of texts. Text Analysis is an advanced quantitative methods course which builds on the skills and knowledge acquired in Econometrics I & II. Students learn the tools required for statistical analysis of text, including working with character encodings, regular expressions and string functions. The course progresses to construction of datasets from text, analysis of texts based on their similarity, sophistication and sentiment as well as classification of texts, descriptive statistics and data visualization for text data and description of texts based on topic.
Learning outcomes
At the end of this course students are expected to be able to:
- Classify texts based on characteristics using unsupervised learning algorithms
- Utilize regular expressions to write code for pattern matching in text strings
- Create variables from text strings and apply these in categorization of texts and analysis of text sentiment
- Extract sentiment information and other variables from text for application in traditional regression analysis
- Analyze the similarity and sophistication of texts for use in comparison between and identification of authors
- Perform Latent Dirichlet Analysis to determine the topics found in bodies of text
Course Assessment and Grading
Item |
Weight |
Class attendance and participation |
20% |
Individual homework assignment |
50% |
Final Exam |
30% |
COURSE #: DMNS 4180
Course Description
Linear programming is one of the most versatile and powerful mathematical programming techniques that can be employed for efficiently solving a stylized class of decision problems.
Linear programming has been used with marked success to solve optimization problems in areas such as economics (including banking and finance), business administration and management, agriculture and energy, contract bidding, nutrition (diet) planning, health care, public decision making, facility location, transportation, strategic planning, and so on.
In this course, students will study mathematics that deals with various real-world situations which leads to mathematical models involving linear optimization problems, geometrical solution of two-variable problem, simplex algorithm, dual problems and transportation problem.
Course Learning Outcomes
Upon successful completion of this course, students should be able to:
- Formulate a mathematical model of real life problem.
- Solve the formulated mathematical problem with the use of different
- Translate the results back into the context of the original problem.
Course Assessments and Grading
Item |
Weight |
Weekly Test |
60% |
Attendance |
10% |
Final exam |
30% |
COURSE #: EAES 3721
Course Description
This course provides a practical introduction to the fundamental principles of Geographic Information Systems (GIS) and Remote Sensing digital image processing. It focuses on the processing and analysis of remotely sensed imagery using the R programming language. This is a tutorial-based course, where students will follow detailed assignments that guide them through working with R and spatial data. The aim of this course is that students will be able to apply R to any work involving geospatial data that they may encounter in the future.
Course Learning Outcomes
Upon completion of this course, the students will be able to:
- Acquire remotely sensed data from USGS EarthExplorer and Google Earth Engine
- Use R to work with and analyze remotely sensed imagery
- Use R to work with vector data
- Use R to create time series analyses
- Use R to create a land cover classification
- Apply R to analyze wildfire burn severity
Course Assignments and Grading
Assignment |
Weight |
10 Home Assignments |
60% |
Class attendance and participation |
10% |
Final Presentation |
30% |