The following project contains a selection of assignments form GMU courses that I have taken. The purpose of this project is to showcase my best work on different subject matters relation to Data Science and Analytics. To view the projects, click on the title of the course which will redirect you to the courses folder in which the contents of the project are enclosed. For more samples of my work, please contact me through the Contact section of my website.
Course Description: Introduces modeling relationships contained in data and linear models to make predictions in business. Topics include estimation, hypotheses testing, statistical inference, analysis of variance and linear regression techniques. Fundamentals of linear programming to solve optimization problems in business. Apply analytical tools to gain insights from real-life datasets. Hands-on experience and application of the methods to data sets using spreadsheet software.
Course Description: Introduction to the use of computers in scientific discovery through simulations and data analysis. Covers historical development and current trends in the field.
Course Description: Covers use of computers to solve practical scientific problems. Topics include creating effective scientific presentations, analysis of experimental data, online literature, data/information ethics, scientific modeling, and communication/collaboration tools. Designed to equip students with the knowledge and confidence they need to use future hardware and software systems both as students and throughout their scientific careers.
Course Description: Undergraduate-level introduction to computational concepts, principles, and modeling approaches in social sciences, emphasizing simulations and elements of complexity theory as they apply to social phenomena.
Course Description: Undergraduate-level introduction to Agent-based Modeling. Provides a background onto why agent-based models and hands-on examination of agent-based models in the social sciences by examining and experimenting with a variety of social simulation projects.
Course Description: This course expands upon the foundation provided by CDS 130. Fundamental computational modeling techniques are used in a variety of science and engineering disciplines. Continued development of algorithmic thinking skills will be done using different computational environments.
Course Description: Focuses on elements of programming using the Fortran language and selected elements of the C language with emphasis on the aspects used in the computational and data sciences.
Course Description: A broad introduction to network methods and applications that examine systems based on relations, structures, connectivity, location, interactions, and other network properties. This class includes, but is not limited to, social networks. Example applications covered will include: infrastructure networks, politics, diseases, and organizations, along with a variety of other phenomena.
Course Description: The techniques and software used to visualize scientific simulations, complex information, and data visualization for knowledge discovery. Includes examples and exercises to help students develop their understanding of the role visualization plays in computational science and provides a foundation for applications in their careers.
Course Description: Data and databases used by scientists. Includes basics about database organization, queries, and distributed data systems. Student exercises will include queries of existing systems, along with basic design of simple database systems.
Course Description: Data mining techniques from statistics, machine learning, and visualization to scientific knowledge discovery. Students will be given a set of case studies and projects to test their understanding of this field and provide a foundation for future applications in their careers.
Course Description: Covers the governing framework of data science for storing and processing big data in a distributed computer environment using simple programming models. Includes a comprehensive selection of tools from Hadoop, MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka.
Course Description: An introductory examination of image mathematics, computational protocols, and applications. Topics include image operator notation, channel operators, informational operators, intensity operators, geometric operators, image transformations, frequency filtering, and image basis set expansions. This course will build the students’ computational skill set as applied to visual data and create a library of image analysis scripts.
Course Description: Introduces computer skills and packages commonly used in quantitative scientific research.
Course Description: Use of computer packages in statistical analysis of data. Topics include data entry, checking, and manipulation, and use of computer statistical packages for graphical procedures, basic descriptive and inferential procedures, and regression.
Course Description: Features statistical graphics, maps and simple models used to bring out patterns in data. Introduces statistical software and addresses data access and import. Presents exploratory strategies motivating data transformations. Stresses the cognitive foundations of good graphics. Graphics include dot plots, box plots, Q-Q plots, parallel coordinate plots, scatterplot matrices and linked views. Exploration includes use of dynamic graphics.