This work has been supported by US National Science Foundation CISE-MSI (RCBP-ED: CNS: Data Science and Engineering for Agriculture Automation) 


    Available courses

    Python for Everyone

    This 4-week course introduces students to the programming language Python, where they will learn the mechanics and language of programming. The fundamental goals of the course are to learn how to take a programming class and how to program in Python. Additional goals include how to be a mentor for programming and be a responsible participant in our modern technological society. Students should take this course first and then progress to other Data Science courses.

    Python for Data Science

    Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This 4-week course brings together data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. Also, this course will cover a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations.

    Data Engineering

    The Data Engineering course covers data ETL (Extract, Transform, Load), analysis, visualization, prediction, and cross-validation using Python and SQL. This course also covers data engineering tools and examples for creating remote access and web services. A variety of different types of know database models/technologies are introduced, along with the concepts and practical examples of containerization, collaboration and open source technologies, to include related security and authentication protocols.

    Image credit: https://www.flaticon.com/free-icon/big-data_4072573?k=1668546239154

    Intro to Unix

    This is a mini-course where students will learn the shell, which is powerful user interface for Unix-like operating systems. It can interpret commands and run other programs. It also enables access to files utilities, and applications, and is an interactive scripting language. Additionally, you can use a shell to automate tasks. Linux shell commands are used for navigating and working with files and directories. You can also use them for file compression and archiving. In this mini-course, you will learn about the characteristics of Linux commands and shell scripting. You will explore the different Linux commands and their outputs, Bash scripting, and how to schedule jobs using crontab. You will learn how to work with filters, pipes, and variables. In case you require any assistance while completing the course we encourage you to post on the course discussion forums that are monitored by the course team.

    Data Science

    This course introduces methodologies and technologies associated with data science, specifically data science that supports business analytics and decision-making. Students will learn concepts and tools such as big data, data and text mining, predictive and analytical modeling and artificial intelligence. Students who complete this course will be able to understand the trends of data science and apply data science methods to improve decision making.

    This course is sponsored by national science foundation. The data science and engineering research team in the computer science and information technology department at the university of the District of Columbia designed this course.


    Computer Networks

    You use the Internet through your PC (Personal Computer), laptop, tablet, smart pad, and smartphone every day in everything you do. Through your own PC/laptop, you can easily learn everything about the Internet, and that is what this course is focused on.

    Image Credit: https://www.flaticon.com/free-icon/computer-networks_3470447

    DevOps on Amazon Web Services

    This course teaches you how to use the combination of DevOps philosophies, practices and tools to develop, deploy, and maintain applications in the AWS Cloud. Benefits of adopting DevOps include: rapid delivery, reliability, scalability, security and improved collaboration.