Looking to elevate your organization, and bring it to the next level? Offer learners from  Georgetown University  the opportunity to be your virtual interns, in a project-based experience. Learners will work on one or more projects of your choosing over the internship period, connecting with you as needed with virtual communication tools. Georgetown University is an approved provider of project management training by the Project Management Institute (PMI)(r). Learners in this program study both predictive and agile project management methodology. They learn how to execute projects to deliver business value as well as plan and manage schedules, budgets, risks and scope. Beyond project management fundamentals, they learn how to lead teams successfully, empower their team members and influence stakeholders. At the end of the program, learners use action-based exercises and the in-depth examination of case studies to apply their learning. Elevate our students' understanding and mastery of project management while providing support for your key projects with a project-based virtual intern.

Georgetown University student consultants, proficient in Python, provide comprehensive support to both data science and data engineering teams through a hands-on applied machine learning program. The program consists of two main phases: Software Engineering for Data Science: Skills development covering databases, data ingestion, and wrangling. Exploration of various software engineering topics. Distributional Statistical Analysis and Modeling: Utilization of scikit-learn for modeling and statistical analysis. Exploration of various models and selection techniques. Throughout the semester, learners engage in a challenging main project, maintaining virtual communication as needed. Additionally, Python-savvy student consultants actively contribute to data analytics teams, receiving training in Python, SQL, statistics, data visualization, and culminating their learning in a capstone project. The capstone project modules include: Foundations of data analytics and Python basics. Advanced data analytics in Python and SQL. Statistics for pattern uncovering and analysis. Data visualization for creating compelling visualizations. Application of knowledge in a capstone project. Throughout the semester, learners undertake a main project, maintaining virtual communication as required.

Student consultants are ready to use their technical and analytical skills to support data science and data engineering teams. Students with at least a beginner proficiency in Python will learn applied machine learning using scikit-learn in two phases. The first phase focuses on software engineering for data science and includes skills building with relational and NoSQL databases, data ingestion and wrangling, as well as software engineering topics such as object oriented programming, testing, security, and agile workflows. The second phase focuses on distributional statistical analysis, classification, clustering, and regression modeling using generalized linear models, non-parametric models, probabilistic models, ensemble models, and multi-layer perceptrons. Students will develop model selection techniques using visual diagnostics to interpret model behavior and to find the simplest, most predictive model for a data set using the bias/variance trade-off and analysis and selection of model triples that include algorithm, features, and hyperparameters.  Learners will work on one main project over the course of the semester, connecting with you as needed with virtual communication tools.

Student consultants are equipped to utilize their technical and analytical skills to bolster data analytics teams. Students who are adept in Python basics will learn data analytics spanning five modules that includes Python, SQL, statistics, data visualization, and a capstone project.  The first module concentrates on the foundations of data analytics and Python basics, providing students with a solid base to handle data and operate Python efficiently. The second module extends into data analytics in Python and SQL, allowing students to process and analyze data from multiple sources effectively. The third module covers statistics, equipping students with the ability to uncover patterns and explain analyses. The fourth module focuses on data visualization, where students learn to create compelling visualizations that effectively communicate their analysis and insights. The final module allows students to apply their accumulated knowledge in a capstone project. Over the course of the semester, learners will undertake one main project, maintaining contact with you through virtual communication tools as required.