Students from a global, experiential, research university built on a tradition of engagement are ready to contribute to your organization's mandate in areas ranging from dashboarding/data visualization, data mining, statistical analysis and modeling, evidence-based management (analysis yielding business insights), and/or AI/machine learning . This is your opportunity to connect with students and faculty from a renowned institution in New England that consistently ranks among the top universities in the United States and globally and is known for its focus on interdisciplinary education, extensive research output, and collaborative student culture .

Integrates theory, practice, case studies, and experiential learning with operational and organizational concepts including, but not limited to, nonprofit law, financial management, human resource management, fund-raising and development, promotions, and grant writing. Aims to synthesize learning in a practical manner.

Students in the Master of Science in Project Management program have developed advanced skills in managing project scope, risks, schedules, budgets, and performance metrics. Throughout the course, students engage in creating tailored project plans that address the entire project lifecycle, from initiation to closing. They are capable of stakeholder management, quality management, risk assessment, and integrating ethical considerations into their projects. Companies will benefit from students who are proficient in using tools like Microsoft Project and can deliver detailed project plans, risk registers, and communication strategies.

This experience is designed for learners who are developing skills in Python programming specifically for financial data analytics. Participants will gain proficiency in using key Python libraries such as NumPy, Pandas, and Matplotlib to analyze financial data and implement financial models. Learners will be equipped to apply their knowledge to real-world financial scenarios, such as portfolio optimization and risk assessment, making them valuable contributors to industry projects. McKinney Ch. 2 – Python Language Basics, IPython, and Jupyter Notebooks McKinney Ch. 3 – Built-in Data Structures, Functions, and Files Introduction to Python McKinney Ch. 4 – NumPy Basics IntermediatePython McKinney Ch. 5 – Getting Started with pandas Web Data, Log and Simple Returns, and Portfolio Math Data Manipulation with Pandas McKinney Ch. 8 – Data Wrangling: Join, Combine, and Reshape, Joining Data with Pandas McKinney Ch. 10 – Data Aggregation McKinney Ch. 11 – Time Series Earn 10,000 XP 2 Trading Strategies Multi-Factor Models Portfolio Optimization 5 Simulations 1 McKinney, Wes (2022). Python for Data Analysis. 3rd ed. O’Reilly Media, Inc. Welch, Ivo (2022). Corporate Finance. 5th ed. Ivo Welch.