Canadore College
Canadore College
Log in Sign up

Financial Data Analytics with Python

FINA 6333
Closed
Northeastern University
Northeastern University
Boston, Massachusetts, United States
Liza Littenberg-Tobias
She / Her
Experiential Partnership Manager
(3)
5
Timeline
  • January 7, 2025
    Experience start
  • May 1, 2025
    Experience end
Experience
1/1 project matches
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries

Experience scope

Categories
Financial modeling Data visualization Data modelling
Skills
mathematics pandas (python package) data recording data manipulation trading strategy python (programming language) data wrangling portfolio optimization ipython (python package) financial data
Student goals and capabilities

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.

Students

Students
Graduate
Beginner, Intermediate, Advanced levels
100 students
Project
15 hours per student
Students self-assign
Teams of 4
Expected outcomes and deliverables
  • Python scripts for financial data analysis and visualization
  • Reports on portfolio performance and risk metrics
  • Algorithmic trading strategy simulations
  • Data-driven insights on options and futures pricing
  • Efficient frontier visualizations for portfolio optimization
Project timeline
  • January 7, 2025
    Experience start
  • May 1, 2025
    Experience end

Project Examples

Requirements
  • Develop a Python-based tool to analyze historical stock data and predict future trends
  • Create a multifactor portfolio model to optimize asset allocation
  • Simulate an algorithmic trading strategy and evaluate its performance
  • Analyze the value at risk (VaR) for a given investment portfolio
  • Visualize the efficient frontier for a set of financial assets
  • Assess the impact of different market conditions on options pricing
  • Generate a report on the risk-return profile of a diversified portfolio
  • Implement a Python script to automate financial data collection and analysis

Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

  • Q1 - Text short
    We would like to communicate via email. Please provide the best e-mail address to reach out!