Hands-on Python for Finance
Updated on
Course overview
- Provider
- Udemy
- Course type
- Paid course
- Level
- Intermediate
- Duration
- 6 hours
- Lessons
- 36 lessons
- Certificate
- Available on completion
- Course author
- Packt Publishing
-
- General programing skills in Python and working with common Python interfaces
- Using Numpy, Pandas and matplotlib to manipulate, analyze and visualize data
- Understand the Time value of money applications and project selection
- Getting and with working data, time series forecasting methods and linear models
- Understand Correlation and portfolio construction
- Be comfortable with Monte Carlo Simulation, Value at Risk and Options Valuation
Description
Did you know Python is the one of the best solution to quantitatively analyse your finances by taking an overview of your timeline? This hands-on course helps both developers and quantitative analysts to get started with Python, and guides you through the most important aspects of using Python for quantitative finance.
You will begin with a primer to Python and its various data structures.Then you will dive into third party libraries. You will work with Python libraries and tools designed specifically for analytical and visualization purposes. Then you will get an overview of cash flow across the timeline. You will also learn concepts like Time Series Evaluation, Forecasting, Linear Regression and also look at crucial aspects like Linear Models, Correlation and portfolio construction. Finally, you will compute Value at Risk (VaR) and simulate portfolio values using Monte Carlo Simulation which is a broader class of computational algorithms.
With numerous practical examples through the course, you will develop a full-fledged framework for Monte Carlo, which is a class of computational algorithms and simulation-based derivatives and risk analytics.
About the Author
Matthew Macarty has taught graduate and undergraduate business school students for over 15 years and currently teaches at Bentley University. He has taught courses in statistics, quantitative methods, information systems and database design.
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