1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
        2、培訓結束后,授課老師留給學員聯系方式,保障培訓效果,免費提供課后技術支持。
        3、培訓合格學員可享受免費推薦就業機會。

• Introduction
• Understanding the Fundamentals of Python
• Overview of Using Technology and Python in Finance
• Overview of Tools and Infrastructure
• Python Deployment Using Anaconda
  Using the Python Quant Platform
  Using IPython
  Using Spyder
  Getting Started with Simple Financial Examples with Python
• Calculating Implied Volatilities
  Implementing the Monte Carlo Simulation
  Using Pure Python
  Using Vectorization with Numpy
  Using Full Vectoriization with Log Euler Scheme
  Using Graphical Analysis
  Using Technical Analysis
  Understanding Data Types and Structures in Python
• Learning the Basic Data Types
  Learning the Basic Data Structures
  Using NumPy Data Structures
  Implementing Code Vectorization
  Implementing Data Visualization in Python
• Implementing Two-Dimensional Plots
  Using Other Plot Styles
  Implementing Finance Plots
  Generating a 3D Plot
  Using Financial Time Series Data in Python
• Exploring the Basics of pandas
  Implementing First and Second Steps with DataFrame Class
  Getting Financial Data from the Web
  Using Financial Data from CSV Files
  Implementing Regression Analysis
  Coping with High-Frequency Data
  Implementing Input/Output Operations
• Understanding the Basics of I/O with Python
  Using I/O with pandas
  Implementing Fast I/O with PyTables
  Implementing Performance-Critical Applications with Python
• Overview of Performance Libraries in Python
  Understanding Python Paradigms
  Understanding Memory Layout
  Implementing Parallel Computing
  Using the multiprocessing Module
  Using Numba for Dynamic Compiling
  Using Cython for Static Compiling
  Using GPUs for Random Number Generation
  Using Mathematical Tools and Techniques for Finance with Python
• Learning Approximation Techniques
  Regression
  Interpolation
  Implementing Convex Optimization
  Implementing Integration Techniques
  Applying Symbolic Computation
  Stochastics with Python
• Generation of Random Numbers
  Simulation of Random Variables and of Stochastic Processes
  Implementing Valuation Calculations
  Calculation of Risk Measures
  Statistics with Python
• Implementing Normality Tests
  Implementing Portfolio Optimization
  Carrying Out Principal Component Analysis (PCA)
  Implementing Bayesian Regression using PyMC3
  Integrating Python with Excel
• Implementing Basic Spreadsheet Interaction
  Using DataNitro for Full Integration of Python and Excel
  Object-Oriented Programming with Python
• Building Graphical User Interfaces with Python
• Integrating Python with Web Technologies and Protocols for Finance
• Web Protocols
  Web Applications
  Web Services
  Understanding and Implementing the Valuation Framework with Python
• Simulating Financial Models with Python
• Random Number Generation
  Generic Simulation Class
  Geometric Brownian Motion
  The Simulation Class
  Implementing a Use Case for GBM
  Jump Diffusion
  Square-Root Diffusion
  Implementing Derivatives Valuation with Python
• Implementing Portfolio Valuation with Python
• Using Volatility Options in Python
• Implementing Data Collection
  Implementing Model Calibration
  Implementing Portfolio Valuation
  Best Practices in Python Programming for Finance
• Troubleshooting
• Summary and Conclusion
• Closing Remarks