Data Analysis and Programming for Finance PC- Virtual Series
This course will teach you the essential elements of Python and R to build practically useful applications and conduct data analysis for finance.
Prerequisite knowledge:
- Basic probability and statistics
- Some familiarity with financial securities and derivatives
- Elementary differential and integral calculus
Learn now pay later with
To make it even easier to learn, you can finance your program through Affirm.
Loans offered through Affirm are available in the U.S. and Canada.
Module 1: Introduction to Python
- The Anaconda Python distribution
- Interactive programming: IPython and Jupyter notebooks
- Programming: control structures, data types, functions, data structures
- Modules and Packages
Module 2: Essential Python Toolkit
- Date and time management : format, measuring time lapse, etc.
- How to build and run a standalone application
- Parsing command line arguments
- Importing/Exporting files
- Reading and writing in CSV format
- Accessing SQL databases
- Multiprocessing
- Using a dictionary for explicit indexing
Module 3: Arrays, Vectorization and Random NUmbers
- NumPy: array processing
- Vectorized functions
- Random number generation
Module 1: Scientific Computing with Python
- Matplotlib: 2D and 3D plotting
- Using pyplot
- SciPy: scientific computing
- Root finding, interpolation, integration and optimization
Module 2: Data Analysis with Python
- Data analysis with scipy.stats and pandas
- Pandas data structures: series and data frames
- Importing and exporting data from/to MS Excel
- Importing data from websites
Module 3: Python Applications
- Monte Carlo simulation basics
- Simulating asset price trajectories
- Variance reduction techniques
- Pricing options by Monte Carlo simulation
- Pricing options by finite difference methods
Module 1: R Basics
- The IDE: RStudio
- R syntax
- R objects: vectors, matrices, arrays, data frames and lists
- Flow control: branching, looping and truth testing
- Importing and manipulating data
- Plotting with R
Module 2: Data Analysis with R
- Manipulating data frames
- Descriptive statistics
- Inference and time series analysis
0 : Regression analysis
1 : Volatility modeling
2 : Risk management: VaR and ES