Data Analysis and Programming for Finance Professional Certificate
This course will teach you the essential elements of Python and R to build practically useful applications and conduct data analysis for finance.
This Professional Certificate comprises the following courses:
- Python Programming for Finance (Days 1 - 3)
- R Programming for Finance (Days 4 & 5)
Prerequisite knowledge:
- Basic probability and statistics
- Some familiarity with financial securities and derivatives
- Elementary differential and integral calculus
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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 1: 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 1: Data Analysis with R
- Manipulating data frames
- Descriptive statistics
- Inference and time series analysis
Module 2: R Applications
- Regression analysis
- Volatility modeling
- Risk management: VaR and ES