Data Analysis and Programming for Finance PC- Virtual Series

New York Institute of Finance

ITEM DAPFVS2023

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17

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

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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.

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  • Flexible payments

    Pay your monthly bill using a bank transfer, check, or debit card.

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

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Data Analysis and Programming for Finance PC- Virtual Series

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$1,550.00

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