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New York Institute of Finance

Data Analysis and Programming for Finance

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Next dates

Aug 5—9
5 days
New York, New York, United States
USD 3509
USD 701 per day

Description

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

CURRICULUM

Day 1

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

Day 2

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

Day 3

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

Day 4

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

Day 5

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

WHAT YOU'LL LEARN

  • Learn the basic elements of programming in Python and R
  • Be familiar with the strengths and weaknesses of each development environment
  • Learn essential data analysis concepts and techniques for finance
  • Build realistic applications for finance using Monte Carlo and finite difference techniques, including an American option pricer

Who should attend

  • Developers
  • quants
  • analysts
  • financial engineers and anyone seeking to become a better financial modeler. While not essential a modest amount of prior programming experience will be beneficial. Some familiarity with financial instruments will be advantageous.
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