Data Analysis and Programming with Python and R of Finance Professional Certificate (NYIF)

Colleagues, the Data Analysis and Programming (with Python and R) for Finance Professional Certificate program from the New York Institute of Finance will equip you to build useful applications and conduct data analysis for finance. Prerequisite knowledge: Probability and statistics, familiarity with financial securities and derivatives, elementary differential and integral calculus. You will gain desk-ready skills in Python and R programming, knowledge of their strengths and weaknesses, essential data analysis concepts and techniques for finance, and build applications for finance using Monte Carlo and finite difference techniques, including an American option pricer. Skill-based training modules include: 1) Introduction to Python - Anaconda Python distribution, Interactive programming: IPython and Jupyter notebooks, Programming: control structures, data types, functions, data structures, Modules and Packages, 2) Essential Python Toolkit - Date and time management: format, measuring time lapse, 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, 3) Arrays, Vectorization and Random NUmbers - NumPy: array processing, Vectorized functions, Random number generation, 4) Scientific Computing with Python - Matplotlib: 2D and 3D plotting, Using pyplot, SciPy: scientific computing, Root finding, interpolation, integration and optimization, 4) 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, 5) Python Applications - Monte Carlo simulation, Simulating asset price trajectories, Variance reduction techniques, and Pricing options by Monte Carlo simulation and finite difference methods, 6) 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, 7) Data Analysis with R - Manipulating data frames, Descriptive statistics, Inference and time series analysis, and 8) R Applications - Regression analysis, Volatility modeling, and Risk management: VaR and ES. 

Enroll today at (teams & execs welcome): https://tinyurl.com/ydm3fnee 

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