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New York Institute of Finance

Data Science for Finance Professional Certificate

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Description

The Professional Certificate course will teach you how to extract valuable insights from financial data with the powerful Python programming language. The course starts with a comprehensive introduction to the fundamentals of the Python open data science stack, including NumPy, SciPy, Pandas, Matplotlib, and scikit-learn with specific applications to finance. You will learn how to wrangle data from many different data sources as well as the fundamentals of machine learning. By the end of the course you will have developed highly relevant and sought after analytical skills and the tools to develop your own financial modeling or algorithmic trading strategy using Machine Learning.

Prerequisite knowledge:

  • Familiarity with Python is essential
  • Familiarity with financial instruments and markets
  • Basic calculus
  • Basic linear algebra

If you are unfamiliar with Python, it is recommended that you complete Python Programming for Finance, before attempting this Professional Certificate.

CURRICULUM

Day 1

MODULE 1: REVIEW OF PYTHON BASICS

  • Variables & Types
  • Python Lists
  • List Manipulations
  • Functions
  • Methods
  • Importing Packages
  • The NumPy Package
  • NumPy Arrays
  • Basic Statistics in Python

MODULE 2: NUMERICAL PROGRAMMING WITH NUMPY

  • Multi-dimensional Arrays
  • Array Operations
  • Array and Boolean Indexing
  • Broadcasting
  • Vectorizing Code
  • Generating Random Numbers
  • Application: Simulating Stochastic Processes

MODULE 3: PLOTTING WITH MATPLOTLIB

  • Pyplot for MATLAB Style Plotting
  • Scatter Plots
  • Histograms
  • Box Plots
  • Financial Plots
  • Application: Technical Analysis of Stocks
  • 3D Plotting
  • Application: Visualizing Volatility Surfaces
  • Back to Top

Day 2

MODULE 1: SCIENTIFIC COMPUTING WITH SCIPY

  • Multi-dimensional Arrays
  • Array Operations
  • Array and Boolean Indexing
  • Broadcasting
  • Vectorizing Code
  • Generating Random Numbers
  • Application: Simulating Stochastic Processes

MODULE 2: DATA ANALYSIS WITH PANDAS

  • Dataframes
  • Series and Panel Objects
  • Operations
  • Selecting and Slicing Data
  • Plotting
  • Application: Working with Financial Time Series
  • Grouping Data
  • Joining, Appending and Merging Data
  • Application: Portfolio Analysis

Day 3

MODULE 1: SQL DATABASES

  • Variety of SQL Databases
  • sqlite
  • The Python Database API
  • Connection Objects
  • Cursor Objects
  • Row Objects
  • SQL Basics: Select, Update, Delete, Insert
  • Joins
  • Databases, Tables, and Indexes
  • Create, Alter, and Drop

MODULE 2: MACHINE LEARNING ALGORITHMS I

  • Parametric vs Non Parametric Models
  • OLS Regression
  • Lasso and Ridge
  • Extending Parametric Models
  • Polynomials
  • Scaling
  • Subset Selection
  • Classification Algorithms
  • Logistic Regression
  • L1 and L2 Penalty
  • Single and Multi-Class
  • Application: Multi Class Modeling

Day 4

MODULE 1: MACHINE LEARNING ALGORITHMS II

  • Non Parametric Models
  • Decision Trees
  • Support Vector Machines
  • Assembling Methods
  • Boosting
  • Adaboost Algorithm
  • Bagging
  • Random Forest Algorithm
  • Latest Advances: Extreme Gradient Boosting (XGB)

MODULE 2: TUNING ALGORITHMS

  • Cross Validation and Testing
  • Pipelines and GridSearch
  • Labs
  • Regression Practice
  • Classification Practice

Day 5

MODULE 1: LEARNING AND CLUSTERING

  • Supervised vs. Unsupervised Learning
  • Principal Components Analysis
  • K Means Clustering
  • DBSCAN Clustering

MODULE 2: NEURAL NETWORKS WITH TENSORFLOW

  • Introduction to Neural Networks
  • Specifying a Model in Tensorflow
  • Training and Testing a Model
  • Application: Predictive Modeling in the Financial Markets

WHAT YOU'LL LEARN

  • Review fundamental features of the Python language including data structures and object-oriented programming
  • Learn how to manipulate and analyze arrays and matrices in Python
  • Use the scientific libraries including NumPy, SciPy, Matplotlib, Pandas, and scikit-learn
  • Learn to work with data in many different formats
  • Develop a portfolio of applications for analyzing and visualizing financial data
  • Learn how to apply machine learning techniques to extract insight from data

Who should attend

  • Developers
  • quants
  • financial engineers
  • data scientists
  • traders
  • portfolio managers and anyone seeking to make the most of the open data revolution.
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