Data Science for Finance Professional Certificate
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.
CPE Credits: 35 - The complete list of CPE courses can be found here.
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.
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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
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
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
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
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