Artificial Intelligence & Data Science
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Artificial Intelligence for Trading

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Course Features

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Duration

6 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Advanced

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Effort

10 hours per week

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Teaching Type

Self Paced

Course Description

Android holds over 80 percent market share in the mobile operating system market. This program is for you if you are new to programming and want to create Android apps.

Course Overview

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Virtual Labs

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Job Assistance

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Personlized Teaching

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

Basic Numpy

Basic data structures

Calculus and linear algebra

Distributions, normal distribution

Eigenvectors, eigenvalues

In order to successfully complete this program, you should meet the following prerequisites

Integrals and derivatives

Linear combination, independenceMatrix operations

Mean, median, mode

Python programming

Random variables, independence

Statistics

T-test, p-value, statistical significance

The Artificial Intelligence for Trading Nanodegree program is designed for students with intermediate experience programming with Python and familiarity with statistics, linear algebra and calculus

Variance, standard deviation

What You Will Learn

Advanced Natural Language Processing with Deep Learning

Basic Quantitative Trading

Factor Investing and Alpha Research

Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization

Sentiment Analysis with Natural Language Processing

Simulating Trades with Historical Data

Target Students

Data intelligence analyst

Desk quant

Desk strategist

Financial data scientist

Financial engineer

Investment analyst

Quantitative analyst

Quantitative researcher

Risk analyst

Course Instructors

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Arpan Chakraborty

Instructor

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
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Cezanne Camacho

Curriculum Lead

Cezanne is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.
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Cindy Lin

Curriculum Lead

Cindy is a quantitative analyst with experience working for financial institutions such as Bank of America Merrill Lynch, Morgan Stanley, and Ping An Securities. She has an MS in Computational Finance from Carnegie Mellon University.
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Eddy Shyu

Instructor

Eddy has worked at BlackRock, Thomson Reuters, and Morgan Stanley, and has an MS in Financial Engineering from HEC Lausanne. Eddy taught data analytics at UC Berkeley and contributed to Udacity’s Self-Driving Car program.
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