Artificial Intelligence & Data Science
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Artificial Intelligence for Trading

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Course Report - Artificial Intelligence for Trading

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

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

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

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

Python programming

Basic data structures

Basic Numpy

Statistics

Mean, median, mode

Variance, standard deviation

Random variables, independence

Distributions, normal distribution

T-test, p-value, statistical significance

Calculus and linear algebra

Integrals and derivatives

Linear combination, independenceMatrix operations

Eigenvectors, eigenvalues

What You Will Learn

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

Basic Quantitative Trading

Factor Investing and Alpha Research

Sentiment Analysis with Natural Language Processing

Advanced Natural Language Processing with Deep Learning

Simulating Trades with Historical Data

Target Students

Quantitative analyst

Quantitative researcher

Investment analyst

Data intelligence analyst

Risk analyst

Desk quant

Desk strategist

Financial engineer

Financial data scientist

Course Instructors

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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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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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Elizabeth Otto Hamel

Instructor

Elizabeth received her PhD in Applied Physics from Stanford University, where she used optical and analytical techniques to study activity patterns of large ensembles of neurons. She formerly taught data science at The Data Incubator.
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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.

Corporate Sponsors

Course Reviews

Average Rating Based on 6 reviews

5.0

100%

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