FE595 Financial Technology (FinTech)
Course Catalog Description
Introduction
This course deals with networking and machine learning technologies underlying activities of markets, institutions and participants. The overall purpose is to give students a working understanding of a wide variety of the technological tools that permeate modern life. The successful student will be able to extend this knowledge, understand systems currently in place and use new developments in the field as they are created.
Campus | Fall | Spring | Summer |
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On Campus | X | X | |
Web Campus | X | X |
Instructors
Professor | Office | |
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Kenneth Blaney
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kblaney@stevens.edu
kenneth.r.blaney@gmail.com |
TBA |
More Information
Course Description
Meetings will be in person/streamed on Canvas, once per week. Recordings of these lectures will also be available online through Canvas. Lectures will focus on either understanding or implementing some element of technology. As this course is being given as both an in person and an online class, use of the discussion boards on Canvas for questions and answers is highly encouraged.
Assessments:Assessments will be conducted through individual or group assignments. They will emphasize the need to communicate code clearly for collaboration or review. Students will become well versed in the use of GitHub for sharing code and managing versions. The intent of this structure is to mimic the basic structure one might encounter in industry where code bases will often be transferred from person to person as staff changes over time. As a result, there will be a focus on writing code that can be understood and maintained.
Grading
Grading Policies
The typical Stevens grading scheme for 500 level courses will apply to this class (of specific note: C minus, D plus and D all become F). All students are expected to abide by the Stevens Honor System, as they would in all classes. The final grade in the class will be determined in the following manner:
- 25% Homework Assignments
- 25% Midterm Project
- 50% Final Project
As this is designed to be a project based course, there will be no in class examinations.
Percentages of points earned will be converted to letter grades as follows:
- 90s - A
- 80s – B
- 70s – C
- less than 70 – F
The high end of these ranges (except A and F) will earn a +. The low end of these ranges (except F) will earn a −.
Lecture Outline
Week | Topic | Reading |
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Week 1 | Python Review/Intro to Git |
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Week 2 | AWS and GitHub |
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Week 3 | Flask and cURL |
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Week 4 | Webscraping |
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Week 5 | Midterm |
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Week 6 | NLP |
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Week 7 | Math on words |
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Week 8 | Graph Databases |
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Week 9 | Science Kit Learn |
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Week 10 | Science Kit Learn 2 |
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Week 11 | What is a Neural Network? |
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Week 12 | Using TensorFlow |
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Week 13 | Docker and Containers |
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Week 14 | What is Blockchain? |
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