Difference between revisions of "FE582 Foundations of Financial Data Science"

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|LectureOutline =  
 
|LectureOutline =  
 
|Week1 = Week 1
 
|Week1 = Week 1
|Topic1 = Introduction to Financial Engineering
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|Topic1 =
|Reading1 = Ch. 1 and 2
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|Reading1 =  
 
|Week2 = Week 2
 
|Week2 = Week 2
|Topic2 = Capital Markets Overview
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|Topic2 =  
|Reading2 = Ch. 3
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|Reading2 =  
 
|Week3 = Week 3
 
|Week3 = Week 3
|Topic3 = Corporate Finance & Valuation
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|Topic3 =  
|Reading3 = Ch. 3
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|Reading3 =  
 
|Week4 = Week 4
 
|Week4 = Week 4
|Topic4 = Equity Analysis
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|Topic4 =  
|Reading4 = Ch. 4
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|Reading4 =  
 
|Week5 = Week 5
 
|Week5 = Week 5
|Topic5 = Fixed Income Debt Securities
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|Topic5 =  
|Reading5 = Ch. 4
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|Reading5 =  
 
|Week6 = Week 6
 
|Week6 = Week 6
|Topic6 = Overview of Bonds Sectors & Instruments
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|Topic6 =  
|Reading6 = Ch. 4
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|Reading6 =  
 
|Week7 = Week 7
 
|Week7 = Week 7
|Topic7 = Valuation of Debt Securities
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|Topic7 =  
|Reading7 = Ch. 4
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|Reading7 =  
 
|Week8 = Week 8
 
|Week8 = Week 8
|Topic8 =Securitized Products
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|Topic8 =
 
|Reading8 =  
 
|Reading8 =  
 
|Week9 = Week 9
 
|Week9 = Week 9
|Topic9 = Leveraged Loans & CLO's
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|Topic9 =  
|Reading9 = Ch. 5
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|Reading9 =  
 
|Week10 = Week 10
 
|Week10 = Week 10
|Topic10 = General Principles of Credit Analysis
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|Topic10 =  
|Reading10 = Ch. 5
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|Reading10 =  
 
|Week11 = Week 11
 
|Week11 = Week 11
|Topic11 = Foreign Exchange
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|Topic11 =  
|Reading11 = Ch. 6
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|Reading11 =  
 
|Week12 = Week 12
 
|Week12 = Week 12
|Topic12 = Poisson Processes and Jump Diffusion
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|Topic12 =  
|Reading12 = Ch. 11
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|Reading12 =  
 
|Week13 = Week 13
 
|Week13 = Week 13
|Topic13 = Exotic Options
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|Topic13 =  
|Reading13 = Ch. 7
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|Reading13 =  
 
|Week14 = Week 14
 
|Week14 = Week 14
|Topic14 = Review & Catch-up
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|Topic14 =  
 
|Reading14 =  
 
|Reading14 =  
  
 
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Revision as of 20:42, 5 April 2018



Course Catalog Description

Introduction

This course will provide an overview of issues and trends in data quality, data storage, data scrubbing, and data flows. Topics will include data abstractions and integration, enterprise level data issues, data management issues with collection, warehousing, preprocessing and querying, similarity and distances, clustering methods, classification methods, text mining, and time series. Case studies will be presented in support of the theoretical concepts. Furthermore, the Hadoop-based programming framework for big data issues will be introduced along with any governance and policy issues. These concepts will be applied to areas such as digital marketing and computational advertising, energy and healthcare analytics, social media and social networks, and capital markets financial data. A one credit Hanlon lab course, FE-513: Practical Aspects of Database Design is co-requisite to this course in order to facilitate learning of the practical side of data management.
Campus Fall Spring Summer
On Campus X
Web Campus X

Instructors

Professor Email Office
Dragos Bozdog
dbozdog@stevens.edu Babbio 429A



More Information

Course Description

This course is the first course for the certificate in Financial Services Analytics. Financial services analytics is the science and technology of creating data-driven decision-making analytics for the financial services industry. This can lead to more effective business operations, enhanced customer services and product offerings, and improved risk analysis and risk management. This course is the key building block in this certificate as good data and the understanding of data is critical to the creation of robust financial services analytics. The financial services analytics certificate has four key areas making up its knowledge base:

  • Foundations of Financial Data Science (FE-582)
  • Introduction to Knowledge Engineering (FE-590)
  • Financial Systems Technology (FE-595)
  • Data Visualization Applications (FE-550)

 

 

Co-Requisite: FE 513 – Practical Aspects of Database Design

Course Outcomes

After taking this course, the students will be able to:

  1. Have a working knowledge of the issues of data quality, data storage, data scrubbing, data flows, and data encryption and their potential solutions.
  2. Understand and design various schemas needed for the representation of financial data.
  3. Tackle problems dealing with data management issues such as collection, warehousing, preprocessing and querying.
  4. Will get a primer on database management as well as advantages and disadvantages from the attached lab course FE 513.
  5. Understand how to write applications using the map-reduce feature of Hadoop clusters.
  6. Have a working understanding of all the databases available for them through the Hanlon lab.
  7. Apply the newly acquired data management and database skills to financial data from the capital markets, social media, and the financial services sector.


Course Resources

Textbook

No single textbook covers all the topics. Several references will be used and supplementary notes will be provided whenever appropriate.

Additional References

  1. Charu C. Aggarwal, Data Classification: Algorithms and Applications. CRC Press, 2015. (ISBN: 978-1-4665-8674-1)
  2. Charu C. Aggarwal, Data Mining. Springer, 2015. (ISBN: 978-3-319-14141-8)
  3. Deborah Nolan and Duncan T. Lang, Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving, CRC Press, 2015. (ISBN: 978-1-4822-3481-7)
  4. Norman Matloff, The Art of R Programming, No Starch Press, 2011. (ISBN: 978-1-59327-384-2)
  5. Cathy O’Neil and Rachel Schutt, Data Science, O’Reilly, 2014. (ISBN: 978-1-449-35865-5)



Grading

Grading Policies

Assignments 60% 
Project 40%


Lecture Outline

Topic Reading
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14