Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus  X  X 
Instructors
Professor  Office  

Xiaodi Zhu 
rcollado@stevens.edu  Babbio Center – 629 
More Information
Welcome to FE513! The course aims to introduce the required techniques and fundamental knowledge in data science techniques. It helps students to be familiar with database and data analysis tools. Students will be able to manage data in database and solve financial problems using R program packages. This course is designed for graduate students in the Financial Engineering program at the School of Business.
After taking this course, the students will be able to: Will be able to extract certain data from database using SQL. Will get basic knowledge about programming in R. Will understand basic data mining concepts (clustering & classification) and be able to implement them in R. Will get basic knowledge about data visualization using R. Will get basic knowledge about big data analysis.
Course Resources
None. Instead, we have a list of recommended readings.
Grading
It is very important to us that all assignments are properly graded. If you believe there is an error in your assignment grading, please submit an explanation via email me within 7 days of receiving the grade. No regrade requests will be accepted orally.
This course has a zero tolerance policy for academic dishonesty, and anyone caught will immediately receive an F for the course grade. You may not under any circumstances give a copy of your code to another student, or use another students’ code to help you write your own code.
Identical assignments not only include 100% identical works, but also include works with different variable names and comments but the same logic, code style, etc..
Due dates are firm. Late submission will not be accepted under any circumstance without prior notice and permission from the instructor. At least 20% Points will be deducted for late submission without notice. For fulltime students, excuses such as "busy for oncampus job", "preparing for interview", "working on other courses" are not accepted. For parttime students, please notice the instructor in prior if you have "heavy work load", "business travel", "business meeting", etc. which may affect the homework submission.
Lecture Outline
Topic  Reading  

Week 1  Introduction to Financial Engineering  Ch. 1 and 2 
Week 2  Capital Markets Overview  Ch. 3 
Week 3  Corporate Finance & Valuation  Ch. 3 
Week 4  Equity Analysis  Ch. 4 
Week 5  Fixed Income Debt Securities  Ch. 4 
Week 6  Overview of Bonds Sectors & Instruments  Ch. 4 
Week 7  Valuation of Debt Securities  Ch. 4 
Week 8  Securitized Products  
Week 9  Leveraged Loans & CLO's  Ch. 5 
Week 10  General Principles of Credit Analysis  Ch. 5 
Week 11  Foreign Exchange  Ch. 6 
Week 12  Poisson Processes and Jump Diffusion  Ch. 11 
Week 13  Exotic Options  Ch. 7 
Week 14  Review & Catchup 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus  X  X 
Instructors
Professor  Office  

Brian Moriarty  brian.moriarty@stevens.edu  Babbio Center – 629 
More Information
Tools Used In This Course Visualization Ecosystem Additional Resources A more concise, but somewhat different perspective
* Develop knowledge of tools for visualizing datasets with emphasis on financial datasets. Develop a programmatic understanding of translating data into useful visual forms Develop a critical vocabulary to engage and discuss information visualization Develop an understanding of data visualization theory. Understanding of ethical considerations for data visualization
Course Resources
* Miller, James D. Big Data Visualization, Packt Publishing, 2017. ISBN: 9781785281945
Milligan, Joshua N.Learning Tableau 10, 2nd edition. Packt Publishing, 2016. ISBN: 9781786466358
Tufte, Edward. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press, 2001. Print. ISBN: 9780961392147
Gohil, Atmajitsinh. R Data Visualization Cookbook. Packt Publishing, 2015. Print. ISBN: 9781783989508
TABLEAU LICENSING:
Are your students new to Tableau? Share our free Data Analytics for University Students guide to help them get started. Students can continue using Tableau after the class is over by individually requesting their own oneyear license through the Tableau for Students program here Need help? Find answers to frequently asked questions here.
TABLEAU COURSE MATERIALS: All Tableau course materials are available in the shared Files directory for this course.
Data Sets:
Grading
Final grades will be determined on a 0100 scale. Your final course grade will be determined as follows: 94100=A; 9093.999=A; 8789.999=B+; 8486.999=B; 8083.999=B; 7779.999=C+; 7476.999=C; 7073.999=C; 6069.999=D; below this is an F. Once issued, all grades are final and will not be changed. Borderline grades will be reviewed on a casebycase basis. If you have questions about how grades are assigned in this course, please bring them up at the beginning of the semester or soon thereafter. No questions concerning grading policy will be considered once grades have been submitted.
Lecture Outline
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus  X  X 
Instructors
Professor  Office  

Ricardo A. Collado 
rcollado@stevens.edu  Babbio Center – 629 
More Information
Tools Used In This Course Visualization Ecosystem Additional Resources A more concise, but somewhat different perspective
After successful completion of this course, students will be able to… Understand common objectoriented (OO) design patterns used in relation to financial models Implement common objectoriented design patterns used in relation to financial models Understand and implement derivatives pricing model in OO paradigm utilizing design patterns with focus on clarity, simplicity, elegance and extensibility. Be proficient with basic C++ and OO programming techniques.
Course Resources
Mark S. Joshi, C++ Design Patterns and Derivatives Pricing, 2nd edition. Cambridge University Press, 2008 (required).
S. Lippman, J. Lajoie, B. Moo, C++ Primer, 5th edition. AddisonWesley, 2012. Design Patterns Explained Simply, ebook, https://sourcemaking.com/designpatternsebook. Materials:
Lecture slides: available online through Canvas course shell.
A working C++ IDE. Recommendations:
Grading
All assignments should be the work of an individual student are due on the date shown in the course schedule. Submit to Canvas any late assignments. Late homework will be penalized one grade letter per late week. Grading will be based upon your understanding and analysis of the issues presented in class and readings.
EXAM ROOM CONDITIONSThe following procedures apply to quizzes and exams for this course. As the instructor, I reserve the right to modify any conditions set forth below by printing revised Exam Room Conditions on the quiz or exam.
Lecture Outline
Topic  Reading  

Week 1  Chpt. 1: A simple Monte Carlo model  
Week 2  Chpt. 2 & 3: Encapsulation, Inheritance, and virtual functions  
Week 3  Chpt. 4: Bridging with a virtual constructor  
Week 4  Chpt. 5: Strategies, decoration, and statistics  
Week 5  Chpt. 6: A random numbers class  
Week 6  Chpt. 7: An exotics engine and the template pattern  
Week 7  Chpt. 8: Trees  
Week 8  Chpt. 9: Solvers, templates, and implied volatilities  
Week 9  Chpt. 10 & 11: The factory & Design patterns revisited  
Week 10  Chpt. 12 & 13: The situation & Exceptions  
Week 11  Chpt. 14: Templatizing the factory  
Week 12  Intel Math Kernel Library (MKL)  
Week 13  Intel Parallel STL and DAAL libraries 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus  X 
Instructors
Professor  Office  

Thomas Lonon

tlonon@stevens.edu  Virtual 
More Information
This course is designed for advanced undergraduate students and masters students in Financial Engineering. The goal is to learn the foundation on which Finance is built upon. The students are supposed to have a strong background in applied mathematics (analysis and calculus) and probability at an undergraduate level. Any student who does not already have this previous knowledge will have much greater difficulty learning the material.
Course Resources
Stochastic Calculus for Finance vol I and II, by Steven E. Shreve, Springer Finance, 2004, ISBN13: 9780387249681 (vol I) and 9780387401010 (vol II).
Introduction to Probability Models, 10th edition, by Sheldon M. Ross, Academic Press, 2009, ISBN10: 0123756863, ISBN13: 9780123756862
Probability and Random Processes, by Geoffrey Grimmett and David Stirzaker, Oxford University Press 2001
Stochastic Differential Equation, by Bernt Oksendal, 6th edition, 2010, ISBN10: 3540047581, ISBN13: 9783540047582
Introduction to the Mathematics of Financial Derivatives, by by Salih N Neftci, 2nd ed, Associated Press, 2000, ISBN 0125153929
Grading
There will be around 5 homework assignments throughout the semester. Collaboration is encouraged as it can be helpful to understand some of these concepts. Do not confuse collaboration for academic misconduct. Attempt each problem on your own before seeking help from another person. Make sure that you understand the entire assignment that you turn in, and could reproduce the work or solve a similar problem. Do not think that you can simply copy another person's assignment and expect to understand the material. Late homework will be accepted under the following policy. If the homework is turned in within one week of the original due date, it will receive 2/3 (two third) of its score, going down by a third each week it is late. The homework assignments will have a very firm deadline, of 11:55 PM on the due date. When I say this is a firm date, I mean that if homework is submitted online at 11:56 PM, its late, no exceptions. Plan ahead and submit your homework early to avoid problems due to internet or computer issues.
ExamsThere will be one midterm and one final exam given in the class. If you miss an exam, you must provide a written explanation signed by proper authorities in order to be allowed the chance to take a replacement exam. The midterm and final exam are closed book, but each student can bring one handwritten page of notes to the midterm and two handwritten pages of notes to the final. Calculators are permitted and encouraged, but cell phones and notebook computers are not allowed.
Lecture Outline
Topic  Reading  

Week 1  One Period BAPM  Ch. 1 in vol. I 
Week 2  Multiperiod Model and FPS  Ch. 1 and 2 in vol. I 
Week 3  Expectation in BAPM  Ch. 2 in vol. I 
Week 4  Martingales and Markov  Ch. 2 in vol. I 
Week 5  Stopping Times and RW  Ch. 4 & 5 in vol. I 
Week 6  SSRW and BM  Ch. 3 in vol. II 
Week 7  Quadratic Variation and MP  Ch. 3 in vol. II 
Week 8  Midterm  
Week 9  Reflection Prop. and Cont. Passage Times  Ch. 3 in vol. II 
Week 10  Stochastic Calculus Integrands  Ch. 4 in vol. II 
Week 11  Ito’s Formula  Ch. 4 in vol. II 
Week 12  BlackScholes and Levy  Ch. 4 in vol. II 
Week 13  Change of Measure  Ch. 5 in vol. II 
Week 14  Review & Catchup 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus  X 
Instructors
Professor  Office  

Professor Chihoon Lee, Ph.D.

clee4@stevens.edu  Babbio 514 
More Information
Prerequisites
Students require sound understanding of probability gathered through an undergraduate class such as MA222 or equivalent. Also students must have the ability to program in R. Please consider taking FE515 if you are not familiar with R.
Attendance
Attendance is mandatory, and there may be short pop quizzes every week, starting from the second week.
This course will allow the students to:
Course Resources
The only required textbook is Moore, McCabe, and Craig (2017).
Peter Dalgaard. Introductory Statistics with R. Springer, 2004.
Ionut Florescu and Ciprian Tudor. Handbook of Probability. Wiley, 2013.
William H. Greene. Econometric Analysis. Prentice Hall, Seventh edition, 2012.
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Verlag, 2013.
David Moore, George P. McCabe, and Bruce A. Craig. Introduction to the Practice of Statistics. W. H. Freeman and Co., Ninth edition, 2017.
Grading
You will be required to submit four homework assignments.
All homework assignments must be submitted in R markdown (.Rmd) format, with all answers written as functions. For your information, the main markdown page is here: https://rmarkdown.rstudio.com/. A nice summary of the use of R markdown appears here: http://www.stat.cmu.edu/~cshalizi/rmarkdown/. You may wish to include mathematical expressions in your markdown code. If so, it is useful to use L A TEX, which is taught in FE505. If you wish, you may optionally submit a .pdf version of your assignment, but no other formats will be accepted.
To emphasize: submission in R markdown format is mandatory. When I grade your homework, I will automatically parse your markdown code to extract your functions. I will run your functions with test data to confirm that they work and provide the correct results.
Late assignments will not be accepted unless you inform me of your circumstances before the assignment is due, and I grant you an extension. I will only grant extensions for serious medical or compassionate reasons. You will not receive an extension just because your computer fails or the network goes down at an inconvenient time.
There will be an inclass, closedbook, handwritten, midterm examination. This will test your understanding of the basic concepts. There will also be a takehome final project that will test your ability to put theory into practice.
For the project, you will work in groups of three to propose, design, and analyze a research topic that contains a significant data component and is applicable to your primary field of study. The project must use statistical methods that are taught in this course. Before you spend more than a few hours of work on your project, you must get my formal approval of your topic.
Your final grade will be determined by your performance in the homework, midterm examination, project, and spot quizzes, as weighted below. However, I reserve the right to “curve” the grades; i.e., to adjust the grades such that they follow the usual distribution at Stevens.
Lecture Outline
Topic  Reading  

Week 1  General statistical methods  Moore et al. (2017): Ch. 1 Moore et al. (2017): Ch. 2 
Week 2  Looking at Data. Descriptive graphical measures. Numerical measures. Sampling distributions.  Moore et al. (2017): Ch. 5 
Week 3  Maximum likelihood, Method of moments, Bayesian estimators. Applications to financial models.  Moore et al. (2017): Ch. 6 
Week 4  One variable statistical inference Confidence intervals and Testing Hypotheses on Population Means and Proportions  Moore et al. (2017): Ch. 7 
Week 5  Two Population tests for Means and Proportions  Moore et al. (2017): Ch. 8 
Week 6  Tests of Population Variance, Two Populations Review  Greene (2012): Ch. 12 Greene (2012): Ch. 13 Greene (2012): Ch. 14 Greene (2012): Ch. 16 
Week 7  Midterm Examination  
Week 8  Categorical Data Analysis. One and Two Way Tables. Goodness of Fit test. Independence Test.  Moore et al. (2017): Ch. 9 
Week 9  Regression Analysis. Least Squares Fitting. Analysis and Testing. Prediction. Multiple Regression. Confidence intervals ANOVA table, multiple R2, residuals  Moore et al. (2017): Ch. 10 
Week 10  Selection of variables. Correlation analysis, Variance inflation factors. Nonlinear regression. Generalized Additive Models.  Moore et al. (2017): Ch. 11 
Week 11  Analysis of variance (ANOVA) models. Applications. Expansion to mixture models Analysis of Covariance  James et al. (2013): Ch. 6 
Week 12  Logistic regression.  Moore et al. (2017): Ch. 12 Moore et al. (2017): Ch. 13 
Week 13  Intro to Risk measures: VaR, CVaR and CoVar Bootstrap Method and Permutation tests. Crossvalidation methods.  Moore et al. (2017): Ch. 14 
Week 14  Applications. Review and catching up  Moore et al. (2017): Ch. 16 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  
Web Campus 
Instructors
Professor  Office  

Ionut Florescu

ifloresc@stevens.edu  Babbio 544 
Course Resources
[FT] Florescu, Ionut and Tudor, Ciprian A. Handbook of Probability, Wiley, 2014, ISBN 1118593146, 9781118593141. I chose to use this book as the primary textbook for two reasons. Each chapter is supposed to be more or less self contained and it contains many details that I believe are useful. Second, each chapter has a section with exercises split into two, First set of problems have solutions while the second set does not. I will assign exercises that do not have solutions but you should be working through the ones that have solutions for practice. We will be using two other textbooks.
[G] Ghahramani, Saeed Fundamentals of Probability: with Stochastic Processes, Third Edition, Chapman and Hill/CRC, Nov. 2015, ISBN 9781498755016 This is an undergraduate textbook and it is very useful for those of you who did not do a serious probability class in undergraduate. The book explains very well the basic probability distributions and concepts. I will be using exercises from the book and you should use it as a source of material and problems. Finally,
[F] Florescu, Ionut Probability and Stochastic Processes, Wiley, Oct. 2014, ISBN13: 9780470624555, ISBN10: 047062455 This book has more material than the main textbook but it isn’t as detailed which is why I am using the handbook as the main text. However, this book has a second part about stochastic processes which is I believe very useful for future. I am referring in particular to Markov chains and Markov processes, Poisson process and the Brownian motion.
Grading
The final grade will be determined upon the student’s performance in the course. We will have multiple assignments and possibly quizzes throughout the course. Most of the grade will be coming from the in class midterm as well as from the final.
Only use the .pdf format when submitting files online. If specified in class you can turn in handwritten assignment in the traditional way. You should be able to transform any document into a pdf file. You can use Adobe Acrobat  should be free to Stevens students as far as I know (please call the students help desk), or a simple alternative: use a pdf printer driver. I write all my documents in LATEX and that typesetting program produces pdf files. A simple alternative (using any typesetting program): search on google for a driver that would print to a pdf file. Such drivers are generally free. Late assignments will not be accepted under any circumstances without prior notice and permission of the instructor. If outside circumstances are affecting your ability to perform in the course, you must contact your instructor before you fall behind. Generally the grade distribution follows the following percentages.
Lecture Outline
Topic  Reading  

Week 1  Axioms of Probability, Sample Spaces, Examples Combinatorial Analysis, Counting Permutations, Combinations, Binomial Coefficient  F1, FT1, G1,2 
Week 2  Conditional Probability and Independence, Law of Total Probability, Bayes Theorem, Applications  FT2, F2, G3 
Week 3  Random Variables: Generalities  FT3 
Week 4  Discrete Random variables, examples  FT4, G4,5 
Week 5  Continuous Random variables, Examples  FT5, G6,7 
Week 6  Generating Random variables. Catching up.  FT6, F3 
Week 7  MIDTERM  
Week 8  Random vectors, Joint distribution  FT7, F4 
Week 9  Conditional distribution, Conditional expectation  G8,9 
Week 10  Moment Generating Function, Characteristic Function  FT8,9,F6 
Week 11  Gaussian Random Vectors, Catch up  FT10 
Week 12  Statistical Inference, Limit Theorems  FT11,12, F7,8 
Week 13  Poisson Process and Markov Chain  F10,12 G12.2,12.4 
Week 14  Brownian motion  F15, G12.5 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus  X 
Instructors
Professor  Office  

Majeed Simaan

msimaan@stevens.edu  Babbio 514 
More Information
The objective of the course is to learn the main financial concepts and analytical tools in risk management. Lectures will be combined with discussions, inclass labs, and applied projects using real data and computations. As a result, the aim of the class is to help students to both think and act as professional risk managers. Additionally, the class aims to cover different topics from FRM Parts I and II, aiding the students to comprehend the exam materials from a deeper perspective.
Course Resources
Financial Risk Manager Handbook, + Test Bank: FRM Part I / Part II 6th Edition by Philippe Jorion
Risk Management and Financial Institutions (Wiley Finance) 4th Edition by John C. Hull
Practical Methods of Financial Engineering and Risk Management: Tools for Modern Financial Professionals by Rupak Chatterjee
The Essentials of Risk Management (McGrawHill) 2nd Edition by Michel Crouhy, Dan Galai, and Robert Mark
A Quantitative Primer on Investments with R by Dale W.R. Rosenthal
HANDOUTSA set of handouts/lecture notes will be given as the course progresses. These handouts will be very useful to conduct computations and address the underlying tasks from the mini projects. These, however, will only serve as a complement to the textbook and should not, by any mean, be treated as a substitute.
Grading
The following procedures apply to exams for this course. As the instructor, I reserve the right to modify any conditions set forth below by printing revised Exam Room Conditions on the exam.
Lecture Outline
Topic  Reading  

Week 1  Intro to Risk Management  Ch. 1 from Jorion Recommended: Ch.1 from Hull 
Week 2  Modeling and Simulation  Ch. 4 from Jorion Recommended: Ch. 7 from Hull 
Week 3  Modeling Risk Factors  Ch. 5 from Jorion Recommended: Ch.10 from Hull 
Week 4  Introduction to Bond and Interest Rate Risk  Ch. 6 from Jorion 
Week 5  Introduction to Derivatives  Ch. 7 from Jorion Recommended: Ch. 5 from Hull For further information on the OTC market refer to Ch 18 from Hull 
Week 6  Option Markets  Ch. 8 from Jorion Recommended: Ch. 8 from Hull 
Week 7  Midterm  
Week 8  Special Topic Session I  
Week 9  Managing Linear Risk  Ch. 13 from Jorion Recommended: Ch. 8 from Hull 
Week 10  Managing NonLinear Risk  Ch. 14 from Jorion Recommended: Ch. 8 from Hull 
Week 11  Advanced Risk Models  Ch 15 from Jorion Recommended: Ch. 12 and 13 from Hull 
Week 12  Credit Risk Management I  Ch 19 and 20 from Jorion Recommended: Ch 18 from Hull 
Week 13  Credit Risk Management II  Ch 21 from Jorion Recommended: Ch 19 from Hull 
Week 14  Special Topic Session II  
Week 15  Final Exam 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  
Web Campus 
Instructors
Professor  Office  

Jonathan Kaufman

jk2156@aol.com  Babbio 303A 
More Information
Notes
Course Resources
Paul Wilmott Introduces Quantitative Finance
Ferguson Ascent of Money, Wall Street Journal, Bloomberg
Grading
Lecture Outline
Topic  Reading  

Week 1  Introduction to course; What is FE?  
Week 2  Money, Credit  
Week 3  Insurance, Management Securitizatio  
Week 4  Instruments; Bonds, Stock, Derivatives  
Week 5  Commodities, Real Estate, Pools  
Week 6  Tangible, Intangible, Contracts  
Week 7  First project idea  
Week 8  Midterm Exam  
Week 9  Terms of Instruments  
Week 10  First presentations  
Week 11  First presentations  
Week 12  Pricing of instruments  
Week 13  Second presentations  
Week 14  Second presentations 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus 
Instructors
Professor  Office  

Thiago Winkler

twinkle1@stevens.edu  Babbio 109 
More Information
This course is designed for both graduate and undergraduate students. It aims to be an introduction to the C++ programming language, as well as to programming in general, including topics such as objectoriented programming and generic programming, with some basic applications in finance. No prior programming experience is required. Upon completion, students are expected to have proficient programming skills in C++ and to be able to apply these skills in any future courses and/or industry positions.
Course Resources
Programming: Principles and Practice Using C++. Bjarne Stroustrup, Second Edition, 2014. ISBN10: 0321992784, ISBN13: 9780321992789
C++ Reference: http://en.cppreference.com/w/cpp
Grading
Lecture Outline
Topic  Reading  

Week 1  Programming and “Hello, World!”  Ch. 1&2 
Week 2  Objects, Types, and Values & Computation  Ch. 3 
Week 3  Lab Problem 1 Computation  Ch. 4 
Week 4  Errors,Writing a Program  Ch. 5  6 
Week 5  Lab Problem 2, Completing a Program  Ch. 7 
Week 6  Technicalities: Functions, etc. Technicalities: Classes, etc.  Ch. 89 
Week 7  Lab Problem 3, Input/Output Streams  Ch. 10 
Week 8  Assignment 1 Review, Midterm Exam  
Week 9  OOP: Encapsulation, OOP: Inheritance  
Week 10  Lab Problem 4, OOP: Polymorphism  
Week 11  Monte Carlo, Vector and Free Store  Ch. 17 
Week 12  Lab Problem 5, Vectors and Arrays  Ch. 18 
Week 13  Vector, Template, and Exceptions, Containers and Iterators  Ch. 1920 
Week 14  Lab Problem 6  
Week 15  Assignment 2 Review Final Project Presentations 
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  X 
Web Campus  X  X  X 
Instructors
Professor  Office  

Dan Wang

dwang35@stevens.edu  Babbio Center 209 
Zhiyuan Yao

zyao9@stevens.edu  HFSL Research Room 
More Information
This course is designed for those students have no
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  X  X 
Web Campus  X  X  X 
Instructors
Professor  Office  

Dan Wang

dwang35@stevens.edu  Babbio Center 209 
Zhiyuan Yao

zyao9@stevens.edu  HFSL Research Room 
More Information
This course is designed for those students have no experience or limited experience on Python. This course will cover the basis syntax rules, modules, importing packages (Numpy, pandas), data visualization, and Intro for machine learning on Python. You will need to implement what you learn from this course to do a finance related project. This course aims to get you familiar with Python language, and can finish a simple project with Python
Course Resources
Dive into Python, http://www.diveintopython.net
Python for Data Analysis, Wes McKinney, O'Reilly Media, 2012
Python for Everyone, https://www.py4e.com/
Python 3 Object Oriented Programming, Dusty Phillips, Packt Publishing, 2010.
Python for Finance  Analyze Big Financial Data, Yves Hilpisch, O'Reilly Media, 2014
Grading
Lecture Outline
Topic  Reading  

Week 1  Installing Python and IPython Notebook  
Week 2  Basic Python Language I  Homework 1 
Week 3  Basic Python Language II  
Week 4  Basic Python Language I  Homework 2 
Week 5  Intro to useful standard library  
Week 6  NumPy Basics  Homework 3 
Week 7  Getting Started with pandas  
Week 8  No Class due to Columbus Day  
Week 9  Pandas II  Homework 4 
Week 10  Plotting and Visualization  
Week 11  Time Series  Homework 5 
Week 12  Financial and Economic Data Applications  
Week 13  Introduction to Machine Learning I  Homework 6 
Week 14  Introduction to Machine Learning II  
Week 15  Final Presentation 
Course Catalog Description
Course Catalog Description
Campus  Fall  Spring  Summer 

On Campus  X  
Web Campus 
Instructors
Professor  Office  

Dragos Bozdog

dbozdog@stevens.edu  Babbio 429A 
More Information
Main topics include: Mathematica Language: Lists, Patterns and Rules, Functional and Procedural Programming, Graphics and Visualization, Dynamic Expressions and Optimizing Mathematica Programs Mathematica Finance Applications: Cash Account Evolution, Stock Price Evolution European Style Options, Stock Market Statistics, Implied Volatility for European Options, American Style Stock Options, Optimal Portfolio Rules, Advanced Trading Strategies
Course Resources
Stojanovic, S., Computational financial mathematics using MATHEMATICA: optimal trading in stocks and options, Boston: Birkhäuser, 2003. (ISBN: 978‐0‐8176‐4197‐9)
Wellin, P. Programming with Mathematica®: An Introduction, 4th Revised edition, Cambridge University Press, 2013. (ISBN: 978‐1107009462)
Grading
Lecture Outline
Topic  Reading  

Week 1  Introduction to Financial Engineering  Ch. 1&2 
Week 2  Capital Markets Overview  Ch. 3 
Week 3  Corporate Finance & Valuation  Ch. 3 
Week 4  Equity Analysis  Ch. 4 
Week 5  Fixed Income Debt Securities  Ch. 4 
Week 6  Overview of Bonds Sectors & Instruments  Ch. 4 
Week 7  Valuation of Debt Securities  Ch. 4 
Week 8  Securitized Products  
Week 9  Leveraged Loans & CLO's  Ch.5 
Week 10  General Principles of Credit Analysis  Ch. 5 
Week 11  Foreign Exchange  Ch. 6 
Week 12  Poisson Processes and Jump Diffusion  Ch. 11 
Week 13  Exotic Options  Ch. 7 
Week 14  Review & Catchup 
To reserve the Conference room please send an email to fscadmin@stevens.edu
]]>To reserve the Conference room please send an email to fscadmin@stevens.edu
]]>To reserve Hanlon 1 or 2 for an event please email to fscadmin@stevens.edu at least two weeks in advance. Please send an email about reserving the lab for a course at least a month before the semester starts
]]>To reserve Hanlon 1 or 2 for an event please email to fscadmin@stevens.edu at least two weeks in advance. Please send an email about reserving the lab for a course at least a month before the semester starts
]]>