Big Data and Dealing with Data in Finance

Bio Overview Takeaways Materials Grading Help Assignments

Course code: XFN1-GB 8107 83

This course will help you learn how to program so that they can effectively retrieve, store, manipulate, and visualize data. You will develop practical programming and data manipulation skills using Python. Python is a beginner-friendly programming language that is widely used in industry. In recent years, it has also become the de facto standard in data science. The courses focuses on practical tasks when dealing with data using Numpy and Pandas; obtaining data from online sources via APIs and web scraping; plotting using Matplotlib, and writing and reading data to/from files (CSV and Excel files).

Overview

The course teaches you how to manipulate and analyze financial data in Python using professional tools. While no prior programming/Python experience is assumed, it does involve coding.

The course covers the following skills:

Structured thinking about financial analysis tasks so that you can automate them using organized and maintainable code.
Automating financial data input and output by interacting with financial statement data in Excel, SQL, and CSV formats.
Financial data analytics for exposure to data analytics packages.

Takeaways

Structured thinking

How to think about analytical tasks in an organized and structured manner to automate them using Python
Using design concepts such as DRY (Do not repeat yourself) and Single Source of Truth (SSOT)

Automating financial data input and output

Interacting with web-based financial data via API
Automating creation and retrieval of Excel data
Visualizing financial data

Financial data analytics for exposure to data analytics packages

Time value of money, Fixed income securities, Loan amortization
Marginal and average tax rates
Portfolios and efficient frontiers
Building blockchains and cryptomining

Materials

I use my materials. Therefore, no textbook is required, and you need not purchase anything. Please check Brightspace for details regarding access to class files on NYRCN (NYU Remote Computing Nework).

Exams and Grading

There are no in-class quizzes, midterms, or final exams.

Final project: 100%

System Requirements

You need to be in the following systems before the start of the first class:
- Albert
- NYU Brightspace
- NYU RCN

Help and Office

Me: dgode@stern.nyu.edu, Office: KMC 10-86.
Teaching assistant: Please check NYU Brightspace.

Assignments

Online assignments: Check Brightspace

MS in Fintech Big Data & Dealing with Data in Finance