FIN312 Mathematics and Programming for FinTech Assignment Sample SUSS Singapore

The FIN312 Mathematics and Programming for FinTech course aims to equip students with suitable mathematical, computational skills that will allow them to measure data; thus providing solutions to problems arising in both conventional finance as well as the digitalized world. The ability to interpret models mathematically and translating those into programming languages is a valuable, repeatable skill needed in today’s technologically advanced society.

The course will use a single, well-known programming language throughout to help students pick up knowledge around the art of translation as well as efficiently handle financial data. They’ll also learn about reasoning with it using various scenarios from finance like risk and return or FinTech.

TOA, TMA, GBA Assignment sample of Mathematics and Programming for FinTech module SUSS Singapore

At the end of this course, Singaporean students will be able to learn Mathematics and Programming for FinTech module with the help of the following learning outcomes:

1. Distinguish between structured and unstructured data used commonly in financial applications.

Structured data is traditional financial application use of data that is concise and fits a particular format. It also has a standard for storing the information used in the application, with a system of record to designate where it can be found.

Structured data consists primarily of numbers or values that are arranged into columns and rows that correspond with each other on spreadsheets, tables, diagrams, models, charts as well as databases.

In the FinTech space, unstructured data has become much more prevalent in the form of financial news articles in various languages all over the world or social media posts by companies and their customers which could influence how they manage their business affairs.

This variety is significantly harder for humans to make sense of as compared to structured datasets like spreadsheets and databases, hence the use of Machine Learning (ML) techniques such as Natural Language Processing (NLP). NLP allows computers to interpret human language through a set of algorithms that can measure words’ importance within documents based on context and user-defined settings.

2. Formulate statistical models to represent financial data.

Financial data typically possesses unique characteristics that set it apart from other data. For example, financial data is extremely scarce in proportion to the other types of datasets commonly found in statistics texts, even though many practitioners use statistical models to represent it. Statistical techniques like probability distribution and regression are used for modeling financial datasets.

The key advantage of these techniques is their power to model vast ranges of variability; while most datasets can be classified under similar distributions or modeled with basic regression equations, the natural variation associated with financial data speaks for itself in terms of its applicability to these techniques.

It’s worth noting that probability distribution models are ill-suited for “fat-tailed” distributions where outlying observations happen all too frequently, but they’re ideally suited for cases where we’re unsure of how to represent the data.

3. Appraise cryptographic primitives underlying security in cryptocurrency networks and calculate network statistics regarding the systems.

Cryptographic primitives are integral to the strength of security in cryptocurrency networks. The only way that cryptocurrencies can process transactions without a central institution verifying them is through public-key cryptography.

With public-key cryptography, different private keys are matched to different public keys and allowing the same person or entity who owns one of these pairs to conduct many secure transactions with multiple recipients. The sophisticated math behind cryptographic primitives makes it impossible for people, entities, or things to infiltrate them without revealing their identity thus making them extremely secure.

It is important not only to know how these systems work but also to understand their limitations in order to make an informed decision when handing money over for transfer on a network such as Bitcoin (BTC).

3. Implement Python programs to acquire financial data using APIs.

Instructions to implementing Python programs using APIs which will acquire financial data.

First, connect to the desired API. These are some common examples for each type of code that wants to use an API.

1) Django/Python on Google Finance API module.

2) .NET on Microsoft Stock Quotes Web Service REST Client.

3) Matlab on Yahoo Finance Data Restful Services.

4) R on Google Fundamentals by Fundations RESTful services 5) JavaScript in JsAstronomy.

6) Mathematica in FGNaviSeries via Time-series library functionality what exports fund prices for you during specified time span based upon user input range and calculates profit based upon initial investment.

4. Use Python to automate large-scale financial calculations.

Interesting question. In order to automate large-scale financial calculations, you will first need to create an algorithm that is written in a programming language such as Python. With the help of the library NumPy and a graphical user interface toolkit like TkInter or PyQt4, one can easily set up a program that accepts input from the user, runs their calculation algorithm on secure data sets, and outputs results back to them.

However, I’m dubious about whether this would be practical for real purposes The amount of time required for developing such a program could be significant especially if your needs are more complex than basic calculations so you might want to consider outsourcing this task instead.

The other primary hurdle is software authentication after you have the “backend” of your program running. In order to run code on a computer, you need an authentication key or some sort of software protection so that unauthorized users can’t get into the system and do damage.

5. Operate a financial information system for obtaining market data and information.

Financial Information Systems are often seen as the engine driving an organization’s competitive edge. With the help of basic computer skills, knowledge, and the ability to recognize financial concepts, one can navigate their way around a Financial Information System.

A Financial information system is usually rolled out with three main parts. The data input system is used for capturing data from different sources both manually and automatically captures sensitive financial events at regular/irregular intervals.

There is an interpretation of the gathered data using sophisticated software tools that are available in ROSCA’s dashboards plus intelligence into relevant macroeconomic factors propelling or restraining demand at chosen verticals linked to IAAS. And finally, insights are made available through reports that enable decision-makers to make informed decisions quickly by supplying them with the relevant data and information they require in a format most suited for their needs.

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