Introduction to Python in Finance

At Bloomberg LP, we’re committed to making the world of finance and news more accessible by providing targeted support for academic achievement and career success - connecting talented young people with Bloomberg’s employees, expertise and opportunities in offices around the world to inspire and empower students to consider careers in finance by learning about new concepts and expanding their professional networks through industry specific networking opportunities.
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Date: 23 March 2023
Time: 3:00 pm - 6:00 pm
Venue: Bloomberg Singapore Office – Training Room 2
Students: Max 16 Female Students at University or Master’s level. Background in Python or coding isn’t necessary.

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Deadline: 20th March 2023. Limited space available

(Terminology legend provided below for terms in italics) ​

Time	Further Details
3:00 – 3:15 pm	Opening by Co-Lead of Bloomberg Women in Technology - Connie Phoon, Enterprise Quant Solutions, ASEAN, and India
3:15 – 3:35 pm	Welcome and Introduction to Bloomberg & Python Students are introduced to the Bloomberg world of finance, news, and technology - our product, our people, our culture of innovation and values. Introduction to why people use python, how students can leverage on BQNT for analysis.
3:35 – 4:00 pm	Basics of Python ● Students are introduced to the basics of python ● Python types: Strings, Floats, Booleans, List, Dictionaries ● Pandas to manipulate data
4:00 – 4:30 pm	Pulling API Data with Python ● Students are introduced to the concept of API, BQL ● Equity market monitoring data ● Equity fundamentals ● Fixed Income market monitoring ● Universe management
4:30 – 4:45 pm  ​	Pantry & Washroom Break
4:45 – 5:45 pm  ​	BQNT Exercise ● Create a small project to explain the stock market
5:45 – 6:00 pm  ​	Networking Session and Q&A  ​

Terminology Legend: 
API - API is the acronym for Application Programming Interface — a software intermediary that allows two applications to talk to each other; allowing us to extract and share data within and across organizations.

BQL - BQL enables users to perform custom calculations directly in the Bloomberg Cloud. Analyses that previously required you to download thousands of data points and perform complicated Microsoft® Excel manipulation now require only one formula.

BQNT - BQuant is a JupyterLab-based development environment that allows a user to rapidly analyze data and evaluate trading strategies. It is designed to support the needs of quantitative users who want to evaluate hypotheses, create interactive visualizations, and publish these to other Bloomberg users. 
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Pandas - Pandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data. 

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