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Python is the fastest growing programming language globally, mainly due to its simplicity, large community, and wealth of application; including data science and quant finance. Though VBA is more immediately applicable for Excel users, and therefore still very relevant, Python is more globally scalable in terms of its possibilities. Python is faster at complex calculations, can handle huge datasets (big data), has more packages (libraries), and can be used to build standalone or web-based applications, as well as interact with Excel if required. Python is also much friendlier and easier to learn than C++, thus is viewed as a language suitable for beginners and advanced programmers alike.
The objective of this course is to get comfortable with the main elements of Python programming. Day one is about acquiring a suite of Python language skills via small exercises concerning stock ticker lists, client databases, basic statistics and other fun topics such as email address, usernames and passwords. Day two further applies python and python packages to the world of finance. On this day, we conduct some data science projects. We specifically aim to import, clean, enrich, transform, visualize and output the analysis of a large dataset of IMF World Economic Data, as well as build a simple Loan Calculator to demonstrate the usefulness of GUIs.
Key Learning Outcomes:
- Learn to write, test and debug Python 3 code with confidence, including working with Containers, Conditionals & Loops, Functions & Modules and Error Handling.
- Learn the fundamentals of some of the most widely used Python packages; including NumPy, Pandas and Matplotlib, then apply them to Data Analysis and Data Visualization projects.
- Build and code a Graphical User Interface (GUI) to run a program.