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Glass work by Chiuli @ Chiuli Garden Museum, Seattle, by mrspatbile
This course is designed for Master's students in Finance to prepare them to develop their own Data Analytics applications using Python. In addition to live, code-along lectures, the course features remote activities where students engage in real-time online interaction with me.
Learning Experience
The course is structured in two main pillars: strenghtening of foundational concepts and live coding sessions. Topics are distributed over data analytics methods, coding principles, and machine learning algorithms, all presented with rich visual animations for easy understanding and retention. This pedagogical approach is suited for audiences of various skill levels.
Visual
boosting trees
pruning a tree
neural networks
Hands-on
Colab Notebooks, Instructional Videos, Recorded Lectures & Assignments
Selected Topics Covered
Fundamentals
Built-in, core and third party libraries
PEP8 and pythonic notation
DRY coding (vs. WET)
Primer on BASH scripting
Data Viz: state based, object oriented, interactive plots
APIs Essentials
Web request-response
JSON manipulation
API developer documentation: key generation, data exchange formats, rate limits
NLP and AI
Text manipulation
Sentiment analyisis and asset pricing
Primer on machine learning & deep learning with tensor flow and keras
New feature: LLMs
Student Evaluation
The largest component of the evaluation is an applied group project. Other elements that contribute to the final grade: class participation, remote work and student growth.
When and Where
M.Sc. Finance & Economics - Digital Transformation in Finance track
2021 and 2022
University of Luxembourg, Kirchberg Campus
A advanced version of this course is scheduled to be delivered in other Business Schools in the year 2024.
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