MFIM 651 Python for Data Analysis

This course is designed for those students have no experience or limited experience on Python. This course will cover the basis syntax rules, modules, importing packages (Numpy, pandas), data visualization, and Intro for machine learning on Python. You will need to implement what you learn from this course to do a finance-related project. This course aims to get you familiar with Python language, and can finish a simple project with Python. You will learn how to write computer programs in Python language to solve real-world problems and how to explain your results as a report in a more readable way. This will be useful in your research and your jobs in the future. This class is designed for students that want to learn to computer programming for data science. This course guides students through the basic Python programming language, from initial concepts to final data analysis using python and external packages. Students will finish the class with a basic understanding of how to execute predictive analytic algorithms. Students will also have a good sense for how to evaluate and test their predictive models.

 

Learning Goals

1. Basic data processing and handling with Python/Pandas
2. Testing and evaluating forecasts/predictions (cross validation)
3. Presenting/describing results (graphics)
4. Implementation of machine learning algorithms
5. Testing and evaluating forecasts/predictions (cross validation)
6. Understand the architecture, basic elements, and the planning of data science
7. Understand and use off-the-shelf packages to solve business related applications, such as resource allocation, finance, accounting, information system management, and many others
8. Understand how to manipulate data (store, query, and summarize) using database for analyzing structured data
9. Understand and use computer programming for basic interactive web applications
10. Understand and use computer programming to collect, analyze, and visualize business data

Offered

Spring/Fall