Introduction to data analysis

This covers the data analysis semester of the seminar Applied Numerical Computing II offered in Mechanical, Industrial and Aerospace Engineering at Concordia University.

During this one semester seminar, targeted to an audience with engineering background but little to no experience in modern data processing, we give a first introduction to some selected concepts of data processing.

Topics

1. Development environment: JupyterHub and GitHub
2. Introduction to Python: functions, classes
3. Introduction to data processing: NumPy, Pandas, DataFrames, MatPlotLib
4. Regression models: Linear and multi-linear regression
5. Model selection and regularization: over/under fitting, re-sampling methods
6. Classification models: K-nearest neighbors, support vector machines, logistic regression
7. Tree based methods: Decision trees as classifiers/regression models, boosting, random forests

Development environment

Use either

• JupyterHub server from the Gina Cody School of Engineering
• Jupyter/JupyerLab installed on your machine
• Google Colab
•