The module can be seen as a collection of standalone notebooks, each one covering a more niche topic in data science. The notebooks provide in-depth explorations of specific analysis topics that students are generally expected to "know beforehand" or pick up on the fly.
The first notebook covers Exploratory Data Analysis, a technique used to quickly gain a broad understanding of a (typically unfamiliar) dataset as a means to inform and guide future analyses.
The second notebook explains and investigates four common preprocessing techniques used in machine learning (Minmax, Standard, Robust, and Quantile scaling).
After completing this module, students should feel comfortable:
- performing short, spontaneous analyses to familiarize themselves with a new dataset
- choosing a preprocessing technique for a given dataset before running a more intensive machine learning analysis on it
- using built-in functions from
numpyandscipyto preprocess data before running a more intensive machine learning analysis on it
Students are expected to be familiar with python, numpy, and matplotlib.
Two in-class notebooks WILL guide students through various preliminary topics in data analysis. The notebooks will cover:
- Exploratory data analysis (
01_dataset_cleaning.ipynb) - Preprocessing techniques for neural networks (
02_preprocessing_techniques.ipynb)
Estimated time to complete: 30-45 minutes per notebook
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Two datasets are provided to accompany the notebook 01_dataset_cleaning.ipynb.
This module would be well-suited to accompany a general course in data science techniques for the physical sciences (or a specific subfield of a physical science). The first notebook in particular would be very apropriate as a first homework assignment for any laboratory course.
- Possibly adding in a quiz module
- Possibly adding in a HW module
- Remove some of the code to students have something to do