GitHub Repository for the CAS Applied Data Science

Below you will find an overview of the different modules which will be part of this CAS program

Module 1: Data Acquisition and Management

Learn to understand** different data sources and types** and how to design data management models and plans

Module 2: Statistical Inference for Data Science

Gain basic understanding of statistical modules used for analysis and descriptive statistics

Module 3: Data Analysis and Machine Learning

Overview of machine learning pipelines and their implementation with scikit-learn
Regression and** Classification**: linear models and logistic regression
Decision trees & random forest models
Principal component analysis (PCA) and non-linear embeddings (t-SNE and UMAP)
Clustering with K-means and Gaussian mixtures
Artificial Neural networks as general fitters, fully connected nets used to classify the fashion-MNIST dataset
Scikit-learn and clustering maps, Q&A

Module 4: Ethics and Best Practices

Create GitHub repository for your CAS material and projects
Document repository and subfolders with Readme files

Module 5: Peer Consulting and Selected Readings

Peer knowledge exchange and consultation groups
Discussion and Collaboration with peers on key concepts and practical applications

Module 6: Deep Learning

TensorFlow for deep learning applications