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

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Module 2: Statistical Inference for Data Science

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

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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

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Module 4: Ethics and Best Practices

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

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Module 5: Peer Consulting and Selected Readings

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

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Module 6: Deep Learning

• TensorFlow for deep learning applications