Introduction to Data Science

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Related lectures (32)

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PyTorch and Convolutional Networks

Covers PyTorch tensor data structure and training a CNN to classify images.

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Structures and Mechanisms: Opening a Box

Explores the analysis of structures and mechanisms through a sample problem of opening a box with a string-held lid.

Statistical Signal Processing

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

3D Stone Scanning Session

Introduces a 'professional' 3D measurement system for stone analysis and feature extraction using stereo photogrammetry and structured light technologies.

Gitlab Agent for Kubernetes (`agentk`)

Covers the setup of a Gitlab agent for Kubernetes, focusing on installation, version control, and troubleshooting.

Total scattering and PDF analysis

Explores total scattering and PDF analysis in materials science, covering in-situ synthesis, data analysis techniques, and applications in host-guest systems.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

Covers the essentials of Data Science using Python, Numpy, Pandas, and Scikit-learn, including DNA sequence analysis and classification.