Fast and Effective Analytics for Big Data: Multi-Dimensional Insights

Related lectures (32)

Page 3 of 4

Introduces Python programming concepts, focusing on dictionaries and classes, including their definitions, usage, and practical examples.

Introduces writing formats and decoding strings using Matlab/Octave, covering various data types and GUI programming.

Covers the basics of C programming, including syntax, data types, pointers, arrays, and compilation commands.

Introduces the basics of programming with a focus on dictionaries and classes in Python.

Explores data types in C/Arduino microcontroller programming, covering boolean, byte, char, word, arrays, and memory allocation.

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Explores graph mining in social networks, covering data analysis and community detection.

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.