Data Science for Engineers: Part 2

Related lectures (31)

Covers Python basics, Pandas, data manipulation, visualization, and machine learning.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Introduces setting up the environment, Git basics, and Jupyter Notebook features with practical examples.

Covers rhythm generation techniques, including Markov models and hierarchical rhythm generation, with a focus on Nancarrow's Study 14.

Explores dictionaries, tuples, mutable objects, and variable-length arguments in Python.

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

Covers the basics of creating and customizing charts in Excel.

Introduces Python modules and NumPy for efficient array operations and linear algebra in data science.

Page 2 of 2