ENG-209: Data science for engineers with Python

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Lectures in this course (31)

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Multivariate Polynomial Regression

Covers multivariate polynomial regression to predict sound velocity in water based on measurements.

Data Structures and Functions

Covers the basics of tuples, lists, sets, linear types, conversions, dictionaries, and defaultdicts.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Logistic Regression: Cost Functions & Optimization

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Named Tuples and Dataclasses

Introduces named tuples and dataclasses in Python, emphasizing their flexibility and ease of use for data manipulation.

Python Modules and Functions

Covers Python libraries, modules, scoping, functions, lambdas, and practical examples.

Understanding ROC Curves

Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.

Advanced Pandas Functions

Covers advanced functions of Pandas, focusing on filtering, labeling, and manipulating dataframes.

Python Basics: Syntax, Variables, Functions

Covers Python basics such as syntax, variables, and functions, introducing the Renku platform for collaborative data science.

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