Course

ENG-209: Data science for engineers with Python

Lectures in this course (31)

Data Structures: Tuples, Lists, Sets, Dicts

Introduces Python data structures like tuples, lists, sets, and dictionaries, emphasizing manipulation and conversion techniques.

Data Science with Python

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

Untitled

Advanced Pandas Functions

Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.

Data Science with Python: Numpy Basics

Introduces the basics of Numpy, a numerical computing library in Python, covering advantages, memory layout, operations, and linear algebra functions.

Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Data Structures: Tuples, Lists, Sets, Dicts

Introduces data structures like tuples, lists, sets, and dicts in Python, covering their definition, manipulation, and iteration.

Untitled

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Data Science with Python: Modules and Numpy

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

Python Modules and Pandas

Introduces Python modules, scoping, lambdas, and pandas for data manipulation and analysis.

Scopes and Lambdas: Data Science with Python

Covers scopes, lambdas, and pandas in data science with Python, including nested declarations, scoping, assignments, and pandas manipulation.

Decision Tree Classification

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

Data Science Essentials: Pandas, Numpy, Matplotlib

Introduces Pandas, Numpy, and Matplotlib for data analysis and visualization in Python.

Data Science: Python for Engineers - Part II

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Linear Regression: Fundamentals and Applications

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Regression Models: Performance and Evaluation

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.

Polynomial Regression and Gradient Descent

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Python for Engineers: Basics and Functions

Covers Python basics, functions, and practical applications for engineers, emphasizing data manipulation, functional programming, and data structures.

Linear Regression: Basics and Applications

Introduces linear regression, from history to practical applications, including model building, prediction, and evaluation.