Data Science Essentials: Pandas, Numpy, Matplotlib

Related lectures (32)

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

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

Introduces data science topics, evaluation methods, and course organization for the semester.

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Covers elementary numerical methods and algorithmic thinking using Python for scientific computing.

Covers the basics of Matplotlib for 2D graphic representation in Python, including plotting data and visualizing functions.

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Covers linear regression training to find the best line for given data points, essential for predicting house prices.

Explores EPFL's doctoral theses history, data visualization, and the innovative D3 approach.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Covers the transition to Python and Jupyter notebooks for Numerical Analysis at EPFL, including interactive exercises and graded tests.

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

Page 2 of 2