Course for Data Science Basics with Python

• Introduction to the fundamental concepts of artificial intelligence and the KDD (Knowledge Discovery in Databases) process.
• Introduction to Basic Statistics: Univariate and bivariate statistics, as well as multivariate statistics.
• Understanding Data, Types, and Formats: Exploring structured, semi-structured, and unstructured data, along with various real-world data formats.
• Data Preprocessing: Techniques for detecting missing values, normalization, scaling, and encoding to prepare data.
• Supervised and Unsupervised Machine Learning: An overview of classification, regression, and clustering algorithms, supported by practical examples.
• Introduction to Python Libraries for Machine Learning.
• Evaluation of Machine Learning Models: Methodologies for measuring accuracy, precision, and other metrics, along with a critical analysis of models.
• Visualization of Results: Creating graphical representations to interpret and communicate model outcomes.

Upon completion, students will be capable of collecting, analyzing, and preprocessing data, building and testing machine learning models, and visualizing results, thereby enabling informed, data-driven decision-making.

• Python
• Spyder
• Jupyter Notebook

All required tools are free, and instructions for downloading and installing them will be provided at the start of the program.