Courses

Get started with SQL for data analysis. Learn to query databases, filter results, perform calculations, and combine tables – essential skills for any data scientist.

Prerequisites: No prerequisites

Level: Beginner

Learn the basic concepts of databases, including relational models, SQL queries, and NoSQL alternatives.

Prerequisites: No prior knowledge required

Level: Beginner

Gain foundational skills in Python data visualization. Learn to use Matplotlib and Seaborn to create clear, effective plots like line charts, bar graphs, histograms, and scatter plots from your data.

Prerequisites: Basic Python programming skills (variables, data types, loops, functions, importing libraries). Familiarity with NumPy and Pandas is helpful but not strictly required.

Level: Beginner

Start your path in data science. Learn foundational concepts, data handling, basic analysis techniques, and visualization principles.

Prerequisites: No prior knowledge required.

Level: Beginner

Master the essential first steps in any data project: cleaning and preprocessing. Understand how to identify and fix common data problems like missing values, duplicates, and incorrect formats.

Prerequisites: No prerequisites

Level: Beginner

Understand how computers 'see'. This course covers digital image fundamentals, basic image processing techniques, feature detection, and the concepts behind object recognition.

Prerequisites: No prior knowledge required

Level: Beginner

Learn the fundamentals of machine learning, including core concepts, common algorithms like regression and classification, and basic data preparation techniques.

Prerequisites: No prior knowledge required

Level: Beginner

Grasp the core concepts behind Large Language Models (LLMs), understand their basic mechanics, and learn how to interact with them through prompts.

Prerequisites: No prior knowledge required

Level: Beginner

Understand how to take a trained machine learning model and make it available for predictions in a real-world setting. Learn about model serialization, simple web APIs, and containerization basics.

Prerequisites: Familiarity with Python programming and basic machine learning concepts (model training) is helpful but not strictly required. Explanations start from fundamentals.

Level: Beginner

Understand how to measure the performance of machine learning models using standard evaluation metrics for classification and regression.

Prerequisites: No prior knowledge required

Level: Beginner