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Learn to harness Stream API in Java for efficient and elegant data processing. Explore its architecture, key principles, and functional interfaces, along with powerful operations like filtering, mapping, sorting, and collecting results. Discover advanced techniques for performance optimization, error handling, and parallel processing to apply Stream API effectively in real-world scenarios.

This course is designed to teach you the very basics of pandas - a must-have Python library for every data analyst or data scientist (and not only). You will start from defining the basic objects, learn how to read data from files, and how to manipulate and aggregate loaded data.

Coding with AI is a hands-on course designed for professionals eager to enhance their everyday work processes without delving into full-scale programming. This course equips you with practical coding techniques and AI tools to automate repetitive tasks, seamlessly convert and organize documents, and perform advanced data analysis. Learn how to create dynamic reports from CSV files, extract critical information from PDFs, auto-categorize files, and even integrate live data feeds like currency updates—all through intuitive, step-by-step projects. With real-world examples and a focus on immediate applicability, Coding with AI empowers you to boost productivity and make smarter decisions with minimal technical background.

This course guides you through analyzing real-world health data to uncover key diabetes risk factors and inform targeted public health interventions.

This project introduces core techniques in environmental data analysis, focusing on sensor data cleaning, descriptive statistics, and short-term air quality trends.

In this project, we study a dataset in which the external characteristics of mushrooms are presented, and with their help, we will predict whether the mushroom is poisonous

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This course on Python requires zero prior knowledge on programming. It uses simple examples and covers same key concepts with greater level of detail in each subsequent section. Enough said, click start and beging coding!

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Despite its seeming limitations, linear regression allows us to approximate not only linear functions, but also square, cubic and other polynomials. We also can add more functions to the basis and approximate, for example, functions with trigonometric expressions, roots, exponents and so on.

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