The course is for students who do not have programming experience or knowledge of machine learning, and who want to acquire data analysis skills using data science. The course introduces the implementation of basic machine learning methods and deep learning methods.

This course explains an introduction to data science, data classification methods using machine learning, regression algorithms, and clustering methods for graduate students majoring in chemistry.

The course introduces the basics of bioinformatics and various algorithms for sequence alignment to graduate students specializing in life science and graduate students who are specializing in information science and also interested in life science.

The course introduces the basics of Python programming and essential commands and syntaxes, such as list, dictionary, class, and function, to learners interested in big data processing. It is tailored for those specializing in data analysis and those keen on understanding the foundational Python concepts for handling large datasets.

The course introduces the fundamentals of data management using Python to individuals keen on big data processing. Designed for learners with an interest in data science, it covers writing and utilizing data handling with pandas, visualization techniques with matplotlib, foundational data analysis, and machine learning approaches. Special emphasis is placed on the core elements of pandas and sklearn libraries, catering to those specializing in large dataset processing.

This course delves into Big Data management using Python in a remote workspace. Tailored for enthusiasts of large-scale data operations, we explore big data strategies, the nuances of remote systems, and terminal interfaces. We emphasize mastering remote access tools and understanding job scheduling systems. Designed for those keen on handling vast data repositories, our curriculum blends foundational knowledge with hands-on collaboration, ensuring a comprehensive learning experience in data processing and remote system utilization.

The course offers a practical guide to typesetting with LaTeX for academic purposes. Geared towards individuals aiming for professional document presentation, it covers topics such as text formatting, page design, and the inclusion of lists, images, and tables. Additionally, it delves into cross-references, reference listings, writing math formulas, and handling large-scale documents. A particular focus is placed on refining and elevating the overall quality of academic papers.

The course delves into the foundational principles of quantum computing and quantum mechanics, targeting students who have a basic to beginner-level understanding of computer science. Focused on making quantum theory approachable, the curriculum explores key phenomena like superposition and entanglement, which form the backbone of quantum computing frameworks. Special attention is given to essential terminologies in quantum mechanics, presented in a simplified manner to facilitate comprehension. Prior knowledge of vectors and matrices is assumed for effective learning.

This tutorial covers basic concepts and tools in 3D graphics with the help of the open-source software Blender. Whether you are looking for a way to visualize your research results, starting new research in 3D graphics, or just want a new hobby, we have something to offer for everyone.