This course focuses on developing practical and conceptual skills for advanced data analysis in scientific research. Students will work primarily in Python, though the use of other programming environments is welcome. The emphasis will be on understanding data at a deeper level: moving beyond routine processing to extracting meaningful physical and scientific insights. Core numerical techniques will be introduced and applied, including differentiation, integration, curve fitting, and frequency-domain analysis. Structured test cases will be provided, and students will learn to approach them systematically, from data preprocessing to interpretation of results. In addition, open-source datasets will be used to explore real-world trends and to assess the effectiveness of different analytical approaches. The course encourages active discussion, critical thinking, and comparison of methods, with an open forum for exploring numerical techniques and their appropriate application across scientific problems.

Python is now being used for basic to advanced operations in academic and industrial workspace. In combination with unix and/or windows OS and other programming languages python can be extensively used for automation, data acquisition and post processing. I teach python from a beginners to advanced level depending on the requirements of the student. No prerequisite for computer programming is required to start. A long term training plan can be established to develop all round computer programming skills with software engineering abilities. Teaching technique : Digital board for concept delivery, active programming training with real world problems. Help in setting up programming environments and git. Homework : Task of about 10 minutes after every class to be done privately. Review of homework during the next class.