This repository offers a structured, hands-on introduction to data manipulation using Python, aimed at learners who want to build foundational skills in handling and transforming data. It uses Jupyter Notebooks as the primary format for instruction, allowing for an interactive and exploratory learning experience. The exercises emphasize both conceptual understanding and practical application.
The course begins with the basics of NumPy, introducing learners to array creation, indexing, and vectorized operations. These are essential skills for numerical computing in Python and provide the groundwork for more complex data tasks. Through guided exercises, users develop an intuition for efficient computation using NumPy's core structures.
Following this, the material transitions into the use of Pandas, a powerful library for working with tabular data. Learners explore Series and DataFrame structures, data selection, filtering, aggregation, and basic cleaning operations. These sessions are geared toward real-world tasks such as reading data from files, exploring datasets, and preparing data for analysis.
The course concludes with more advanced data manipulation exercises that combine techniques from both libraries. These sessions encourage learners to think critically about data workflows, transformation pipelines, and how to structure code for clarity and performance. By integrating previous concepts, the advanced section reinforces cumulative learning and problem-solving.
Overall, the repository is designed to help learners move from basic scripting in Python to confidently working with structured data. It emphasizes clarity, repeatability, and hands-on engagement—skills that are critical in data analysis, scientific computing, and applied machine learning contexts