This repository documents my journey in learning the fundamentals of Data Analytics using Python, guided and enhanced by AI-assisted learning.
It serves as a hands-on notebook and code collection for exploring how raw data can be cleaned, analyzed, and visualized to uncover meaningful insights.
AI-assisted learning provides personalized, real-time guidance — helping simplify complex topics, explain reasoning, and offer tailored practice.
By combining traditional learning with AI support, I’m able to:
- Understand analytics concepts faster and more deeply.
- Get instant clarification and step-by-step guidance while coding.
- Practice real-world problem-solving with context-based examples.
- Build confidence and independence in performing data analysis tasks.
This repository focuses on building a strong base in Data Analytics through Python:
- Python basics for analytics
- Working with NumPy and pandas
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Data visualization using Matplotlib and Seaborn
- Drawing insights and summaries from real datasets
- Python 3.x
- Jupyter Notebook
- pandas
- NumPy
- Matplotlib
- Seaborn
- Visual Studio Code
This project represents my foundational learning path in Data Analytics, guided by curiosity and supported by AI-assisted exploration.
Each notebook reflects practical understanding and step-by-step improvement toward becoming confident in handling and analyzing data.
- Add more case studies and EDA projects.
- Explore real-world datasets from Kaggle.
- Learn dashboarding tools like Power BI or Tableau later on.
- Transition into intermediate Data Science and Machine Learning concepts.
Created and maintained by a passionate learner exploring data analytics through AI-assisted learning.