• “Knowing is not enough; we must apply. Willing is not enough; we must do.” ~ Johann Wolfgang von Goethe

This repository serves as a comprehensive portfolio for my coursework on mathematical foundations, which is an integral part of the Data Science curriculum at Masterschool.

This project's primary objective is to deepen my understanding of core mathematical principles essential for data science. It focuses on conceptual and practical applications, bridging theoretical knowledge with hands-on implementation. Key areas of focus include:

• Descriptive Statistics: Summarizing and visualizing data sets to understand their key characteristics.
• Probability Theory: Exploring fundamental concepts of probability and their role in predictive modeling.
• Statistical Distributions: Implementing and analyzing various probability distributions (e.g., Normal, Binomial, Poisson) to model real-world phenomena.
• Inferential Statistics: Making inferences and drawing conclusions about a population based on sample data.

Through this project, I aim to achieve proficiency in:

• Using Python libraries like NumPy and SciPy for statistical computations.
• Applying Pandas for efficient data manipulation and cleaning.
• Creating informative visualizations with Matplotlib and Seaborn to communicate statistical findings.
• Implementing conditional logic and functions to automate analytical tasks.

• PMF/CDF ve Momentler Uygulaması (Google Colab Notebook)

• notebooks/: Jupyter notebooks containing code, explanations, and analysis for each topic.
• data/: Datasets used throughout the project.
• resources/: Relevant external resources and lecture notes.

This repository will be continuously updated as I progress through the curriculum.

This repository or document contains notes and explanations covering fundamental concepts of Probability and Statistics, specifically tailored for their application in Machine Learning and Data Science fields.