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Data Analysis With Python My Mooc

Kenji Sato
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data analysis with python my mooc

Welcome to my learning repository for the Data Analysis with Python 2024–2025 course offered by the University of Helsinki and MOOC.fi. This course is part of my personal and academic journey to build strong foundations in data analysis and statistics using Python. I’m completing it alongside my CS degree and using it as a stepping stone toward applying data analysis in real-world projects — including things like analyzing fantasy football data and preparing for future work in AI and machine learning.

"Manipulate, clean, visualize, and analyze real data using Python and its data science ecosystem." — MOOC.fi This course introduces different phases of the data analysis pipeline using Python and libraries such as NumPy, pandas, Matplotlib, and SciPy.

It walks through: - Downloading and cleaning data into consistent formats - Combining, grouping, and summarizing data - Performing statistical operations - Visualizing data for insight - Introducing machine learning concepts for modeling and prediction Course Level: Ideal for students finishing a Bachelor's degree or starting a Master's Prerequisites: - Strong programming fundamentals in any language - Basics of linear algebra and probability Each chapter builds on both technical and conceptual knowledge: - ✅ Python fundamentals and data structures - ✅ NumPy for numerical data processing - 🔄 pandas for handling datasets and tables - 🔄 Data wrangling and cleaning - 🔄 Summary statistics and feature engineering - 🔄 Introductory machine learning concepts - 🔄 Final project-based application of skills - Python 3.13 - NumPy - pandas - matplotlib - SciPy - Jupyter / VS Code - Git & GitHub This repository is part of my ongoing self-learning and GitHub activity.

While the files are provided by the course, I’ll be adding to them as I complete exercises and interact with the material. 📈 My personal motivation includes building enough skill to analyze sports data — especially player stats and performance in fantasy football — while building a base for future work in AI/ML. - GitHub: @ilmfeemster - LinkedIn: https://www.linkedin.com/in/immanuel-m-a245b1106/ - Course Link: Data Analysis with Python – 2024–2025 This repository includes course-provided content and my personal work. Course materials © University of Helsinki. Shared here for educational, non-commercial purposes.

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While the files are provided by the course, I’ll be adding to them as I complete exercises and interact with the material. 📈 My personal motivation includes building enough skill to analyze sports data — especially player stats and performance in fantasy football — while building a base for future work in AI/ML. - GitHub: @ilmfeemster - LinkedIn: https://www.linkedin.com/in/immanuel-m-a245b1106/ ...