DSC_COURSE/

├── Phase_1/ ├── Phase_2/ └── Phase_3/ └── Phase_4/

🧠 DSC_COURSE Repository

Welcome to the DSC_COURSE repository! This project contains my structured learning journey through a Data Science curriculum, including hands-on labs, code challenges, and projects.

---

📁 Repository Structure

The repository is organized by phases, each corresponding to a core focus area in the Data Science workflow:

📦 Phase_1 – Data Collection & Cleaning

Focuses on collecting raw data, cleaning it, and preparing it for analysis.

• Loading CSVs and APIs
• Handling missing values
• Data type conversions
• Pandas fundamentals

📦 Phase_2 – Exploratory Data Analysis (EDA)

Emphasizes visual and statistical techniques to understand data patterns.

• Univariate & bivariate analysis
• Data visualization with Matplotlib & Seaborn
• Correlation and distribution analysis

📦 Phase_3 – Statistics & Inference

Covers hypothesis testing and statistical thinking for making data-driven decisions.

• Probability distributions
• Confidence intervals
• T-tests and ANOVA
• Code challenge on sample network data

📦 Phase_4 – Machine Learning Fundamentals

Introduces supervised learning and model evaluation techniques. Includes the following labs:

• dsc-distance-metrics-lab
• dsc-k-nearest-neighbors-lab
• dsc-knn-with-scikit-learn-lab
• dsc-nonparametric-models-lab
• dsc-gridsearchcv
• dsc-pipelines-lab-v2-1
• dsc-pipelines-v2-1
• dsc-refactoring-with-pipelines
• dsc-tuning-pipelines-intro

---

💻 Technologies Used

• Python
• Jupyter Notebooks
• Scikit-learn
• Pandas, NumPy
• Seaborn & Matplotlib
• Git & GitHub

---

📌 How to Navigate

• Each phase has a clear folder with labs and exercises.
• Notebooks contain markdown cells with instructions and code cells with implementations.
• Run the notebooks locally in a conda environment or using Jupyter Lab.

---

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/FGithaiga/DSC_COURSE.git
   cd DSC_COURSE