Скачать или смотреть Understanding Data: Types, Quality, and Common Issues

Welcome to the Gudsky AI & ML Educational Series 🚀

In this theory-focused session, we dive deep into the different types of data, why data quality is crucial, and the common issues that arise when working with real-world datasets. This knowledge is essential for building reliable machine learning models.

🔍 What you’ll learn
✅ Data types: Structured, Unstructured, and Semi-structured
✅ Numerical vs Categorical data (Nominal, Ordinal, Continuous, Discrete)
✅ Special types of data: Time-series, Text, and Multimedia
✅ Data sources — sensors, APIs, surveys, logs
✅ Data quality dimensions: Accuracy, Completeness, Consistency, Timeliness
✅ Common issues in datasets:
Missing data
Outliers
Duplicates
Noisy data
Imbalanced data
Biased data
✅ Case Study — Titanic dataset problems and challenges
✅ Best practices for ensuring data quality before applying ML

📘 Next up
Next video: Data Preprocessing Pipeline: Cleaning, Transformation, and Feature Engineering
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