Data scientists spend 80% of their time cleaning data — not building models. In this episode we walk through the complete data preparation pipeline: missing values, duplicates, outliers, encoding, normalization, train/test splitting, and data leakage — with a practical checklist you can use on any ML project.
In this episode:
→ What messy real-world data actually looks like (live demo)
→ The three types of missingness — MCAR, MAR, MNAR — and how to handle each
→ Duplicates, noise, and outlier handling — when to remove and when to keep
→ Encoding categorical variables — ordinal, one-hot, and target encoding
→ Normalization vs. standardization — when each applies
→ Train / validation / test splits — why three sets, not two
→ Data leakage — the silent killer of ML projects
→ An 8-step data preparation checklist
This is Episode 8 of Master AI & Machine Learning — Module 2: Data Essentials.
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📋 FULL COURSE PLAYLIST
⬅ Ep 07 — Data Types Explained
➡ Ep 09 — Bias in Data
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Series of Thoughts · Presented by TechnovativeAI
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In this data science tutorial, we explore the importance of dedicating a significant portion of your time to the data science workflow, specifically data analysis, data cleaning, and data transformation. Effective data prep and data preprocessing are crucial steps in any machine learning or deep learning project, as they can make or break the accuracy of your model training. A well-structured AI pipeline or ML pipeline, including ETL pipeline, feature engineering, and data wrangling, can significantly improve the efficiency of your data science workflow. By focusing on these areas, you can master AI and ML, and develop a robust data science workflow that yields high-quality results. Learn how to optimize your data science workflow and discover why 80% of your time should be spent on these critical tasks, using tools like Python data libraries to streamline your process and improve your overall data science skills.
0:00 The 80% Reality of Data Preparation
1:32 Strategies for Handling Missing Values
2:21 Managing Duplicates and Outliers
6:15 The Practitioner's Data Checklist