EDA Techniques for Data Analysis
summary
Covers a practical EDA workflow that starts from raw CSV/text to cleaned features ready for models, demonstrating Python tools and exact functions: pandas DataFrame for isnull, dropna / fillna / drop_duplicates for missing and duplicate handling, outlier identification and removal, and scaling with StandardScaler / MinMaxScaler. Shows common plots (scatter, histogram, boxplot, heatmap, line, geo) and recommends iterating, documenting steps, and using scikit‑learn preprocessing and Plotly/Tableau for interactive checks; claims 0–1 normalization improves numeric stability for downstream models.
AI-written summary · about
chapters (8)
- 0:00Intro
- 2:23Data Analysis
- 6:07EDA and Data Analysis
- 9:18Data Cleaning Techniques
- 20:37Data Cleansing Techniques
- 21:51Data Visualization Techniques
- 29:29Data Libraries
- 31:28Summary
related talks
01h 53mAnalyze your data in Python
38mTransforming Data to Unlock Its Latent Value
35mExploratory Data Analysis: The Missing Step for AI
24mThe TL;DR of EDA
32mData Transformation: A Framework for Exploratory Data Analysis
01h 29mPandas and Plots for Data Analysis
04h 02mHands-on Session on Data Science for Beginners
01h 38mEffective Data Visualization