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https://github.com/ricardorobledo/data_science_for_beginners


https://github.com/ricardorobledo/data_science_for_beginners

matplotlib numpy pandas python3 scikit-learn

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README

          

# Data Science Beginner's Guide Notebook

This notebook is based on the The Beginners Guide to Data Science book from [machinelearningmastery.com](https://machinelearningmastery.com/) and covers essential data science topics with practical examples and explanations.

## Key Topics Covered

### I Wrangling with Data
- Visualizing missing data with the Ames Housing dataset
- Exploring data dictionaries and classifying variables
- Techniques for imputing missing data
- Advanced data transformation with Pandas: querying, grouping, pivoting, merging

### II From Data to Information
- Introduction to descriptive statistics with Ames dataset
- Visualizing geographic data and housing prices with Python
- Understanding feature relationships using heatmaps, scatter plots, and pair plots
- Using pair plots for hypothesis generation

### III Inferential Statistics and Hypothesis Testing
- Confidence intervals and assumptions in real estate data
- Hypothesis testing basics and applications
- Chi-squared test for categorical data insights
- ANOVA and Kruskal-Wallis tests to analyze housing market variations

### IV Outlier Detection and Data Transformation
- Classical methods for detecting outliers in datasets
- Understanding and addressing skewness in data with transformation strategies

### V Finding Value with Data
- Interactive mapping with Folium for luxury real estate analysis
- Using data science to identify market opportunities in real estate

This notebook provides practical guidance to beginners aiming to understand the full data science workflow, from data wrangling and exploration to inference and visualization.