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Introductory Data Science
Introduction
1. Data Curation
1.1. Types and Sources of Data
1.2. Collecting Data
1.3. Organizing and Standardizing Data
1.3.1. Introduction to Python
1.3.2. Organizing in Tables
1.3.3. Scaling and Standardizing data
1.4. Cleaning data
1.4.1. More Python
1.4.2. Introduction to Pandas
1.4.3. Data Wrangling
2. Enhanced data visualization
2.1. Traditional Plots
2.2. Single vs. Two or More Variables
2.3. Dynamic Visualization
2.4. Distribution Maps
2.5. Time Series Plots
2.5.1. Forecasting using Time Series
2.6. Misleading Graphs
3. Statistical models, estimation, and prediction
3.1. Statistical Model
3.2. Estimator and Sampling Distribution
3.3. Simulation-Based Estimation and Prediction
3.4. * Loss Function and Decision
3.5. * Model Diagnostics
3.6. * Estimation (mathematical approaches)
4. Machine learning and statistical learning
4.1. Machine Learning
4.2. Supervised Learning
4.3. Unsupervised Learning
4.4. * Sentiment Analysis
5. Data ethics
5.1. Laws and Regulations
5.2. Unfair Discrimination and Social Bias
5.3. Transparency of Methods and Analysis
5.4. Sampling Bias
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Index