Data Science in Practice
Data Science in Practice
Section 1: Understanding Data
1. Introduction to Data Science
1.1. What is Data Science?
1.2. The Scientific Method
1.3. A Data Science Example
2. Tabular Data
2.1. Introduction to Tabular Data
2.2. DataFrame Methods
2.3. Data Types and Performance
3. Querying and Describing Data
3.1. Selecting Data
3.2. Kinds of Data
3.3. Categorical Distributions
3.4. Quantitative Distributions
4. Understanding Assumptions and Data Cleaning
4.1. Modifying DataFrames
4.2. Cleaning Messy Data
4.3. Exploratory Data Analysis
4.4. Hypothesis Testing
5. Aggregation and Extension of Data
5.1. Data Granularity
5.2. Understanding Aggregations
5.3. Combining Data (Observations)
5.4. Combining Data (Attributes)
5.5. Permutation Tests
5.6. Exploratory Data Analysis II
6. Missing Data
6.1. Definitions
6.2. Identifying Missing Data
6.3. Handling Missing Data
6.4. Single-Valued Imputation
6.5. Probabilistic Imputation
Section 2: Collecting Data
7. Data Collection
7.1. Using Existing Data
7.2. HTTP Requests
7.3. Parsing HTML
8. Information Extaction
8.1. Text Processing
8.2. Natural Language Processing
9. Introduction to Features
9.1. Feature Engineering
9.2. Data Pipelines
Section 3: Modeling With Data
10. Modeling Basics
10.1. Introduction to Statistical Models
10.2. Building Modeling Pipelines
11. Bias and Variance
11.1. Model Quality (Inference)
11.2. Model Quality (Prediction)
11.3. Cross Validation
11.4. Parameter Search
12. Evaluating Models; Fairness
12.1. Evaluation metrics
12.2. Parity Measures
12.3. Fairness in Machine Learning
Index
