Data Science in Practice
Data Science in Practice
Understanding Data
Introduction to Data Science
What is Data Science?
The Scientific Method
A Data Science Example
The Basics of Tabular Data
Tabular Data
Tables in python, using Pandas.
Table Methods
Data Types and Performance
Querying and Describing Data
Selecting Data
Kinds of Data
Categorical Distributions
Quantitative Distributions
Understanding Assumptions and Data Cleaning
Modifying DataFrames
Cleaning Messy Data
Exploratory Data Analysis
Hypothesis Testing
Aggregation and Extension of Data
Data Granularity
Understanding Aggregations
Combining Data (Observations)
Combining Data: Attributes
Combining different measurements over the same individuals
Permutation Tests
Exploratory Data Analysis II
Missing Data
Definitions
Identifying Missing Data
Handling Missing Data
Single-Valued Imputation
Probabilistic Imputation
Collecting Data
Data Collection
Using Existing Data
HTTP Requests
Parsing HTML
Information Extaction
Text Processing
Natural Language Processing
Introduction to Features
Feature Engineering
Data Pipelines
Modeling With Data
Modeling Basics
Introduction to Statistical Models
Building Modeling Pipelines
Bias and Variance
Model Quality (Inference)
Model Quality (Prediction)
Cross Validation
Parameter Search
Evaluating Models; Fairness
Evaluation metrics
Parity Measures
Fairness in Machine Learning
.ipynb
.pdf
Binder
Cross Validation
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See Lecture Notes
When is a Model a Good Model? (Prediction)
Parameter Search
