更新时间：2021-07-08 11:13:02
封面
版权页
Credits
About the Author
About the Reviewer
www.PacktPub.com
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Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Chapter 1. Extracting and Handling Data
Introduction
Why should we use Julia for data science?
Handling data with CSV files
Handling data with TSV files
Working with databases in Julia
Interacting with the Web
Chapter 2. Metaprogramming
Representation of a Julia program
Symbols and expressions
Symbols
Quoting
Interpolation
The Eval function
Macros
Metaprogramming with DataFrames
Chapter 3. Statistics with Julia
Basic statistics concepts
Descriptive statistics
Deviation metrics
Sampling
Correlation analysis
Chapter 4. Building Data Science Models
Dimensionality reduction
Linear discriminant analysis
Data preprocessing
Linear regression
Classification
Performance evaluation and model selection
Cross validation
Distances
Distributions
Time series analysis
Chapter 5. Working with Visualizations
Plotting basic arrays
Plotting dataframes
Plotting functions
Exploratory data analytics through plots
Line plots
Scatter plots
Histograms
Aesthetic customizations
Chapter 6. Parallel Computing
Basic concepts of parallel computing
Data movement
Parallel maps and loop operations
Channels
