Preface
by Patrick R. Nicolas, Arun Manivannan, Pascal Bugnion
Scala: Guide for Data Science Professionals
- Scala: Guide for Data Science Professionals
- Table of Contents
- Scala: Guide for Data Science Professionals
- Scala: Guide for Data Science Professionals
- Credits
- Preface
- 1. Module 1
- 1. Scala and Data Science
- 2. Manipulating Data with Breeze
- Code examples
- Installing Breeze
- Getting help on Breeze
- Basic Breeze data types
- Vectors
- Dense and sparse vectors and the vector trait
- Matrices
- Building vectors and matrices
- Advanced indexing and slicing
- Mutating vectors and matrices
- Matrix multiplication, transposition, and the orientation of vectors
- Data preprocessing and feature engineering
- Breeze – function optimization
- Numerical derivatives
- Regularization
- An example – logistic regression
- Towards re-usable code
- Alternatives to Breeze
- Summary
- References
- 3. Plotting with breeze-viz
- 4. Parallel Collections and Futures
- 5. Scala and SQL through JDBC
- Interacting with JDBC
- First steps with JDBC
- JDBC summary
- Functional wrappers for JDBC
- Safer JDBC connections with the loan pattern
- Enriching JDBC statements with the "pimp my library" pattern
- Wrapping result sets in a stream
- Looser coupling with type classes
- Creating a data access layer
- Summary
- References
- 6. Slick – A Functional Interface for SQL
- 7. Web APIs
- 8. Scala and MongoDB
- 9. Concurrency with Akka
- GitHub follower graph
- Actors as people
- Hello world with Akka
- Case classes as messages
- Actor construction
- Anatomy of an actor
- Follower network crawler
- Fetcher actors
- Routing
- Message passing between actors
- Queue control and the pull pattern
- Accessing the sender of a message
- Stateful actors
- Follower network crawler
- Fault tolerance
- Custom supervisor strategies
- Life-cycle hooks
- What we have not talked about
- Summary
- References
- 10. Distributed Batch Processing with Spark
- 11. Spark SQL and DataFrames
- DataFrames – a whirlwind introduction
- Aggregation operations
- Joining DataFrames together
- Custom functions on DataFrames
- DataFrame immutability and persistence
- SQL statements on DataFrames
- Complex data types – arrays, maps, and structs
- Interacting with data sources
- Standalone programs
- Summary
- References
- 12. Distributed Machine Learning with MLlib
- 13. Web APIs with Play
- Client-server applications
- Introduction to web frameworks
- Model-View-Controller architecture
- Single page applications
- Building an application
- The Play framework
- Dynamic routing
- Actions
- Interacting with JSON
- Querying external APIs and consuming JSON
- Creating APIs with Play: a summary
- Rest APIs: best practice
- Summary
- References
- 14. Visualization with D3 and the Play Framework
- A. Pattern Matching and Extractors
- II. Module 2
- 1. Getting Started with Breeze
- Introduction
- Getting Breeze – the linear algebra library
- Working with vectors
- Getting ready
- How to do it...
- Creating vectors
- Constructing a vector from values
- Creating a vector out of a function
- Creating a vector of linearly spaced values
- Creating a vector with values in a specific range
- Creating an entire vector with a single value
- Slicing a sub-vector from a bigger vector
- Creating a Breeze Vector from a Scala Vector
- Vector arithmetic
- Scalar operations
- Calculating the dot product of two vectors
- Creating a new vector by adding two vectors together
- Appending vectors and converting a vector of one type to another
- Concatenating two vectors
- Standard deviation
- Find the largest value in a vector
- Finding the sum, square root and log of all the values in the vector
- Working with matrices
- Vectors and matrices with randomly distributed values
- How it works...
- Creating vectors with uniformly distributed random values
- Creating vectors with normally distributed random values
- Creating vectors with random values that have a Poisson distribution
- Creating a matrix with uniformly random values
- Creating a matrix with normally distributed random values
- Creating a matrix with random values that has a Poisson distribution
- How it works...
- Reading and writing CSV files
- 2. Getting Started with Apache Spark DataFrames
- Introduction
- Getting Apache Spark
- Creating a DataFrame from CSV
- Manipulating DataFrames
- Creating a DataFrame from Scala case classes
- 3. Loading and Preparing Data – DataFrame
- 4. Data Visualization
- 5. Learning from Data
- Introduction
- Supervised and unsupervised learning
- Gradient descent
- Predicting continuous values using linear regression
- Binary classification using LogisticRegression and SVM
- Binary classification using LogisticRegression with Pipeline API
- How to do it...
- Importing and splitting data as test and training sets
- Construct the participants of the Pipeline
- Preparing a pipeline and training a model
- Predicting against test data
- Evaluating a model without cross-validation
- Constructing parameters for cross-validation
- Constructing cross-validator and fit the best model
- Evaluating the model with cross-validation
- How to do it...
- Clustering using K-means
- Feature reduction using principal component analysis
- How to do it...
- Dimensionality reduction of data for supervised learning
- Mean-normalizing the training data
- Extracting the principal components
- Preparing the labeled data
- Preparing the test data
- Classify and evaluate the metrics
- Dimensionality reduction of data for unsupervised learning
- Mean-normalizing the training data
- Extracting the principal components
- Arriving at the number of components
- Evaluating the metrics
- How to do it...
- 6. Scaling Up
- 7. Going Further
- 1. Getting Started with Breeze
- III. Module 3
- 1. Getting Started
- 2. Hello World!
- 3. Data Preprocessing
- 4. Unsupervised Learning
- 5. Naïve Bayes Classifiers
- 6. Regression and Regularization
- 7. Sequential Data Models
- 8. Kernel Models and Support Vector Machines
- 9. Artificial Neural Networks
- Feed-forward neural networks (FFNN)
- The multilayer perceptron (MLP)
- The activation function
- The network architecture
- Software design
- Model definition
- Training cycle/epoch
- Training strategies and classification
- Evaluation
- Benefits and limitations
- Summary
- 10. Genetic Algorithms
- 11. Reinforcement Learning
- 12. Scalable Frameworks
- B. Basic Concepts
- C. Bibliography
- Index
Scala is a popular language for data science. By emphasizing immutability and functional constructs, Scala lends itself well to the construction of robust libraries for concurrency and big data analysis. A rich ecosystem of tools for data science has therefore developed around Scala, including libraries for accessing SQL and NoSQL databases, frameworks for building distributed applications like Apache Spark and libraries for linear algebra and numerical algorithms. We will explore this rich and growing ecosystem in this learning path.
Module 1, Scala for Data Science, will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this module will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectures to process and modeling your data, starting from simple concurrency constructs such as parallel collections and futures, through to actor systems and Apache Spark. As well as Scala's emphasis on functional structures and immutability, you will learn how to use the right parallel construct for the job at hand, minimizing development time without compromising scalability. Finally, you will learn how to build beautiful interactive visualizations using web frameworks. This module gives tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed with building data science and data engineering solutions.
Module 2, Scala Data Analysis Cookbook, will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightful visualizations, and machine learning toolkits.Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips with how to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you'll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. Discover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX.
Module 3, Scala for Machine Learning, will introduce you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits.Your learning journey starts with data pre-processing and filtering techniques, then move on to clustering and dimension reduction, Naïve Bayes, regression models, sequential data, regularization and kernelization, support vector machines, Neural networks, generic algorithms and re-enforcement learning. The review of the Akka framework and Apache Spark clusters concludes the tutorial. Techniques throughout the module is applied to the analysis, recommendation, classification, and prediction of financial markets.
This module will guide you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets and useful tips.
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